Generated by All in One SEO Pro v4.9.10, this is an llms-full.txt file, used by LLMs to index the site. # Taylor Geospatial: For the Digital Public Good We aim to democratize the power of GeoAI through global partnerships while strengthening innovation capacity in the St. Louis region. ## Posts ### [News](https://taylorgeospatial.org/news/) **Published:** June 9, 2026 **Author:** taylorgiwp **Content:** ## In the News [![A person wearing a scarf and jacket bends over a cart filled with apples at an outdoor market, with other people and produce visible in the background under a clear blue sky.](https://taylorgeospatial.org/wp-content/uploads/2026/05/kabul-market-960x640.jpg)](https://www.devex.com/news/how-geospatial-ai-is-helping-aid-groups-predict-famine-and-deliver-food-112935)July 15, 2026 ### [How geospatial AI is helping aid groups predict famine and deliver food](https://www.devex.com/news/how-geospatial-ai-is-helping-aid-groups-predict-famine-and-deliver-food-112935) Devex — A St. Louis nonprofit is investing in AI to help WFP, FEWS NET, and other groups build and improve open-source tools for humanitarians to deliver food aid much more quickly and efficiently to people in need. [![Hannah Kerner shakes hands with attendees at the APEC ASPIRE ceremony.](https://taylorgeospatial.org/wp-content/uploads/2026/06/Hannah-Kerner-ASPIRE_01_v3-960x640.jpg)](https://taylorgeospatial.org/news/hannah-kerner-us-nominee-aspire/)June 25, 2026 ### [Hannah Kerner Named U.S. Nominee for the APEC ASPIRE Prize](https://taylorgeospatial.org/news/hannah-kerner-us-nominee-aspire/) Taylor Geospatial research advisor Hannah Kerner recognized for advancing scientific research in AI and data science to promote industrial innovation and economic resilience. [](https://scai.engineering.asu.edu/news/ai-project-aims-to-help-humanitarian-organizations-identify-food-security-risks-in-conflict-zones/)June 23, 2026 ### [AI project aims to help humanitarian organizations identify food security risks in conflict zones](https://scai.engineering.asu.edu/news/ai-project-aims-to-help-humanitarian-organizations-identify-food-security-risks-in-conflict-zones/) A new initiative at Arizona State University seeks to provide earlier, more accessible insights into emerging food security risks by combining artificial intelligence, or AI, satellite observations and natural-language tools. [](https://taylorgeospatial.org/news/satsummit-comes-to-st-louis/)June 18, 2026 ### [SatSummit Comes to St. Louis](https://taylorgeospatial.org/news/satsummit-comes-to-st-louis/) Taylor Geospatial to co-host conference for leaders in the satellite industry and experts in global development November 18-19, 2026, at The Post Building. [](https://taylorgeospatial.org/news/mapping-the-world-at-taylor-geospatial/)June 11, 2026 ### [Mapping The World at Taylor Geospatial](https://taylorgeospatial.org/news/mapping-the-world-at-taylor-geospatial/) Robin Cole from the Satellite Image Deep Learning podcast interviews Jennifer Marcus, VP for Strategic Innovation Programs and Isaac Corley, Director of AI/ML Research, about Fields of The World. [](https://taylorgeospatial.org/news/fields-of-the-world-matt-forrest/)June 10, 2026 ### [Mapping Every Field on Earth](https://taylorgeospatial.org/news/fields-of-the-world-matt-forrest/) Spatial Stack’s Matt Forrest interviews Jennifer Marcus, VP for Strategic Innovation Programs and Isaac Corley, Director of AI/ML Research, about Fields of The World. [](https://taylorgeospatial.org/news/fields-of-the-world-great-data-products/)May 19, 2026 ### [Fields of the World on Great Data Products](https://taylorgeospatial.org/news/fields-of-the-world-great-data-products/) Jed Sundwall, executive director of Radiant Earth, creator of Source Cooperative, interviews Jen Marcus and Isaac Corley about Fields of The World. [](https://engineering.washu.edu/news/2026/Boundaries-of-agricultural-fields-worldwide-now-publicly-available.html)May 5, 2026 ### [Boundaries of agricultural fields worldwide now publicly available](https://engineering.washu.edu/news/2026/Boundaries-of-agricultural-fields-worldwide-now-publicly-available.html) McKelvey School of Engineering at Washington University in St. Louis; May 5, 2026 [](https://spacenews.com/taylor-geospatial-unveils-global-field-dataset/)May 4, 2026 ### [Taylor Geospatial unveils global field dataset](https://spacenews.com/taylor-geospatial-unveils-global-field-dataset/) SpaceNews — Providing a template for applying AI to satellite imagery to create other global datasets [](https://www.stlmag.com/business/st-louis-geospatial-ecosystem-evolution/)March 23, 2026 ### [Two new organizations aim to drive St. Louis’ geospatial ambitions](https://www.stlmag.com/business/st-louis-geospatial-ecosystem-evolution/) St. Louis Magazine; March 23, 2026 --- ### [How geospatial AI is helping aid groups predict famine and deliver food](https://taylorgeospatial.org/news/how-geospatial-ai-is-helping-aid-groups-predict-famine-and-deliver-food/) **Published:** July 15, 2026 **Author:** Allison Braun **Categories:** GIFS, In the News --- ### [Hannah Kerner Named U.S. Nominee for the APEC ASPIRE Prize](https://taylorgeospatial.org/news/hannah-kerner-us-nominee-aspire/) **Published:** June 25, 2026 **Author:** Allison Braun **Excerpt:** Taylor Geospatial research advisor Hannah Kerner recognized for advancing scientific research in AI and data science to promote industrial innovation and economic resilience. **Content:** Earlier this week at the U.S. Department of State, Dr. Hannah Kerner—Taylor Geospatial Research Advisor and the scientific lead behind Fields of The World—was recognized as the U.S. Nominee for the 2026 Asia-Pacific Economic Cooperation (APEC) Science Prize for Innovation, Research and Education (ASPIRE). We are incredibly proud of Hannah and pleased that she is receiving this well-deserved recognition. To learn more about APEC and ASPIRE competition, read the [release](https://www.state.gov/2026-u-s-aspire-competition). ### **Breakthrough Research Meets Real-World Impact** Dr. Kerner’s nomination cited her research developing and deploying machine learning solutions to advance agricultural insights and strengthen community resilience to natural disasters and food insecurity. Hannah was instrumental in launching and developing Taylor Geospatial’s effort, [**Fields of The World (FTW)**](https://taylorgeospatial.org/news/agricultural-field-boundaries-mapped-globally-for-the-first-time/), a first-of-its-kind, openly available machine learning ecosystem for mapping agricultural field boundaries, which are a foundational input for food security, agricultural monitoring, and supply chain planning worldwide. ![A grid of satellite images shows agricultural fields from various countries, each with unique patterns. A logo reads Fields of The World: Open-source AI and data ecosystem for global agricultural field boundaries.](https://taylorgeospatial.org/wp-content/uploads/2026/06/FTW-apec-member-economies-1920x1075.jpg)Agricultural field boundaries of APEC’s 21 member economies, as captured through the Fields of the World dataset. Shown during Dr. Kerner’s presentation during the 2026 U.S. ASPIRE award ceremony.She led the development of the FTW model and, in collaboration with Taylor Geospatial’s cadre of Technical Fellows, the ecosystem necessary to tackle such a massive, collaborative undertaking. Her leadership on FTW aided our efforts to bridge the gap between breakthrough academic research and real-world deployment, accelerating the transition from idea to impact. Dr. Kerner is also part of the Arizona State University–led team behind one of the winning projects in our [Geospatial Innovation for Food Security (GIFS)](https://taylorgeospatial.org/initiatives/gifs/) program —a [food system instability prediction tool](https://taylorgeospatial.org/initiatives/gifs/predicting-food-system-instability/) developed with the University of Maryland, Washington University in St. Louis, NASA Harvest, NASA Goddard Flight Center and the Famine Early Warning System Network (FEWS NET). The innovation lets analysts pose plain-language questions and receive satellite-backed answers with confidence estimates. Taken together, Hannah’s work with FTW and the GIFS project reflect an ongoing collaboration aimed squarely at the challenges ASPIRE was created to address. ![Three award recipients posing with trophies beside a tall ASPIRE Awards banner at a ceremony in Washington, DC.](https://taylorgeospatial.org/wp-content/uploads/2026/06/monica-hannah-rohith-1920x1280.jpg)APEC President Monica Hardy Whaley, Hannah Kerner, and 2026 U.S. ASPIRE runner-up Rohith Krishna.We are proud to have a member of our team nominated as the U.S. representative for an award that champions international cooperation, economic stability, and scientific innovation. Dr. Kerner is a principal at Taylor Geospatial because her efforts align squarely with our mission to democratize the power of GeoAI through global partnerships, to unlock AI-driven geospatial breakthroughs that address critical global challenges, and to put those breakthroughs in the hands of the people who need them. ##### Congratulations, Hannah! **Categories:** In the News --- ### [Co-investment with AWS expands GIFS program](https://taylorgeospatial.org/news/aws-gifs-pilot-projects/) **Published:** June 25, 2026 **Author:** Allison Braun **Excerpt:** Geospatial Innovation for Food Security (GIFS) program adds 10 pilot projects **Content:** We’re excited to announce the addition of 10 pilot projects contributing to our Geospatial Innovation for Food Security (GIFS) program thanks to co-investment from [Amazon Web Services (AWS)](https://www.linkedin.com/company/amazon-web-services/). We are grateful for their support of our shared goal: to address critical global challenges through the power of AI. Over a 12-to-18 month timeline, the pilot projects’ teams will develop foundational methods and components to translate their research into practical applications. Their goal: To transform complex geospatial data into actionable insights for farmers, policymakers, and industry stakeholders across various regions, including Africa, Southeast Asia, and the United States. They will leverage GeoAI, Earth observations, and advanced modeling to enhance global food security and agricultural sustainability across three critical areas: 🚚 Strengthening supply chain resilience 🌽 Optimizing climate-adaptive crop shifting 🌱 Improving nitrogen use efficiency for sustainable productivity Project teams span academia, nonprofits and industry with representation from the following: - Auburn University - Better Planet Laboratory - EarthDaily - Earth Genome - Global Nitrogen Innovation Center for Clean Energy and the Environment (NICCEE) - IGAD Climate Prediction & Applications Centre (ICPAC) - IntelinAir - International Center for Tropical Agriculture (CIAT) - NASA Harvest - Operation Food Search - Spatial Informatics Group, LLC - University of Alabama - University of Ghana Centre for Remote Sensing and Geographic Information Service (CERSGIS) - University of Maryland; UM Center for Environmental Science - University of Missouri - University of Missouri-Saint Louis - University of South Alabama - University of Texas at Austin - University of Wyoming - UrbanKisaan - Virginia Tech [Explore pilot projects](https://taylorgeospatial.org/initiatives/gifs/#pilot) **Categories:** From Taylor Geospatial, GIFS --- ### [Transforming Global Food Systems: New Geospatial Innovations Aim to Combat Hunger and Support Agricultural Stability](https://taylorgeospatial.org/news/transforming-global-food-systems-new-geospatial-innovations-aim-to-combat-hunger-and-support-agricultural-stability/) **Published:** May 27, 2026 **Author:** taylorgiwp **Content:** A new suite of geospatial innovations designed to turn data into actionable intelligence for global food systems was announced today. The Geospatial Innovation for Food Security (GIFS) Challenge has selected three project awardees to develop tools that will aid humanitarian agencies, governments, and agricultural specialists to navigate the complexities of agricultural production, climate variability, and supply chain disruptions. Launched by Taylor Geospatial, a nonprofit organization focused on advancing geospatial artificial intelligence (GeoAI) for global public benefit, the GIFS Challenge addresses a critical gap in technologies to address food insecurity. While geospatial research is abundant, it often does not align with the problems those on the front lines actually face and stops short of providing usable tools. “These projects prioritize execution over theory, ensuring that the work functions under the real-world constraints of time, scale, and uncertainty,” said Rachel Opitz, GIFS program manager at Taylor Geospatial. “The GIFS awardees are not just producing research; they are building tools that can be used to manage resources more efficiently and that humanitarian teams in conflict zones can use to identify food system risks before they become crises.” The selected projects, chosen through a competitive process with external expert review, include: #### Early Warning Systems for Hunger & Malnutrition The [United Nations World Food Programme](https://www.wfp.org/), in partnership with the [REACH Initiative](https://www.impact-initiatives.org/what-we-do/reach/), is developing Afghanistan’s PULSE platform (Platform for Understanding Local Shocks and Emergencies). The system tracks hazards affecting food access and supply routes, helping responders plan in environments where ground-level data is often incomplete. This includes combining climate, food security, nutrition and market data to give a complete picture of the on-ground realities. ![A person wearing a scarf and jacket bends over a cart filled with apples at an outdoor market, with other people and produce visible in the background under a clear blue sky.](https://taylorgeospatial.org/wp-content/uploads/2026/05/kabul-market-350x350.jpg) “AF-PULSE reduces the risk of hunger and malnutrition escalating into famine-like conditions during conflict and disasters by helping prioritize limited resources and reaching communities sooner – ultimately saving lives,” said Raul Cumba, Head of Research, Assessment and Monitoring for WFP Afghanistan. “It is a gamechanger for disaster risk reduction and preparedness, providing crucial insights tailored to local contexts.” By integrating information and community feedback with GeoAI models trained by rapid assessment feeds, the system can forecast supply chain disruptions and identify alternate transport routes to ensure timely humanitarian response. The researchers’ work focuses on Afghanistan; however, their methodology and outcomes can serve as a template for other countries facing conflict. #### Predicting Food System Instability A collaboration between [Arizona State University](https://www.asu.edu/), the [University of Maryland](https://umd.edu/), and [Washington University in St. Louis](https://washu.edu)—alongside partners [NASA Harvest](https://www.nasaharvest.org/), [NASA’s Goddard Space Flight Center](https://www.nasa.gov/goddard/), and the [Famine Early Warning Systems Network (FEWS NET)](https://fews.net/)—is developing a GeoAI capability to identify early signals of instability in food systems. ![Woman kneeling by a cultivated bed, planting green onion seedlings in dark soil next to a stone wall.](https://taylorgeospatial.org/wp-content/uploads/2026/05/pexels-muhammeddiler-36407729-350x350.jpg) “Decision-makers often have to assess food security risks with limited and delayed information about what is happening on the ground,” said Inbal Becker-Reshef, Director of NASA Harvest. “By generating more timely and transparent information, it will help address critical gaps and support organizations working to anticipate emerging food security risks.” The system aligns in-season satellite-based embeddings with natural-language queries, enabling users to generate accessible, question-driven insights while explicitly communicating uncertainty. For example, a FEWS NET analyst could ask, “Which fields have been prepared?” The tool would generate a map showing likely prepared fields and provide an estimate, such as “about 80% of fields appear prepared,” along with an indication of how confident the system is in that estimate. The framework is designed to lower technical barriers and accelerate innovation across the GeoAI and food security communities. The project team plans to test the open-source tool in active conflict regions, including Sudan, Ukraine, Syria, and Haiti; however, like AF-PULSE, their findings will be applicable to any active conflict region. #### Precision Agriculture Led by researchers at the University of Missouri in partnership with the MU Extension, this project focuses on “water first” GeoAI model development to improve nitrogen application decisions. By combining satellite imagery and machine learning, the team maps plant-available soil water at sub-field scales. This allows agronomists, farmers, and developers of variable rate application plans to make more accurate nutrient application decisions based on water availability. “There are no reliable tools that dynamically update estimates of crop nitrogen needed throughout the growing season based on local conditions,” said project lead Tim Haithcoat, Associate Professor in Data Science and Analytics, MU Institute for Data Science and Informatics. “Advancing a system that does that is a win for everybody – better harvests, fewer inputs, healthier ecosystems.” ![Close-up of young green corn plants growing in rows on dark soil, with water droplets on the leaves and a blurred field in the background.](https://taylorgeospatial.org/wp-content/uploads/2026/05/young-corn-in-soil-350x350.jpg) The initial open-source model is being developed for rainfed arable farms in the US Midwest, focused on claypan soil regions in Missouri and Iowa, and is designed to be adaptable to regions with similar agricultural systems and soils globally. “What excites us most is that this model doesn’t need to stay proprietary or locked to one region,” said Jasmine Neupane, Assistant Professor of Agricultural Systems Technology at the Digital Agriculture Research and Extension Center. “By designing it to travel—to adapt to different soils and seasons—we’re building something the global agricultural community can eventually use for precision agricultural management.” The GIFS Challenge awardees represent Taylor Geospatial’s commitment to collaborative innovation, pairing world-class researchers with the practitioners responsible for global food security. All initiatives will now move from the proof-of-concept phase toward full operational deployment during an 18-month period. Each team will receive up to $550K in funding as well as expert guidance and support. For more information, visit the [GIFS page](https://taylorgeospatial.org/initiatives/gifs/ "GIFS") on the Taylor Geospatial website. --- ##### About Taylor Geospatial Headquartered in St. Louis, Taylor Geospatial is a nonprofit organization focused on advancing geospatial artificial intelligence (GeoAI) for global public benefit. The organization partners with academic researchers and industry leaders to translate breakthrough geospatial research into accessible tools, datasets, and shared infrastructure. Through this work, Taylor Geospatial supports the development and deployment of digital public goods that help innovators and communities address complex global challenges. For more information, visit taylorgeospatial.org. ##### For Media For more information, refer to the [press kit](https://drive.google.com/drive/folders/1KryjCwNSzX59KnG_7BBCus7VgF_fCWE4?usp=sharing). If you would like to schedule an interview or have further questions, contact Allison Hawk at 314-458-7668 or . **Categories:** From Taylor Geospatial, GIFS --- ### [AI project aims to help humanitarian organizations identify food security risks in conflict zones](https://taylorgeospatial.org/news/ai-project-aims-to-help-humanitarian-organizations-identify-food-security-risks-in-conflict-zones/) **Published:** June 23, 2026 **Author:** Allison Braun **Categories:** GIFS, In the News --- ### [Agricultural Field Boundaries, Mapped Globally for the First Time](https://taylorgeospatial.org/news/agricultural-field-boundaries-mapped-globally-for-the-first-time/) **Published:** April 23, 2026 **Author:** taylorgiwp **Excerpt:** For the first time, every agricultural field on Earth has a boundary on the map. Taylor Geospatial funded and co-developed this work with Microsoft AI for Good Lab — one of the most ambitious GeoAI efforts we know of — because we believe GeoAI should work everywhere, not just in the data-rich regions where labeled training data is abundant. Today, it’s publicly available for everyone to benefit from. **Content:** ##### **Why field boundaries matter** Knowing where individual agricultural fields are — their size, shape, and location — is foundational to a wide range of applications: food security monitoring, carbon accounting, precision agriculture, and climate adaptation planning. Until now, no one had mapped them globally. The data simply didn’t exist. ##### **One audacious goal. The collaboration to make it real.** Taylor Geospatial assembled and led a collaboration of world-class researchers and industry technologists, keeping the team relentlessly focused on a goal that’s easy to lose sight of in academic research: producing outputs that are actually usable and accessible to a broad swath of industry, not just publishable. ![](https://taylorgeospatial.org/wp-content/uploads/2026/04/FTW-explorer-1920x905.png) Fields of the World global datasetOne of the ways we did that was by building something we’re proud of in its own right: a cadre of Technical Fellows — some of the best geospatial industry technologists working today — embedded alongside the academic research teams. That pairing of scientific rigor with applied industry expertise is what turned a hard research problem into a deployable, global-scale result. Microsoft AI for Good Lab was a co-investor in this vision, contributing both the deep technical expertise of researcher **Caleb Robinson** and significant cloud compute infrastructure to make global-scale inference possible. ##### **What the team built** Together, the team developed a novel model and model architecture specifically designed to infer field boundaries at global scale. Critically, they also produced the training dataset needed to get there — itself a significant scientific contribution. This was not a small lift. Global inference requires solving hard problems around data diversity, compute scale, and model generalization across vastly different agricultural landscapes worldwide. Smallholder plots in Ethiopia look nothing like Iowa corn fields or Brazilian soy. The model had to learn them all. ##### **The collaboration that made it possible** We’re grateful to the exceptional partners who made this work real: - **[Hannah Kerner’s lab](https://hannah-rae.github.io/research-group/)** at **Arizona State University** and **[Nathan Jacobs’ lab](https://mvrl.cse.wustl.edu/)** at **Washington University in St. Louis**, whose research teams contributed core scientific innovation to the model and architecture. - **[Microsoft AI for Good Lab](https://www.microsoft.com/en-us/research/group/ai-for-good-research-lab/)** — co-investor in this effort — whose researcher **[Caleb Robinson](https://www.microsoft.com/en-us/research/people/davrob/)** contributed deep technical expertise alongside significant cloud compute support. - **[Lyndon Estes](https://www.clarku.edu/faculty/profiles/lyndon-estes/)** at **Clark University**, who provided additional research contributions to the effort - [**Source Cooperative**](https://source.coop/), partner providing an open repository for hosting cloud-native geospatial datasets directly enables our mission to democratize access to GeoAI and deliver digital public goods at scale. - [**Wherobots**](https://wherobots.com/), who built the RasterFlow platform, generated the global mosaics, and ran the model efficiently at planetary scale to produce the final data products. - **Taylor Geospatial Technical Fellows**, an elite group of geospatial industry technologists who worked shoulder-to-shoulder with the academic teams to ensure the science translated into something the industry can actually use ##### **Bringing this work to users** Research only matters if it reaches the people who need it. That’s why we’re partnering with [NASA Harvest](https://www.nasaharvest.org/), the [Food and Agriculture Organization of the United Nations (FAO)](https://www.fao.org/home/en), and other global and regional partners to put this dataset directly into the hands of food security analysts, climate researchers, and agricultural development organizations worldwide. ##### **The science behind it** Along with the global data release, the team authored a [paper on global field boundaries at 10m resolution](https://aka.ms/ftw-global-paper) describing how confidence in the model was evaluated, along with confidence data layers. This work is grounded in peer-reviewed research. The PRUE dataset and methodology are described in a [paper accepted by CVPR this year](https://arxiv.org/abs/2603.27101). --- ## See it for yourself We’ve built a visualization tool so you can explore the global field boundary dataset. If you’re a researcher, practitioner, policymaker, or working in agriculture, food systems, or geospatial technology, we’d love your feedback. [Explore the Dataset](https://fieldsofthe.world/ftw-inference-app/) --- This is just the beginning. Global field boundaries are a foundational layer for the next generation of GeoAI. We’re excited to build what comes next! If you want to know more about Fields of The World, visit [fieldsofthe.world](http://fieldsofthe.world) or email us at . **Categories:** Fields of the World, From Taylor Geospatial --- ### [Convening a National Roadmap on Agricultural Nitrous Oxide MMRV Research](https://taylorgeospatial.org/news/convening-a-national-roadmap-on-agricultural-nitrous-oxide-mmrv-research/) **Published:** June 8, 2026 **Author:** Rachel Opitz **Excerpt:** Taylor Geospatial was honored to help guide this effort alongside colleagues at the Natural Resources Defense Council, bringing together researchers from universities, federal agencies, industry, and the nonprofit sector to reach consensus on the research needed to develop fit-for-purpose tools and methods to measure, model, and ultimately reduce the nitrous oxide emissions that come from the ways we grow our food. **Content:** Today we are happy to share the launch of the *[Scientific Roadmap for Advancing Agricultural Nitrous Oxide MMRV and Modeling](https://osf.io/rt63b/overview)*, the product of a yearlong collaboration among more than two dozen U.S. and Canada-based scientific experts. Taylor Geospatial was honored to help guide this effort alongside colleagues at the [Natural Resources Defense Council](https://www.nrdc.org/), bringing together researchers from universities, federal agencies, industry, and the nonprofit sector to reach consensus on the research needed to develop fit-for-purpose tools and methods to measure, model, and ultimately reduce the nitrous oxide emissions that come from the ways we grow our food. Agriculture and food security are a priority application area for Taylor Geospatial, and convening this roadmap is part of a longer-term commitment to putting the power of geospatial science into the hands of the communities and practitioners who depend on it. The roadmap plainly makes the case for why this work is necessary. Nitrous oxide (N₂O) is a greenhouse gas with roughly 273 times the warming power of carbon dioxide over a century. It is the dominant driver of stratospheric ozone depletion. Agriculture is responsible for about 80 percent of anthropogenic N₂O emissions, and those emissions also impose large, quantifiable public costs across drinking-water contamination, ecosystem damage, and elevated cancer risk. Taken together, these impacts are estimated to cost Americans as much as $210 billion a year. Yet our ability to measure agricultural N₂O at the specificity needed to guide farm practices, inform policy, verify markets, and back corporate climate claims is strikingly limited. Only seventeen long-term N₂O measurement sites operate across the entire United States, and some national inventories still rely on DayCent calibrations from just seventy-five observation points. A shared, prioritized agenda is essential to rapidly growing our collective capacity to assess N₂O emissions and enable their mitigation. ### **The role of geospatial innovation in this work** ![A diagram showing five measurement platforms: Satellite (global coverage), Tall Towers (regional, long-term), Airborne (flexible, high resolution), Tower (spatiotemporal integration), and Chamber (environmental connections).](https://taylorgeospatial.org/wp-content/uploads/2026/06/figure1-ag-n20-roadmap.png)Different instrumentation operates at distinct measurement scales. Each system has advantages and limitations, collectively providing insight into agricultural N2O emissions and their drivers. Source: Scientific Roadmap for Advancing Agricultural Nitrous Oxide MMRV and Modeling, Figure 1, page 25. Nearly every problem identified in this roadmap is, at its core, a geospatial problem. N₂O emissions are famously heterogeneous in space and time. Roughly one percent of the “hottest moments” in a year contribute nearly half of total annual N₂O losses, and poorly drained soils can emit at twice the rate of their better-drained neighbors. Quantifying that variability means integrating observations that live at fundamentally different scales: from chamber measurements across a few square meters to eddy-covariance towers across hectares to aircraft campaigns across tens to hundreds of kilometers. Ideally satellites with global reach will become part of the measurement cadre, too. Connecting those scales, fusing them with land-cover and land-management data layers, and delivering the results into models and decisions is exactly the kind of work geospatial science exists to do. Taylor Geospatial’s role as a convener is a reflection of where we believe progress happens: at the intersection of foundational geospatial innovation and advances in the disciplines that put it to use. We are committed to co-developing tools and methods with the practitioners and end users who will ultimately put them to work: farmers, agronomists, sustainability teams, atmospheric scientists, data stewards, and policymakers. Agricultural N₂O MMRV sits squarely at that intersection, and the roadmap is a blueprint for the kind of application-grounded, community-shaped research Taylor Geospatial exists to accelerate. ### **GeoAI and the future of N₂O MMRV and modeling** Developing and applying emergent GeoAI technologies is core to Taylor Geospatial’s mission to accelerate the integration of geospatial innovations into applications like agricultural management and emissions monitoring. The development and adoption of GeoAI methods are necessary to make progress on several of the research priorities identified in the roadmap. For example, the roadmap’s authors highlighted combining process-based models with machine learning as key to improving model quality, pointing to knowledge-guided architectures such as KGML-ag as particularly promising. Process-based models like DNDC and DayCent encode decades of mechanistic understanding of how soils, microbes, weather, and management practices generate N₂O. Machine-learning models, by contrast, are powerful at finding nonlinear patterns across multi-scale observational datasets but can be opaque and data-hungry. Hybrid approaches–physically informed, interpretable, and less dependent on massive training sets–are a promising route. In addition to improved models, Taylor Geospatial’s focus on GeoAI strongly aligns with the following recommendations from the roadmap: - Satellite retrieval algorithms that account for atmospheric variability, - AI tools that operate on structured semantic relationships, - Proxy-based inference from co-occurring species like NO₂, and - On-sensor firmware that delivers real-time, machine-learning-enabled decision support. A credible national N₂O MMRV system would be, in effect, a large-scale demonstration of what GeoAI can do when it is co-developed with the people who plan to use it. ### **Open science and cross-scale coordination** If there is a single thread that runs through the roadmap, it is the conviction that open science is required to make progress on agricultural N₂O. Emissions vary across plots, fields, landscapes, watersheds, and regions, and no individual lab, agency, or company can cover that range alone. The roadmap therefore places heavy emphasis on the shared infrastructure that makes cross-scale collaboration possible: - Coordinated multi-site monitoring networks, - Benchmark datasets that are FAIR (findable, accessible, interoperable, and reusable), - Community-maintained semantic resources and ontologies, - Open-source sensor firmware and model code, - Standardized methodological descriptions, and - Data-governance frameworks that protect farmer privacy while enabling integration. Without these foundations, chamber-scale measurements cannot be reliably combined with tower, aircraft, and future satellite observations; machine-learning methods cannot be trained and validated on representative data; and models cannot be compared or improved across the regions and cropping systems that matter. Open science and cross-scale data coordination are central to Taylor Geospatial’s own research priorities. We see them as prerequisites for application-grounded geospatial research: only when data, methods, and models flow openly between researchers and the practitioner communities who use them can science translate reliably into on-the-ground impact. The roadmap’s framing of N₂O MMRV as shared, pre-competitive infrastructure, the foundation on which both public policy and private markets depend, is precisely where we believe geospatial can add the most value. ### **Applied research for the public benefit** Taylor Geospatial exists to produce geospatial research that benefits the public, and the N₂O roadmap is a clear example of what that looks like in practice. The roadmap’s intended audience is not narrowly academic. It is written to build momentum for applied research initiatives with end users and to ground policy in science. Ultimately we hope this roadmap will support the work of farmers making on-farm nitrogen decisions, NGOs designing supply-shed programs, companies trying to meet Scope 3 commitments without misreporting, regulators implementing the 2026 U.S. and EU climate disclosures, and funders deciding where new dollars will have the greatest leverage. It makes the public-benefit case explicitly: improved MMRV lowers farmer input costs, reduces public remediation costs, protects drinking water, strengthens the credibility of climate markets, and ensures that public investments in nitrogen management can actually be evaluated against measurable outcomes. This co-development posture–research that is shaped by and delivered back to the communities it is meant to serve–is how Taylor Geospatial approaches applied research across all of our priority areas, including agriculture and food security, humanitarian and disaster relief, defense and security, and regional natural resource management. ### **Fostering St. Louis as a hub of geospatial expertise** St. Louis sits at a unique intersection for a project like this one. It is the gateway to the Corn Belt, the region that dominates U.S. agricultural N₂O emissions and whose corn-soybean rotations on poorly drained soils figure prominently in the roadmap. It is home to a remarkable concentration of agricultural-technology companies, plant-science institutions, geospatial firms, and federal partners. It’s also home to a growing research community with the breadth needed to tackle problems that cross soil science, remote sensing, atmospheric modeling, AI, and decision support. Helping convene this roadmap is part of how Taylor Geospatial is growing that hub. Ongoing engagement with this research agenda brings leading N₂O researchers into contact with St. Louis-based expertise in GeoAI, satellite data infrastructure, and agricultural innovation. It gives local companies, growers, and agencies a direct line into a national research agenda whose priorities–including cheaper sensors, multi-scale monitoring networks, hybrid models, and FAIR data pipeline–align closely with capabilities the St. Louis ecosystem is building and with the practitioner communities it serves. ![A scenic view of a rural landscape with green cornfields, a red barn, a white farmhouse, and a backdrop of trees under a blue sky with scattered clouds.](https://taylorgeospatial.org/wp-content/uploads/2026/06/midwest-corn-farm-1920x1441.jpg)Midwest corn field (David Mark via Pixabay)### **What comes next** The roadmap identifies thirty-seven high-value research opportunities and distills them into ten community-consensus priorities that it emphasizes as the near-term focus for the field. Those ten priorities span cheaper and more portable sensors, new measurement modalities, coordinated multi-site networks, rigorous QA/QC, methods that connect measurements across scales, FAIR data practices, sustained data infrastructure, faster pipelines from observations into models, regular model intercomparison, and the integration of process-based models with machine learning. Open science, cross-scale coordination, and sustained co-development with practitioner communities are central to driving the uptake of this agenda. Taylor Geospatial intends to keep playing a connective role to help turn the roadmap’s recommendations into reality. We are grateful to the authors, to our partners at NRDC, and to every researcher who contributed time and expertise to this community-built document. The work of turning its priorities into deployed sensors, calibrated models, and usable decision tools is just beginning, and we look forward to being part of what comes next. *Rachel Opitz served as a co-convener of the roadmap on behalf of Taylor Geospatial, alongside Daniel Rath and Matthew Kaplan of NRDC.* [Download the report](https://osf.io/rt63b/files/vkepc) **Categories:** From Taylor Geospatial --- ### [SatSummit Comes to St. Louis](https://taylorgeospatial.org/news/satsummit-comes-to-st-louis/) **Published:** June 18, 2026 **Author:** Allison Braun **Excerpt:** Taylor Geospatial to co-host conference for leaders in the satellite industry and experts in global development November 18-19, 2026, at The Post Building. **Content:** For more than a decade, [SatSummit](http://satsummit.io) has brought together leaders from across the satellite, Earth observation, and geospatial communities to explore the technologies, ideas, and challenges shaping our world. Taylor Geospatial is excited to be a part of the next chapter as SatSummit returns with two upcoming editions: - **St. Louis, Missouri: November 18-19, 2026 at The Post Building** - Lisbon, Portugal: March 16-17, 2027 Previously hosted in Libson and Washington D.C., SatSummit is expanding its footprint while staying true to its core mission: creating space for meaningful conversations about the future of our field. Together with [Common Space](https://www.commonspace.world/) and [Development Seed](https://developmentseed.org/), we look forward to convening practitioners, researchers, policymakers, entrepreneurs, nonprofits, and technology leaders for two days of discussion, debate, and connection November 18-19, 2026 at The Post Building. Located at 900 N. Tucker Blvd, [The Post Building](https://www.thepoststl.com/) is a premier $70-million redevelopment of the former St. Louis Post-Dispatch headquarters, transformed into the epicenter of St. Louis’ rapidly growing geospatial intelligence (GEOINT) and fintech ecosystem. ## About SatSummit The satellite and geospatial sectors are entering a period of rapid change. Artificial intelligence is reshaping how Earth observation data is analyzed and applied. Commercial and government priorities are increasingly intertwined. Questions around competition, public access to data, funding, ethics, and long-term impact are becoming more urgent than ever. SatSummit St. Louis will create space for the conversations our community needs right now—not only about what technology can do, but about who benefits, who decides, and what kind of future we want to build. Topics will include: - Sustaining community resources as funding landscapes shift - The dual-use dilemma and when satellite imagery serves both humanitarian and military ends - How humanitarian and climate organizations are doing mission-critical work with less - GeoAI in practice: what works, what’s hype, and who benefits - Commercial imagery, government pressure and the price of access - And more! Additional details on speakers, [sponsorship opportunities](https://satsummit.io/2026-st-louis-sponsor-prospectus.pdf), registration, and programming will be announced in the coming weeks. Join us in St. Louis this November! **Categories:** In the News --- ### [Fields of the World on Great Data Products](https://taylorgeospatial.org/news/fields-of-the-world-great-data-products/) **Published:** May 19, 2026 **Author:** Allison Braun **Excerpt:** Jed Sundwall, executive director of Radiant Earth, creator of Source Cooperative, interviews Jen Marcus and Isaac Corley about Fields of The World. **Content:** Our Vice President of Strategic Innovation Programs [Jennifer Marcus](https://www.linkedin.com/in/jennifer-marcus-b559091/) and Director of AI/ML [Isaac Corley](https://www.linkedin.com/in/isaaccorley/) sit down for a conversation with Taylor Geospatial strategic partner [Jed Sundwall](https://www.linkedin.com/in/jedsundwall/), Executive Director of [Radiant Earth](https://radiant.earth/), creator of [Source Cooperative](https://source.coop/). Jed has been a co-conspirator on Fields of The World since the beginning, so we were excited to celebrate and discuss our most recent milestone together: mapping agricultural field boundaries at global scale. **Categories:** Fields of the World, In the News --- ### [Mapping The World at Taylor Geospatial](https://taylorgeospatial.org/news/mapping-the-world-at-taylor-geospatial/) **Published:** June 11, 2026 **Author:** Allison Braun **Excerpt:** Robin Cole from the Satellite Image Deep Learning podcast interviews Jennifer Marcus, VP for Strategic Innovation Programs and Isaac Corley, Director of AI/ML Research, about Fields of The World. **Content:** ### Episode Description In this episode I sat down with Jennifer Marcus and Isaac Corley from Taylor Geospatial to explore Fields of the World – an open initiative to create globally consistent agricultural field boundary datasets from satellite imagery using AI and cloud-native geospatial infrastructure. Taylor Geospatial, a newly formed research organization, is building openly licensed global datasets as foundational public goods. Jen and Isaac explain the motivation behind the project, the challenges of scaling machine learning beyond well-labelled regions, and why openness in datasets, tooling, and intermediate model outputs, is central to their approach. We dive into the technical details behind the first global release: assembling noisy and uneven benchmark datasets from around the world, training models that generalise across diverse agricultural systems, and releasing everything from Sentinel-2 mosaics and raw segmentation probabilities to polygonised field boundaries through Source Cooperative. Along the way, we discuss community-driven improvement loops inspired by OpenStreetMap, the limitations of 10 m imagery for smallholder agriculture, and the importance of pairing academic researchers with engineering teams to rapidly operationalise new methods. Finally, we look ahead to Taylor Geospatial’s next phase – richer agricultural datasets, “Features of the World,” and a benchmarking initiative aimed at improving evaluation standards and reproducibility across geospatial foundation models. **Listen or download** this episode of the [Satellite Deep Image Learning](https://www.satellite-image-deep-learning.com/p/mapping-the-world-at-taylor-geospatial) podcast on [Apple Podcasts](https://podcasts.apple.com/ca/podcast/mapping-the-world-at-taylor-geospatial/id1668125812?i=1000771999276) or [Spotify](https://open.spotify.com/episode/4ahpCdS2OLZRc64Iru674k?si=99550584e9da490e). **Categories:** Fields of the World, In the News --- ### [Mapping Every Field on Earth](https://taylorgeospatial.org/news/fields-of-the-world-matt-forrest/) **Published:** June 10, 2026 **Author:** Allison Braun **Excerpt:** Spatial Stack's Matt Forrest interviews Jennifer Marcus, VP for Strategic Innovation Programs and Isaac Corley, Director of AI/ML Research, about Fields of The World. **Content:** ## Episode Description What does it actually take to map every agricultural field on Earth? In this episode, Matt sits down with Jen Marcus, Vice President of Strategic Innovation Programs at Taylor Geospatial, and Isaac Corley, Director of AI/ML Research at Taylor Geospatial and a torchgeo maintainer, the team behind Fields of The World (FTW). In late April they released the first globally consistent dataset of agricultural field boundaries, at 10m resolution, fully open on Source Cooperative. They dive deep into how it came together, from building the fiboa format to standardize ground truth across 24 countries, to running model inference across the entire planet, to shipping it with a confidence layer instead of pretending it was perfect. You’ll hear honest perspective on what GeoAI can really do today and where the hype outpaces reality. In this episode, we cover: - Why a global field boundary map had never been done, and why no single organization was positioned to do it - The labeled-data problem and why models have to generalize to places like South America and Africa with little ground truth - The fiboa format and Chris Holmes’s “architectures of participation” - How the Technical Fellows program turned open-source contributors into the core team - Running global inference efficiently with Sentinel-2 planting and harvest mosaics - Cloud-native outputs (GeoParquet, PMTiles, Zarr) you can stream with no backend - What’s real vs. what’s marketing in geospatial AI, and the ImageNet lesson - What’s next: stakeholder feedback loops, higher-resolution imagery, and mapping new features beyond fields Whether you build ML pipelines, work with satellite data, or you’ve ever wondered how much of the planet is still genuinely unmapped, this conversation breaks it down without the buzzwords. **Listen to or download** this episode of the [Spatial Stack](https://www.youtube.com/playlist?list=PL6L1mY6cDuDAqOlXiKKQGV-DxCKZlaqfD) podcast on [Apple podcasts](https://podcasts.apple.com/us/podcast/mapping-every-field-on-earth-global-field-boundaries/id1748871891?i=1000772072365) or [Spotify](https://open.spotify.com/episode/7mOK8Rlj9pHYMptVczJkT7?si=1dfc9d5c860a4c30). Connect with the host Matt Forrest: 📸 Instagram: [ / matt\_forrest ](https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa2YtdXRpUEMwc0dXR0lmbU1aaTNkdGlfS3gyd3xBQ3Jtc0tsR2piX3U5dVdUNXVLUmlyQzlMNzEyWUFjUzQ1eVpVR1MzcDNTdXU1MGRQenpBTXA4RFRUcy1iM0NNYjNiOVI4MjAzTFBzekNBZnJpVk1HVk9EeEdmdEQ1S0NSZHR2bDZGX0FoRFJtaGpMWXNzVzlxRQ&q=https%3A%2F%2Fwww.instagram.com%2Fmatt_forrest%2F&v=uZIOjoZAReo) 💼 LinkedIn: [ / mbforr ](https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbEVTQ2ZpbHpCSFU5ZDBXMTRnamlIV3gwTXlGQXxBQ3Jtc0ttQlhOUDlmR3JnTm5kSFRXcXJ3T1lsdkJCbHFNdzBZOU5tYk9SY19pQUhGMUNvWUltRzZkWmFvRl80dDZUOWd3TjVNTlZndDJxWEFDT2E3Wk9fR01SdG5JdHliS1ZtR2UtTS05TmZNSGhQVGttVHh2Zw&q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fmbforr%2F&v=uZIOjoZAReo) 📧 Newsletter: [https://forrest.nyc](https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbmJiUGtzN3ZzOTEzODJxNjBXdVA3OEZ6NTUwZ3xBQ3Jtc0tuVVIyRmVqb3lNaE9lRXVDOXlSV2sxb0J6ZU9YT0tuT3ZvMjNpNzNsVnJHQS1kd1daNEJBdXgzbm5XYzZFek9nVFBSUzlaMnVQbl83dzBXYklqNmQ3aDFqYWs0MlZsOVVQcGJVRDV2OW5tOTlLYUtNTQ&q=https%3A%2F%2Fforrest.nyc%2F&v=uZIOjoZAReo) 🌐 Website: [https://forrest.nyc](https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbXRsRVczX2JtVlVEemdPbFlqcEZ2ZnpKNnFHd3xBQ3Jtc0ttZEY0T3llR3hZNDRZTjBQaXFSbnpzd2NXNWswWGxnd3ZvXzczWEJPR2lNeHdhZDVkd0plNE9aQU8wWVVuWjV4eU9uNWdZeG9HcDlOZUhTVXNZOVpLaEZsNHB4WHB1cTlEOVE1bkQ0TjBtcWRiTG43dw&q=https%3A%2F%2Fforrest.nyc%2F&v=uZIOjoZAReo) ### Read the transcript+ *Edited for brevity and clarity.* **Matt Forrest:** Today, I’m sitting down with Jen Marcus and Isaac Corley from Taylor Geospatial, the team behind Fields of the World. In late April, they released the first global map of every agricultural field on Earth. If you work with satellite imagery, build machine learning models, or you’ve ever wondered why something this basic has never been done before, this conversation is for you. We get into what it actually takes to do this at a global scale, the label data problem, the [fiboa format](https://fiboa.org/) to standardize ground truth from 24 countries, running inference across the whole planet, and the constant tension between what AI can really do and what’s just marketing. This is the Spatial Stack, and as always, the future is spatial, so let’s get into it. Jen and Isaac, welcome. I’m excited to talk today, learn a little bit more about Fields of the World, which is one of the more interesting projects that I’ve seen kind of take place in all things geospatial. But I think it’s definitely something that has drawn a lot of interest. What drove this and how are you working on taking this forward from there? But before we jump in, if you both wanted to give a quick intro of who you are? **Jennifer Marcus:** Super. Thanks for having us. We’re grateful for your interest and kind words at the beginning there. I’m Jen Marcus. I’m the Vice President for Strategic Innovation Programs at Taylor Geospatial. **Isaac Corley:** Hey, Matt. Great to be back. Thanks for having us. I’m Isaac, Director of \[AI/ML\] Research at Taylor Geospatial, and I joined about halfway through \[Fields of The World\]. So Jen definitely can talk more about the inception of all of this, but I helped get it across the finish line to help train the final models and actually do the global inference run and publish everything. So yeah, happy to be here. **Matt Forrest:** Yeah, we talked a lot last time around and I think what’s interesting is, you know, everything in geospatial is starting to get bigger, right? We’re looking at doing things with more data, looking at global phenomena, even into some of the models and embeddings and things like that. But what I think is particularly interesting is you took a very specific problem, right? Understanding the agricultural landscape of the world and simply mapping it, which is something that, as far as I know, has been talked about, or at least explored, but never completed, at least to this degree. So I wanted to understand, and just start at the beginning, why did you pick this problem? What were the motivating factors that went into that, and more importantly, why has this never been done before? **Jennifer Marcus:** That’s a lot there. That could cover this whole time period! There’s a number of whys, but the most general why, and we have to step back a little bit. It’s not actually about field boundaries per se. It’s about the massive amount of satellite imagery that has come to exist in the last decade. Will Marshall, who’s a co-founder of Planet Labs, in 2014 said, If you had a daily scan of the Earth, what would you do with that? I spent a lot of my career focused on the national security defense and intelligence customer. The National Geospatial Intelligence Agency said: We want a daily take. That’s what we want. That’s what we need. A picture everywhere on Earth, every day. In 2017, Planet made that happen. Since then, this whole industry with the lowering costs of launch and the minimization of satellites, we have the daily take of the Earth and then some every day. It became very clear to me that in the expense and the fundraising of launching that many satellites and getting that much into space, something was missing. We kind of jumped over the concept of: It’s not the data collector in space we want. It’s the insights that are held in all that imagery that we want. It’s a lot of work to get that out, to have the machines tell us what’s captured in all that imagery. So, I also saw that a lot of people were working on small parts of this and the repetitive work on small parts of pulling features out of satellite imagery meant that we weren’t getting an effort to pull something out at **global scale** that everyone could use and move on to their actual special use case or their special analysis. Where the opportunity came from is that we have a philanthropist, Andy Taylor, who got excited about geospatial and was willing to fund this organization to do this work, to increase the innovation capacity. So, we were able to ask ourselves, what could we contribute to our field? Pun, actually, not intended there. But we had the great opportunity to think about it, not from a for-profit, fundraising, or could we IPO? But from a nonprofit standpoint, what could we do to take this geospatial field that I’ve known and loved my entire adult life and move it forward so that the promise of all that information held in imagery could be unlocked and made available to solve some really massive questions of our time? **Matt Forrest:** Yeah, a lot has been driven by the data and I think just the sheer amount of data that we’ve been able to collect and it just keeps going up every year. You hear we have more data, better data, better granularity. We have more and more things that we can do with that. But really taking something out of it and extracting that from any different angle that you want–so much of that seems to be on very local scales or smaller scales. You can do that for a very specific area, be that even just a part of a region or small scales like that. How do you take that problem and then start to go to that global level scale, and what does the data that goes into that look like? Because I imagine it can’t be, you mentioned a daily scan of the Earth, right? You probably need a lot of scans of the Earth to get a consistent picture of these areas. Also, how do you account for things like change and stuff like that? When you started to map out this project, what are some of the big considerations that you had to start to really understand before you got into this, to understand what this means to do this at global scale and differences in fields and boundaries and all these different pieces? What went into some of that thinking even before starting to go down this road? **Jennifer Marcus:** I can answer some of that from a people and organizational standpoint, and I can let Isaac address it from a technical perspective. **We knew from the beginning that we wanted to tackle a problem that was bigger than any one organization could do**. In fact, it’s the same, it’s the flip side of what I talked about before. **If you’re going to do something at global scale, it requires collaboration**, **and the reason it doesn’t already exist is because no one organization is incentivized or positioned to do it.** So we thought if we could create an ecosystem of–one of my collaborators from the beginning on this is Chris Holmes, he calls them “architectures of participation”–how could we think about this problem in a way that opens it up to have contributions from many organizations contributing their strengths so that we’re not replicating strengths or things that people have already solved? How could we formulate projects or a project approach or build a community to do that? What that’s resulted in is this ecosystem of capabilities that we’ve created. Isaac, I’ll let you talk about that part, the technical part. **Isaac Corley:** Yeah, I think it goes back to machine learning 101 or at least geospatial machine learning 101 and the ability to generalize. So, the reason why things are very local is because they only have the labels for areas people care about, and everyone doesn’t work together to do this on a global scale. So, somebody has to be the first one that goes and collects all the label data sets and works with partners and organizations to not only collect them, but get them in the right format. So, once you have all the data, we had to create the new fiboa format so that we can all agree on some kind of schema, even if it’s very minimal, that we can have people transform into fiboa format, and then we can just aggregate all that and create machine learning data sets that we can train models on. Then we have to do all these experiments in evals. So it really is like almost agnostic of the model itself, and it almost goes back to just the basics of everything is spatially autocorrelated, so we want things to generalize because we don’t have labeled data for the entire Earth. That’s the number one problem. So, we’re having to get data sets from very specific areas and hope that they generalize or work and find out tips and tricks that we can do to make it generalize to areas like South America, Africa, where there’s not a lot of labeled data for that. Then once you have that, you have to run it at scale, and there’s so many parameters just to get it running at scale and to make it work across the globe. People don’t consider that outside of: I train my model. Now what? There’s all these other parameters and knobs you have to tune just to do global inference. So, we had to spend a lot of time on that, not considering post-processing. I think there’s a lot of work that has to go into this entire pipeline and somebody had to end up doing it eventually. I always go back to, I always say this is very much a, “If you build it, they will come” situation. Nobody is actually getting together and organizing and contributing. But now that we put it out there, we already know it’s not perfect. There are some areas that are not amazing that we don’t have labeled data in. We put out a paper and produced a confidence layer because we don’t have ground truth to even compare to tell people when they ask, “How well does it do in smallholder fields in Rwanda?” or something. We don’t have ground truth for that. So, we have to make some assumptions and some estimates to give people confidence in what they can depend on what they can’t. But yeah, we’ve been swarmed with the amount of people now that want to contribute and add label data to it just because we put it out there first. So, I think that’s a big part of it–that is the mission of Taylor Geospatial. Someone has to lead the charge to get everyone on the hype train to start contributing, and that’s really what we’ve done. **Matt Forrest:** Yeah, walk me through that process as well. Like when you’ve picked a problem and you’re going to start solving it. First of all, how did that work from getting people involved? Was that you finding them? Was it creating the spec? And I do want to talk a little bit about fiboa because I don’t want to skip over explaining what that is to folks listening so they can kind of understand what that plays into it. Because it sounds like there’s this whole organizational participation layer of just getting people to come to the table and start talking about it first. Then you have to give them a way to consistently contribute data in a way that’s useful and well formatted and structured. Then from there, how do you take that and actually say, okay, well now we have a model. How do you actually technically scale that from just on your computer to actually running a complete process to make that work, which is a different engineering process. It seems like there’s multiple layers of like design here from participation to schemas and data collection all the way through to ML ops at scale. So tell me, start at the beginning and kind of say, okay, here’s who came to the table, here’s how we got people involved, here’s how we decided on a specification, and here’s how we actually made this thing run, right? How did you do all three of those pieces? **Jennifer Marcus:** Yeah, this is where the magic is. I still to this day am dazzled by how this went. Chris Holmes and Jed Sundwall and I… so I was charged with: Figure out a way to kind of migrate academic research and geospatial on the cutting edge into commercial impact or that has the potential for commercial impact, get it closer to commercializable. I knew that from previous work that I had done, I knew that what I saw in academic labs was great work that was, from the words of it, massively relevant to what industry needs. But when I looked at it closer, being a person who’d always been on the industry side and the customer facing side, I did not know what I was looking at. I couldn’t take it and make a map or do something because it just was done for academic purpose in a paper. And the incentives there were for papers to be published. And sometimes you have a requirement for it to be replicated, but not replicated at scale, not done in formats that people could take and run with. So, we knew there was a gap that we wanted to bridge between cutting edge academic innovation and usability by a broad community. So, what we did was we gathered a group of people, basically a coalition of the willing, and invited them to St. Louis, had a meeting, said: Here’s this thing we think we’re going to do, what we want to create. We had already decided field boundaries was a good place to be because it was global, has tons of use cases, has a lot of climate impact that can be asked and things like that. Plus AgTech, there’s also a lot of commercial capabilities that are looking at agriculture. So, that was sort of easy for us as a starting point. Jed, Chris, and I sat down and thought of everyone we knew who might have an interest and might want to work with us. We really did not know what we wanted to ask them to do or how to ask them to do it, but we knew that we were going to–and I keep saying this, I hope it’s okay if your channel is a little PG-13–but our mantra was: Get sh\*\* done. The first meeting we had was not going to be a meeting where we talked about what we wanted to do in the future. We sort of interwove talking about it with, let’s just sit down and scrap it out. Let’s see what we could walk away with. So fiboa was actually from that first meeting of, what if we were to try to bring data sets, ground truth data from all over the world, from different governments, from people who’d driven by with GPS on their mopeds and put that all into one place so we could use it as training data? What would this common spec need to be? You know, I was constantly saying, we’re not doing specifications. That’s not what we’re here to do. We’re doing something to enable us to bring a bunch of data together and eventually create a global data set. So, we sort of drove for the minimal, most basic features and attributes you would need in this to bring field boundary data sets together, and we’ll make it extensible so when we got it wrong, you can fix it later. We ended up because of that, you know, about half the people who joined us. I think we had 20 plus people that first time, from all over the world. About half of them walked away with a way they could continue to contribute and kind of excited about the approach. Over time, we awarded grants to the research teams. We had this idea of having technical fellows who would run alongside the research teams and say, hey, if you put that data in this format, then other people could use it right away. If we store it on Source Cooperative, then people can find it. So, we sort of started drawing–and this is how we met Isaac–people who wanted to work like that, who were working in the open. I don’t think we’ve said this explicitly in this call, but everything we do, we’re publishing as open source in every piece of the ecosystem. So, you find these incredible, incredibly talented people who are working on the side because they’re motivated by this kind of work and their friends are doing it. So, we end up with people that are contributing. I was saying to our team, there’s people we don’t know putting code in there, guys. Like, what do we do? How do we get them out of there? And they’re like, no, no, that’s what we want. We know them! They are highly qualified people. So we ended up, kind of after the fact, creating this program called the Technical Fellows Program. I’m like, if they’re good, I’ll pay them a stipend to keep them around and keep them motivated. So that just grew and kind of shook out to be the research teams and the technical fellow teams. Then we had some infrastructure partners who were doing the storage and dissemination, Wherobots doing the scale up of infrastructure. So, we had partners also that were our scaling partners, really, come along when we needed to do that. **Matt Forrest:** Yeah. Then once you get to that point, when you brought these people together and you’re obviously getting data contributions, you’re getting code contributions, you’re getting sort of this critical mass going from that perspective. Now you’re faced with the challenge of actually running this thing and getting the global data produced. What does that look like? Because, you know, again, you mentioned that there’s incredible research that’s going on from the academic side producing very good models. You’re now sitting on top of this collection of ground truth data and data sources that can be used. It’s out there, it’s in the open. I’ve used it, I’ve seen it sitting on Source Cooperative, I’ve seen it. How do you go from that to now this final output that you produce, both the map, the data set, everything from there to help that scale from the technical side? Because I think that’s one thing that I’ve seen. You can go on online and you can find incredible models that people have published or papers, things that are sitting on GitHub that look very cool. I’ve even seen great global data sets be produced and they’re sitting on a Google Drive and just kind of, you can get them, they’re there. So how did you go to build that with both the engineering design in mind and then also the ability to make this useful for all the other people downstream that want to use it? What did that kind of process look like? **Jennifer Marcus:** I want to say one thing and then I want to hand it to Isaac for the technical side of it. But I think that we were relentless about our North Star, which was, can we do this at global scale? Can we do it? I asked myself the question over this time period of like, should we be doing this? Like a couple of times I called Chris Holmes and I was like, what are we doing? Like nobody thinks this is a good idea. But we kept insisting, no, it is because we’re going to do it at global scale and then local scale will try use cases and we’ll try it out and they’ll tell us what they like about it and we feed that back into the global scale. So I think just the insistence on that as an end state and our comfort with it not being perfect. Our comfort with our goal is to see if we can do it. And in that sometimes you can’t. And we found out there’s places that need improvement and we’re okay with that. So Isaac, I’ll let you talk about this from the technical standpoint. **Isaac Corley:** Yeah, I agree with Jen. I think a lot of people will get analysis paralysis and they won’t publish something because they think it’s not perfect or that there’s flaws. And I think definitely understanding what the long term goal is outside of criticism is really important. Like being okay with the criticism and already knowing in advance, like, what, what works and what doesn’t is super important. I’m unfortunately immune to criticism just by being in academia and publishing and having very rude reviews publicly online. But yeah, I think the big thing is like, like as you mentioned, Matt, you can find countless models online, right? I think actually we had a good partner in [Caleb](https://www.microsoft.com/en-us/research/people/davrob/) and [Hannah](https://hannah-rae.github.io/) who were mostly training like U-NETs and trying to find out what is the simplest model we can do, not just the fanciest model and that gets us an extra couple IOU percentage points. So, we already came in with a model that was already pretty efficient and is pretty well thought out–how would I run this on a full Sentinel 2 tile? So, really we just had to scale that up. I think the biggest complexity of our model was definitely because agriculture is very temporal oriented, we had to feed in a planting and a harvest season mosaic stack to the model just to get that contrast of planting versus harvest season, what the field looks like. But yeah, I think all of that requires–and that’s why shout out to Wherobots and the Rasterflow team–because there’s a lot of tuning and getting the mosaics right and efficient at scale because there’s a lot of ways to do it wrong. It takes a lot of engineering effort and experience and just the tenacity to keep trying to tune and get that extra bit of performance out. It takes a lot of effort on that side. But once we had got it connected and figured out, we were able to run it at a very efficient cost compared to Google Earth Engine or if we just ran it on single node, or whatnot, it would take forever. And I think that was important because we ended up doing multiple runs. It wasn’t like a single run. We had done a bunch of country scale inference like in our proof paper that’s at CVPR. We had released five country scale field boundary data sets for two years before we even did it global scale. So then releasing it, you know, we used a lot of the hottest new tools like Zarr and GeoParquet and PMTiles. And I think having that expertise from engineers at Wherobots definitely helped us rely on being able to ask questions about: what is the best format, what chunking and sharding should I use and how do I make this efficient? Because our goal was to get it in Source Cooperative in cloud native formats so that we are not required to have a backend. People can simply just stream it from cloud storage for whatever front end or application they want to use it for. We didn’t want to be yet another API that’s rate limiting everybody. And so thinking about all these things outside of just training the model and getting the model checkpoint and the benchmark data set out there, I think in academia, a lot of times that’s where the incentivization structure cuts you off and taking it that last mile to get it to a product, even if it’s not perfect, like getting it out there into the hands of people. We’ve just seen so many people with so many applications. I think someone was using it for a golf simulator game that they’re building. Yeah, it’s literally insane what people are using it for and we’re here for it. **Matt Forrest:** It’s funny that you say that. I mean, you both said we want to get it out there and get people reacting to it, interacting with it and just seeing it. Despite the fact that it may not be 100% perfect in all areas. There’s a certain amount of thick skin you have to build up to be able to say, okay, there’s going to be feedback and stuff like that. Usually what I find is that the positive aspects of that generally outweigh the, “well, this wasn’t right, so it’s not perfec”t type of thing. And I’m sure you’re seeing that from how people are even just using the data now or being able to interact with it and contribute to it. I think there’s something interesting in that, and I want to get into what the future looks like maybe a little bit as we start to go through this discussion. One thing I did want to dig into a little bit more as well was just understanding the really key parts that came together to solve this on a global scale. And I think that’s one thing that’s probably the most impactful and and one thing that you know, maybe not a lot of folks understand. You know, people in the public, I think generally think that most things have been mapped, right? You have Google Maps and I can go anywhere in the world and click here and see these things. And while that may be true, and we know that there’s certain limits in different areas and stuff, and we’re still working on that for things maybe like roads or cities and some things like that. But even stuff like buildings and obviously fields, but you’re solving that now. Why does that specifically matter? And what does that look like kind of having solved this at least once or gone through this process. What are other things this opens up to start to explore, to map at a global level? **Jennifer Marcus:** Yeah, there’s a lot there. The component pieces that came together are the benchmark data set. Then we did a whole series of model evaluations and model tweaking to get the best results. And then there’s running it at scale, the infrastructure to do that, which is more commoditized. And then publishing it out on Source Cooperative, again, that’s riding on AWS. So, we really focused on not having any ownership over things that somebody else was building a business around. And I’ve seen that a lot in my career is people set out to do one thing and then they’re like, well, we should probably do this thing too and this thing too, but we’re not really experts in that thing. So, we just focused on the pieces that needed to be moved forward. And you said something that really resonated with me, and that was that I could see this hype cycle going to where no one knew anymore what is actually what, what the AI and satellite imagery, AI writ large is actually capable of and what is kind of marketing speak. I talked to some very senior government people who would tell me–and research people–this was what I found interesting. One researcher who still works with us, I was talking to him about, are you interested in doing global data sets? And he was like, yeah, but you know, they’ve already done that. I’m like, who? And he lists, you know, the usual suspects in our industry. And I said, how do you know? And he said, well, it’s on their website. And I’m like, dude, that’s your market research? Like that’s marketing. That’s not real. It means you could do that if you paid them a hundred million dollars. So I was like, there’s really something here. There’s a lot of organizations who have a motivation to market either their cloud storage or their scaling things at, or running models at scale, but they don’t even have something to do that. They’re doing that on “maybe someday we’ll…” And the other thing was–I need to go check because I’ve been talking about this recently–but Chris Holmes and I typed into ChatGPT when we were getting this started, “How many farm fields are in Kansas?” I’m from Kansas. And it said, I don’t know. It said, you could call the Kansas Land Bureau. There’s this organization. You know, so I’m like, we need to get this realm ready for the other capabilities that aren’t in geospatial that are advancing like crazy. And also I had read, listened to, actually, Fei-Fei Li’s book about her career, but about the creation of ImageNet. There were a lot of times that there was doubt and no one thought it was worth anything and it wasn’t the right thing to do. And eventually it grew to where it was. And so that sort of helped me because I’m like, well, she just got awarded, I don’t know what it was, $250 million to run a company, but she spent 10, 15 years in this realm of holding onto her North Star. So Isaac, you take it from there. **Isaac Corley:** Yeah, it’s great that you bring up ImageNet and Fei Fei Li because I always think of Fields of the World as like, sometimes I’m like, is this an agricultural foundation model? But yeah, I think there’s a lot that goes into it, but I would love to have Jen just kind of talk a little bit more about the outlook of what Features of the World is going to be. Because I think setting all this up was a really hard task, but then we need to go into the next step of, we did all this experimentation of how to get the pipeline right and how to get that formula right. And we’re planning to apply it to other things. I think one misconception people have is like, oh, Taylor Geospatial is an agricultural research shop. It’s like, no, this is just one piece of the larger scheme that we have that we’re wanting to go into. So I’d love to spend some more time on just that. **Matt Forrest:** Yeah, I think that’s really important is that, yes, problem one was agriculture, right? It provided a good framework to go to this, that, and for, I understand it because I kind of work in this space, but like, if someone’s thinking, why do you want to… Why fields? Why farms? What does that mean? Well, I mean, there’s obviously the social implications that you mentioned, the climate implications, all these things. But from a pure challenge, I mean, the collaboration of collecting, you said ground truth data from many different countries and getting that in is one piece. But they also look very different in different places. And that’s something I understood, is that a farm in Iowa is going to look way different than a farm in Brazil versus a farm in… Africa or any other place. They’re just fundamentally different in terms of how they look, their seasonality, there’s different harvest seasons depending on what part of the globe you’re in, different crops that are grown. Not to mention all the other fun things that come with satellite images like cloud cover. Mapping in Brazil is very difficult because there’s a lot of clouds there. You just have to deal with all these different pieces. But then there’s the organizational piece that I think is really a cool framework to say, okay, how do you take that forward to apply this to the next challenge. You went into it not knowing how it would all come together. And I think those are some of the most fruitful ventures to actually come forward to actually say, okay, how do we bring people together and do things like this? And that’s going to show you, okay, here’s the elements we need, and it might look a little different the next time around, but you’re able to do all those different pieces and bring that together. So I guess the big question or two questions that might be are, number one, what does that look like for Fields of The World? What are the next iterations of that going forward? And then how do you plan to apply all the stuff you learned from fields of the world, the entire process part one, into future problems? Because I think that’s also interesting to say, what else is out there that you can start to explore? And now that you’ve done it once, obviously you’re starting from a place further than you were before. How is that going to take forward and solve the next big problem that you need to solve? **Jennifer Marcus:** Yeah. So I’ll start with Fields. We knew from the beginning that we wanted to have the voice of people who would be potential future users in the mix, and we were able to do that with the fiboa spec and some other things. But where we were as an organization at that time wasn’t established enough to really manage what we call stakeholders in the mix throughout. With the current organization that we just established in January, we are now in a position to do that. So with Fields of The World, there’s two things that are going to happen. One is us really working hand in hand with a handful of interested users to really understand whether their purview is global or their purview is local. We’re going to work with those communities and understand how this could be made better for them and really focus on a feedback loop that gets better data on the output or on ground truth side and have some automation to loop that in to improve what the model spits out. So essentially working with stakeholders is next. Then we’re also going to expand the benchmark data set. We’re going to look at higher resolution imagery and look at how that affects plus or minus to the outputs. So taking that whole end to end and really looking at everywhere we could make it better along the way is next for field boundaries. The other thing we’re want to do is say, okay, we know what all the pieces and parts are, and this is why this problem set to me was a good one from the beginning, ’cause I was asking again, that same researcher that got ideas from websites, you know, marketing websites, was if you do one global data set or another global data set, is there a series of problems, technical approaches in there that are the same across all of them? And he was like, oh yeah. From the beginning also, we wanted this to be componentized and the architectures of participation so that we could then say, is the effort to then say, well, now I don’t want a field, I want a building globally. So right now we’re in the process of identifying what feature or features should that be that we tackle next. And we already kind of have some rough ideas of where to go, but looking at what we could do that would be in about the same timeframe we’ve just done and in high impact. So that’s where we’re going to take those things. The other piece that we’re going to do, which kind of goes back to, and Isaac has thought a lot about this, but it goes back to: What’s real and what’s hype? So we’re going to do some work on benchmarking. It’s not sexy, but it’s a thing that we can offer, to do a real analysis of what can you do globally or what can you do for a certain use case, and what’s the best way to do it? **Because one of the things that we really want as an outcome of our organization is that the research is going where the problems are, rather than people having to guess.** **Matt Forrest:** Yeah, I’m curious to see how this goes forward in the future. There’s a lot of things that can continue to make it more accurate, I guess you could say, and more useful. I think you’re already starting to see that, I believe, from people providing feedback and stuff like that. And that’s one of the great things about getting something out there and letting people react to it is that you’re already getting more feedback there, but then you’re even more set up for next time to make this successful in terms of knowing all the pieces you need to bring together to do that. I think what’s most interesting to me is that it’s solving one of those problems that maybe people weren’t fully aware of. Just in terms of impact, I put together a short video about this and published it out on social media. Between the two channels that I’m tracking, I think it’s been viewed 1.8 million times. just people are really interested in this stuff. They’re not aware that these things are happening, and I think that they need to kind of see that and understand that. And I think that’s really where the cool work is happening, that you’re able to really see the full view of the problem and actually make it happen and make something come out of it. I think there’s a lot to be said about that, which is really great. So I’m excited to see what comes next and stuff like that, both from fields and from future problems. Something that has always been a true component in anything in geography has been exploring what is maybe unknown, and this is certainly one of those things. And it may not be, you’re going to the Arctic Circle, but I think you’re doing some of the work in the same vein that is solving these problems that we don’t know about our planet, and I think that’s one of the coolest things about all of this. And I think that’s what people get excited about and want to get behind. As we wrap up here, any final thoughts on some of the work that you’re doing, things going forward? Or more importantly, how can people that may be listening to this for the first time find out about fields, use the data, and get involved if they want to? How can they do that? **Jennifer Marcus:** Well, first off, thank you for having us and for your excitement about this because that means the world to us. Secondly, we are just beyond fortunate to be able to do this. I get chills thinking about it. We’ve been beyond fortunate about people like Isaac who’ve come and joined us, and we were lucky enough to be able to hire Isaac because of the good work he was doing and the alignment. I actually think that’s–as much hard work as all of this is and very sophisticated, you know, PhD level work–it’s also, there’s magic in the people and in the way we work together and we’re going to see how to scale that. You know, how can we keep, um, keep the magic and not stifle it. But we’re so grateful for Taylor Geospatial giving us this platform to be able to solve these problems and do the hard work. So we’re really just beyond grateful. **Isaac Corley:** Yeah, I think shout out to like the open source geospatial community because if you look at all the tech fellows we have and a lot of the contributors to some of our GitHub repositories, it’s pretty much entirely dominated by those who have been in the open source geospatial scene for a long time. We’re very happy to be able to fund them to work on fun stuff and like global scale. I think there’s definitely an excitement that they get about being a part of something that allows them to, you know, put something global that, you know, as you mentioned, **a lot of people would assume, oh, it’s 2026, like we’ve definitely mapped every field that’s ever existed, right? And the answer is no**. And so we get compared a lot recently to like, we’re like the Open Street Map of field boundaries where, you know, people are, you know, we’re building an annotation tool where people can edit, you know, fields that they feel like are not well represented or they’re missing in our data set. And over time, I think there can be this, this growth potential that will have an even better understanding of our, our world and our Earth and for food security and all the different aspects that in agricultural applications, but also change, like how has it changed over time? Because there is a lot of change and we’ve had a lot of people reach out about that as well. Yeah, shout out to the open source geospatial community. Shout out the Rasterflow team at Wherpbots for all the support. Hannah Kerner’s lab, [Nathan Jacobs’ lab](https://mvrl.cse.wustl.edu/), everybody who spent time training models and trying to get the best performance out of the models. That was a lot of effort and we sat in a lot of meetings and I had to crowd everyone to get on the same page a lot of times. But I think we, as Jen mentioned, we always had this like North Star and everyone on the team was pushing towards like, let’s get something that can work and scale and put it out there. And it doesn’t just disappear into the black hole of archive papers over time. **Jennifer Marcus:** Yeah, I want to add that the researchers who were willing to work in this way They took a leap because we said, we don’t exactly know how we’re going to do this. And so they were willing to expose themselves to that. And it’s really worked out well. The two things I’d like to kind of plug while we’re here are that one, we have a postdoc position open that Caleb Robinson put out on LinkedIn. So it’s a postdoc that we’re funding that will be hosted at Washington University in St. Louis and directed by Caleb from the Microsoft AI for Good Lab. So we’ve seen a lot of really good candidates already, but if there’s anybody listening to this, then you are probably a good candidate and you should reach out. And then the other thing is If you’re interested, if you’re in the open source community as a developer and interested in joining us, we do have the Tech Fellows program and we would love to talk because we need more help along that route. Last thing I’ll say is next time you’re on an airplane, sit by the window and look out. I’m in the Midwest, so every time I fly, I’m flying in the space where I can see the ground and see fields. And I was like, what’s so hard about this? I mean, you can see them, you can see the outline, but if you really look, you’re like, oh, well, I don’t know exactly what the boundary of that field is. So your own brain can’t do it. It’s hard to get the computers to do it. So that was a fun exercise for me when I was like, oh, I can’t segment that exactly, right? So look out the airplane and see what the world has to show you. **Matt Forrest:** Yeah, well, it’s certainly, I think, taking that, looking at other things and just understanding how you understand the world and understanding now this deep process that goes into actually doing these things. Hopefully that connects you to help understand what a monumental feat this was. So Jen, Isaac, thank you again for taking the time to join me. If you’re watching this, I’ll put all the relevant links in the description. Or if you’re listening to this, it’ll be in the show notes on wherever you’re listening to this on, But thank you both. I can definitely say I’m excited to see what is coming next. And I’m sure we’ll be hearing more from both of you and Taylor Geospatial in the future. So thanks for taking the time and we’ll talk soon. **Categories:** Fields of the World, In the News --- ### [Taylor Geospatial unveils global field dataset](https://taylorgeospatial.org/news/taylor-geospatial-unveils-global-field-dataset/) **Published:** May 4, 2026 **Author:** taylorgiwp **Categories:** Fields of the World, In the News --- ### [Boundaries of agricultural fields worldwide now publicly available](https://taylorgeospatial.org/news/boundaries-of-agricultural-fields-worldwide-now-publicly-available/) **Published:** May 5, 2026 **Author:** taylorgiwp **Categories:** Fields of the World, In the News --- ### [Taylor Geospatial spins off from SLU to become independent, adds AI research to list](https://taylorgeospatial.org/news/taylor-geospatial-spins-off-from-slu-to-become-independent-adds-ai-research-to-list/) **Published:** March 16, 2026 **Author:** taylorgiwp **Categories:** In the News --- ### [Taylor Geospatial Launches as a New Hub for GeoAI Innovation](https://taylorgeospatial.org/news/taylor-geospatial-launches-as-a-new-hub-for-geoai-innovation/) **Published:** March 5, 2026 **Author:** taylorgiwp **Content:** Taylor Geospatial today announced its launch as a new organization focused on unlocking AI-driven geospatial breakthroughs for global public benefit while strengthening innovation capacity in St. Louis. The organization brings together deep geospatial research expertise and proven pathways to commercialization under a single mission, leadership structure, and brand—positioning it to accelerate the development and real-world use of geospatial artificial intelligence (GeoAI). *“This new organization brings strategic focus to a fast-moving field at exactly the right time,”* said Robert Cardillo, Chair, Taylor Geospatial. *“Taylor Geospatial will be a trusted bridge—aligning research with operational needs and converting GeoAI innovation into reliable, scalable capabilities. Uniting our efforts under one organization gives partners a clear front door and strengthens our ability to deliver measurable impact.”* Formed by bringing together the Taylor Geospatial Institute and Taylor Geospatial Engine, both originally launched with support from a philanthropic gift from Andy Taylor, Executive Chairman of Enterprise Mobility, Taylor Geospatial unifies research and applied innovation under a single organization and brand. The launch contributes to St. Louis’s development as a national center for geospatial innovation while advancing accessible GeoAI tools, datasets, and digital public goods for global use. *“Society has reached an inflection point where the pace of scientific progress is faster than our ability to put it into practice,”* said Elliott Kellner, President, Taylor Geospatial. *“Taylor Geospatial was built to do the hard work of execution—connecting research to real operational needs, reducing fragmentation across the ecosystem, and turning promising GeoAI advances into tools and capabilities that people can actually use at scale.”* Every day, satellites generate vast volumes of Earth observation data, yet much of its potential remains untapped. Taylor Geospatial works with partners to turn that data into insight for the public good by accelerating the development and commercialization of GeoAI. The non-profit organization focuses on building shared scientific infrastructure—open datasets, benchmarks, models, and tools—that enable researchers, governments, and industry partners to reduce risk, accelerate adoption, and deliver tangible outcomes. Headquartered in St. Louis, Taylor Geospatial pairs a strong regional commitment to innovation and economic development with a global outlook. Its work supports applications ranging from climate resilience and food security to deforestation monitoring, infrastructure planning, and environmental compliance. *“We are thrilled at the potential with this new organization, which is thoroughly designed for this moment,”* said Jennifer Marcus, Vice President of Strategic Innovation Programs, Taylor Geospatial. *“By bringing together deep academic research, industry expertise, and entrepreneurial pathways, Taylor Geospatial is uniquely positioned to turn GeoAI breakthroughs into digital public goods. Our focus on applying AI to satellite imagery at scale will help address critical global challenges while building a world-class center for geospatial innovation in St. Louis.”* The launch includes a new visual identity, logo, and redesigned website that reflect Taylor Geospatial’s unified strategy and role within the global GeoAI ecosystem. From this point forward, all programs, partnerships, and initiatives will operate under the Taylor Geospatial name. For more information, visit [taylorgeospatial.org](https://taylorgeospatial.org). --- ##### **About Taylor Geospatial** Headquartered in St. Louis, Taylor Geospatial is a nonprofit organization focused on advancing geospatial artificial intelligence (GeoAI) for global public benefit. The organization partners with academic researchers and industry leaders to translate breakthrough geospatial research into accessible tools, datasets, and shared infrastructure. Through this work, Taylor Geospatial supports the development and deployment of digital public goods that help innovators and communities address complex global challenges. **For more information, contact:** Allison Hawk | 314-458-7668 **For media:** [View the digital press kit](https://drive.google.com/drive/folders/1e2pAem8s-ARW_uo6uyr9Yju7zZ-eq3MK?usp=sharing) **Categories:** From Taylor Geospatial --- ### [Two new organizations aim to drive St. Louis’ geospatial ambitions](https://taylorgeospatial.org/news/two-new-organizations-aim-to-drive-st-louis-geospatial-ambitions/) **Published:** March 23, 2026 **Author:** taylorgiwp **Categories:** In the News --- ### [Taylor Geospatial Institute and Engine roll into a single nonprofit focused on GeoAI](https://taylorgeospatial.org/news/taylor-geospatial-institute-and-engine-roll-into-a-single-nonprofit-focused-on-geoai/) **Published:** March 5, 2026 **Author:** taylorgiwp **Categories:** In the News --- ### [Taylor Geospatial merges 2 nonprofits to speed geospatial AI tools from lab to market](https://taylorgeospatial.org/news/taylor-geospatial-merges-2-nonprofits-to-speed-geospatial-ai-tools-from-lab-to-market/) **Published:** March 5, 2026 **Author:** taylorgiwp **Categories:** In the News --- ## Pages ### [Home](https://taylorgeospatial.org/) **Published:** February 25, 2026 **Author:** taylorgiwp **Content:** ## Our Vision Taylor Geospatial is a new nonprofit organization focused on democratizing the power of GeoAI through global partnerships while strengthening innovation capacity in the St. Louis region. --- ![A simple black icon of a stylized atom with an orbiting path and a four-pointed star shape above it on a white background.](https://taylorgeospatial.org/wp-content/uploads/2026/02/impact-icon.svg)### Ideas to Impact Bridging the gap between breakthrough academic research and real-world industry deployment, to accelerate the transition from idea to impact. ![Two dark brown pretzels are overlapping, forming a linked pattern on a white background.](https://taylorgeospatial.org/wp-content/uploads/2026/02/scale.svg)### Collaboration for Scale Actively co-developing and co-investing with leading GeoAI contributors–satellite operators, cloud providers, foundation model developers, and prominent research labs. ![A black square with a thick, curved arrow extending upward and to the right from its upper right corner, resembling an open in new window or external link icon.](https://taylorgeospatial.org/wp-content/uploads/2026/02/pipelines.svg)### Open Pipelines We build an ecosystem to democratize access to global scale labels, models, embeddings, and datasets that enable researchers, entrepreneurs, companies, and governments to create geospatial insights and accelerate commercialization. ![A view of a farm field with neat rows of green crops growing, surrounded by hills and a clear sky in the background. Some farm structures are visible in the distance.](https://taylorgeospatial.org/wp-content/uploads/2026/05/crops-small.jpg)May 27, 2026Winning Projects Announced: Geospatial Innovation for Food Security Challenge [Read the Release](https://taylorgeospatial.org/gifs-awardees/) --- ## Initiatives Taylor Geospatial unifies experts in academia, industry, and technology to produce tangible outcomes for the digital public good. Initiative### Fields of the World ![Aerial view of intersecting roads dividing green fields and a plowed farmland, with scattered trees and a small building casting long shadows in the sunlight.](https://taylorgeospatial.org/wp-content/uploads/2026/02/aerial-view-350x350.jpg) Fields of The World is a groundbreaking program designed as an “innovation bridge” between academic research and industry. In its first phase, FTW released the largest benchmark dataset for training models to infer field boundaries from satellite imagery. In its second phase, FTW released an assessment of over 70 geospatial AI models and their comparative performance, in addition to releasing a high performing model architecture for inferring global field boundaries. By making this ecosystem openly available, we’re empowering researchers, NGOs, and governments to better understand and manage agricultural systems worldwide. [Learn More](https://fieldsofthe.world/) ![A black globe icon showing a grid of horizontal and vertical lines representing latitude and longitude on a white background.](https://taylorgeospatial.org/wp-content/uploads/2026/02/icon-earth.svg)Global CoverageComprehensive fields boundary data across multiple continents ![An icon showing a globe on the left and two stacked rectangles with rounded corners on the right, enclosed partly by brackets, symbolizing global access or network connectivity.](https://taylorgeospatial.org/wp-content/uploads/2026/02/icon-dataset.svg)Open DatasetFreely accessible to researchers and innovators worldwide ![A black outlined microchip icon with a diamond shape in the center, set against a light background.](https://taylorgeospatial.org/wp-content/uploads/2026/02/icon-ai-powered.svg)AI-PoweredCreated using advanced machine learning and satellite imagery Initiative### Geospatial Innovation for Food Security ![A person wearing a hat works in a lush green rice field, surrounded by trees, with tall mountains and a blue sky in the background.](https://taylorgeospatial.org/wp-content/uploads/2026/02/field-person-350x350.jpg) Taylor Geospatial convened a transdisciplinary community of researchers, innovators, and implementers to identify critical challenges in food security and sustainable agriculture that could be addressed through geospatial innovation. We launched the Geospatial Innovation for Food Security Challenge to support advanced research to address three of these challenges: enabling agri-food supply chain resilience, informing crop shifting, and increasing nitrogen use efficiency. [Learn more](https://taylorgeospatial.org/innovation/gifs/) ![A simple black and white icon of an open hand facing up with a small robot head hovering above the palm.](https://taylorgeospatial.org/wp-content/uploads/2026/03/tools-icon.svg)Research-to-Action TeamsCollaboration that bridges the gap from discovery to deployment ![Three black and white outlined cubes, arranged in a triangular formation, with two on the bottom and one on top.](https://taylorgeospatial.org/wp-content/uploads/2026/03/building-blocks-icon.svg)Open Building BlocksCreating foundational tech to advance everyone’s work ![A black outlined microchip icon with a diamond shape in the center, set against a light background.](https://taylorgeospatial.org/wp-content/uploads/2026/02/icon-ai-powered.svg)GeoAI for GoodLeveraging AI to address global challenges ## Our Team ![Headshot of Elliott Kellner](https://taylorgeospatial.org/wp-content/uploads/2026/02/Elliott-Kellner.jpg) Elliott Kellner President [](https://www.linkedin.com/in/elliott-kellner-2021a6201/) ![Headshot of Jennifer Marcus](https://taylorgeospatial.org/wp-content/uploads/2026/03/Jennifer-Marcus2.jpg) Jennifer Marcus Vice President, Strategic Innovation Programs [](https://www.linkedin.com/in/jennifer-marcus-b559091/) ![A woman with straight, shoulder-length brown hair smiles at the camera. She is wearing a gray top and a gold necklace, standing against a plain, light-colored background.](https://taylorgeospatial.org/wp-content/uploads/2026/02/Allison-Braun.jpg) Allison Braun Strategic Communications Director [](https://www.linkedin.com/in/allisonrbraun/) ![Headshot of Isaac Corley](https://taylorgeospatial.org/wp-content/uploads/2026/04/Isaac-Corley.jpg) Isaac Corley AI/ML Research Director [](https://www.linkedin.com/in/isaaccorley/) ![Headshot of Aviskar Giri](https://taylorgeospatial.org/wp-content/uploads/2026/02/Aviskar-Giri.jpg) Aviskar Giri Geospatial Data Scientist [](https://www.linkedin.com/in/aviskar-giri-a626a2155/) ![Headshot of James Haithcoat](https://taylorgeospatial.org/wp-content/uploads/2026/02/James-Haithcoat.jpg) James Haithcoat Technical Project Manager [](https://www.linkedin.com/in/james-haithcoat/) ![Headshot of Hannah Kerner](https://taylorgeospatial.org/wp-content/uploads/2026/02/Hannah-Kerner.jpg) Hannah Kerner Research Advisor [](https://www.linkedin.com/in/hannahkerner/) ![Headshot of Henning Lohse-Busch](https://taylorgeospatial.org/wp-content/uploads/2026/03/Henning-Lohse-Busch.jpg) Henning Lohse-Busch Operations Lead [](https://www.linkedin.com/in/henning-lohse-busch/) ![Headshot of Christopher Michael](https://taylorgeospatial.org/wp-content/uploads/2026/03/Christopher-Michael.jpg) Christopher Michael Community Affairs & Outreach Manager [](https://www.linkedin.com/in/christopher-michael-16b6a89/) ![Headshot of Rachel Opitz](https://taylorgeospatial.org/wp-content/uploads/2026/02/Rachel-Opitz.jpg) Rachel Opitz Program Manager, Geospatial Innovation for Food Security [](https://www.linkedin.com/in/rachel-opitz-a8a85a7b/) ![Headshot of Rhiannan Price](https://taylorgeospatial.org/wp-content/uploads/2026/02/Rhiannan-Price.jpg) Rhiannan Price Humanitarian Fellow in Residence [](https://www.linkedin.com/in/rhiannan-price/) ![Nick Reinke headshot](https://taylorgeospatial.org/wp-content/uploads/2026/04/Nick-Reinke.png) Nick Reinke Commercialization Director [](https://www.linkedin.com/in/nickreinke/) ## Join Our Community Interested in collaborating or partnering on open geospatial research initiatives, or learning more about our work in advancing GeoAI for the digital public good? Whether you’re a researcher, service provider, organization, or community leader, we’d love to hear from you and explore how we can work together to accelerate deployment and development of breakthrough geospatial solutions. --- ### [Contact](https://taylorgeospatial.org/contact/) **Published:** June 9, 2026 **Author:** Allison Braun **Content:** To receive updates about Taylor Geospatial, subscribe to [our newsletter](https://mailchi.mp/4d242ea79d94/taylor-geospatial-email-signup). For media inquiries, please contact our Director of Strategic Communications, Allison Braun at . For all other messages, please fill out the form below or email . --- Organization / Company(Required) Your Name(Required) First Last Email(Required) Subject(Required) Cover Letter & CVMax. file size: 128 MB. Message Submit --- ### [Innovation](https://taylorgeospatial.org/innovation/) **Published:** May 26, 2026 **Author:** Allison Braun **Content:** [![A wide view of a farm field with rows of green crops and tilled soil, surrounded by rolling hills and trees under a clear blue sky.](https://taylorgeospatial.org/wp-content/uploads/2026/05/crops-960x541.jpg)](https://taylorgeospatial.org/initiatives/gifs)### [Geospatial Innovation for Food Security ](https://taylorgeospatial.org/initiatives/gifs) The Geospatial Innovation for Food Security (GIFS) Challenge promotes advanced research in three critical food systems problem spaces: enabling agri-food supply chain resilience, informing crop shifting, and increasing nitrogen use efficiency. [![Aerial view of a landscape with patchwork fields in various shades of green and brown, intersected by a winding dirt road, with hills and forests in the background under a partly cloudy sky.](https://taylorgeospatial.org/wp-content/uploads/2026/05/fields-landscape-960x686.jpg)](https://taylorgeospatial.org/agricultural-field-boundaries-mapped-globally-for-the-first-time/)### [Fields of The World](https://taylorgeospatial.org/agricultural-field-boundaries-mapped-globally-for-the-first-time/) Fields of The World is an open ecosystem for agricultural field boundary detection — combining a global benchmark dataset, baseline ML models, inference tools, and web applications. ![Aerial view of a densely packed residential area with red-roofed houses, narrow streets, and patches of green trees scattered throughout the neighborhood.](https://taylorgeospatial.org/wp-content/uploads/2026/05/aerial-town-2-960x720.jpg)### Coming Soon: Features of the World Through our Fields of The World effort, we proved that we can map agricultural field boundaries globally. Next we plan to explore how the FTW ecosystem can be adapted to map additional infrastructure at global scale. ![A network graph with interconnected nodes of varying sizes and colors (blue, red, orange), connected by thin lines on a dark background, illustrating relationships or connections between entities.](https://taylorgeospatial.org/wp-content/uploads/2026/05/citation-network-960x698.png)### Coming Soon: Benchmarking Geospatial foundation models are proliferating faster than tools to evaluate their performance. We’re developing a coalition to establish a framework that will enable practitioners to choose the right model for their mission. --- ### [GIFS](https://taylorgeospatial.org/innovation/gifs/) **Published:** June 22, 2026 **Author:** Allison Braun **Content:** Taylor Geospatial launched the Geospatial Innovation for Food Security (GIFS) Challenge to promote advanced research in three critical food systems problem spaces > 1. Enabling agri-food supply chain resilience > 2. Informing crop shifting > 3. Increasing nitrogen use efficiency. All outputs will be made publicly available, advancing our collective ability to address critical global challenges using AI-driven geospatial technology. We are proud to support a strong cohort of [flagship projects](#flagship) and [pilot projects](#pilot) that are developing new methods and tools and sharing their outcomes and learnings with the community. Project pages will be updated regularly as research progresses. Check back for updates on new data, models, code, and research outcomes. [Read the press release](https://taylorgeospatial.org/news/transforming-global-food-systems-new-geospatial-innovations-aim-to-combat-hunger-and-support-agricultural-stability/) --- ## Flagship Projects Fully funded by Taylor Geospatial for 18 months to progress research and translate it into practice, resulting in advanced prototypes or working tools. Outputs will be shared openly, advancing our collective ability to address critical global challenges using AI-driven geospatial technology. [![A person wearing a scarf and jacket bends over a cart filled with apples at an outdoor market, with other people and produce visible in the background under a clear blue sky.](https://taylorgeospatial.org/wp-content/uploads/2026/05/kabul-market-960x640.jpg)](https://taylorgeospatial.org/initiatives/gifs/af-pulse/)### [Early Warning Systems for Hunger & Malnutrition](https://taylorgeospatial.org/initiatives/gifs/af-pulse/) The World Food Programme, in partnership with the REACH Initiative, is enhancing Afghanistan’s PULSE platform (Platform for Understanding Local Shocks and Emergencies). The system tracks hazards affecting food access and supply routes, helping responders plan in environments where ground-level data is often incomplete. [![Woman kneeling by a cultivated bed, planting green onion seedlings in dark soil next to a stone wall.](https://taylorgeospatial.org/wp-content/uploads/2026/05/pexels-muhammeddiler-36407729-960x636.jpg)](https://taylorgeospatial.org/?page_id=488u0026preview=true)### [Predicting Food System Instability](https://taylorgeospatial.org/?page_id=488u0026preview=true) A collaboration between Arizona State University, the University of Maryland, and Washington University in St. Louis—alongside partners NASA Harvest, NASA Goddard, and FEWS NET—is developing a GeoAI capability to identify early signals of instability in food systems. [![Close-up of young green corn plants growing in rows on dark soil, with water droplets on the leaves and a blurred field in the background.](https://taylorgeospatial.org/wp-content/uploads/2026/05/young-corn-in-soil-960x640.jpg)](https://taylorgeospatial.org/initiatives/gifs/water-first)### [Water-First Nitrogen Management](https://taylorgeospatial.org/initiatives/gifs/water-first) Led by researchers at the University of Missouri in partnership with the MU Extension, this project focuses on “water first” GeoAI model development to improve nitrogen application decisions. By combining satellite imagery and machine learning, the team maps plant-available soil water at sub-field scales. ## Key characteristics - Actively engage with the ongoing emergence of geospatial artificial intelligence (GeoAI) and its implications for geospatial technologies, methods, and models. - Advance geospatial technologies, methods and models at Technology Readiness Levels (TRLs) 3-7. - Do not rely on proprietary data or proprietary software for essential capabilities. - Teams must combine technological and methodological innovation with domain expertise and practical knowledge of the challenges of implementing new ways of working in food and agriculture. - Teams must include at least one research and one implementing partner organization. --- ## Pilot Projects Exploring new approaches and creating building blocks for research-to-practice translation over 12-to-18 months. Supported by Taylor Geospatial and [Amazon Web Services](https://aws.amazon.com/about-aws/our-impact/). ![Icon of a grid/map with a blue water drop at the center on a dark red satellite-map background (likely a water-focused app button)](https://taylorgeospatial.org/wp-content/uploads/2026/06/AcreN.png)### AcreN: A Hybrid GeoAI Framework for Monitoring, Modeling, and Verification of Agricultural NUE Prototyping a hybrid differentiable GeoAI framework for sub-field to regional monitoring of nitrogen losses and NUE under diverse management practices. Led by Yanghui Kang, Yongfa You at Virginia Tech, Mingwei Yuan at IntelinAir, and Dapeng Feng at the University of Texas at Austin ![A stylized white seedling inside a blue circular arrow is centered on a reddish-brown, mountainous terrain background viewed from above.](https://taylorgeospatial.org/wp-content/uploads/2026/06/Evidence-to-Local-Farms.png)### Bringing Global Agricultural Evidence to Local Farms: A GeoAI Approach for Strategic Crop Shifting Decisions IPCC-framework climate vulnerability mapping and geospatial foundation models guiding crop-shifting decisions across Kenyan farming systems. Led by Ritvik Sahajpal at University of Maryland and NASA Harvest & Oliver Kipkogei at IGAD Climate Prediction and Applications Centre (ICPAC). ![Search icon overlay on a red satellite/map background, indicating zoom or find functionality](https://taylorgeospatial.org/wp-content/uploads/2026/06/Supply-Chain-Visibility.png)### Building Local Agrifood System Resilience and Food Security through Increased Supply Chain Visibility GeoAI graph neural network modeling supply chain relationships with MarketMaker to strengthen Alabama’s local food systems. Led by Nicholas Magliocca at the University of Alabama and Sara Gonzalez at Auburn University. ![](https://taylorgeospatial.org/wp-content/uploads/2026/06/Climate-Resilient-Cashew-Systems.png)### Climate-Resilient Cashew Systems: GeoAI and Crop Modeling for Northern Ghana Mapping cashew extent and building satellite-driven models to forecast yield and production across Northern Ghana. Led by Foster Mensah at the University of Ghana, Center for Remote Sensing and Geographic Information Services (CERSGIS). ![Brand logo featuring a blue water droplet above a white semicircular bowl on a dark red textured background, suggesting hydration or water-related services.](https://taylorgeospatial.org/wp-content/uploads/2026/06/Pixels-to-Impact.png)### From Pixels to Impact: GeoAI for Nitrogen Efficiency and Food Security in Burma Field boundary delineation, crop-type identification, and productivity analytics to improve yields and reduce nitrogen use on smallholder farms. Led by Ate Poortinga at the Spatial Informatics Group, LLC. ![Two small plant sprouts with circular arrows, illustrating a plant growth cycle nearby a maroon background.](https://taylorgeospatial.org/wp-content/uploads/2026/06/Opportunity-Crops.png)### GEO-AI Driven Crop Shifting Strategy for Opportunity Crops GeoAI-driven identification of opportunity crops and optimal shifting strategies across Zambia and Kenya. Led by Anastasia Wahome, Benson Kenduiywo and Majambo Jarumani at the International Center for Tropical Agriculture (CIAT). ![Circular dotted ring with three hexagons and a central dot over a red canyon landscape. (logo/overlay)](https://taylorgeospatial.org/wp-content/uploads/2026/06/GLO-FORCE-Blockchain-AI-Supply-Chain.png)### GLO-FORCE: Blockchain & AI for Optimizing Food Supply Chain Resilience and Security Blockchain smart contracts and responsive rerouting architecture treating food supply chains as critical infrastructure. Led by Vijay Anand at Kennesaw State University, Kate Trout at the University of Missouri, KC Kroll at EarthDaily, Haitao Li at University of Missouri St. Louis (UMSL), Jake Hawes at University of Wyoming, and Carlton Adams at Operation Food Search. ![](https://taylorgeospatial.org/wp-content/uploads/2026/06/Crop-Suitability-Adaptation-Atlas.png)### Global Crop Suitability and Adaptation Atlas (GSTFM) Pre-training a geospatial spectral-temporal foundation model that captures agricultural factors for dynamic, seasonal crop suitability predictions. Led by Praveen Pankajakshan at UrbanKissan. ![](https://taylorgeospatial.org/wp-content/uploads/2026/06/Global-Digital-Food-Twin.png)### Global Digital Food Twin for Supply Chain Resiliency Integrating Earth observations with trade, infrastructure, and consumption data to simulate how shocks propagate through the global food network. Led by Mikel Marron at Earth Genome and Zia Mehrabi at Better Planet Laboratory. ![Blue circular target icon with a white water drop in the center over a red-tinted satellite map background.](https://taylorgeospatial.org/wp-content/uploads/2026/06/Model-Data-Integration-NUE.png)### Model–Data Integration for Scalable Nitrogen Use Efficiency Monitoring Harmonizing farm surveys with Sentinel-2 imagery and soil property data into a multimodal NUE model for the Chesapeake Bay. Led by Xin Zhang at University of Maryland Center for Environmental Science (UMCES) and the Global Nitrogen Innovation Center for Clean Energy and the Environment (NICEE) and Hai Lan at the University of South Alabama. --- ## About Taylor Geospatial Taylor Geospatial is committed to bridging the gap between breakthrough academic research and real-world industry deployment to accelerate the transition from idea to impact. We are building an ecosystem to democratize access to global scale labels, models, embeddings, and datasets that enable researchers, entrepreneurs, companies, and governments to create geospatial insights and accelerate pathways to impact. --- ### [Predicting Food System Instability](https://taylorgeospatial.org/innovation/gifs/predicting-food-system-instability/) **Published:** May 26, 2026 **Author:** Allison Braun **Content:** Armed conflict doesn’t just destroy lives and cities—it also quietly undermines food production, trade, and access in ways that are difficult to track in real time. To help humanitarian agencies respond more effectively, this project develops a new way to monitor how wars disrupt agriculture and critical infrastructure as those changes are happening. Our approach uses advances in artificial intelligence and satellite imagery to turn frequent Earth‑observation data into clear, timely maps of crop conditions and transportation access. By “teaching” AI models to recognize seasonal changes in farming and infrastructure, and to respond to simple, language‑based questions, we can generate flexible maps that highlight where crops are being planted or abandoned, where harvests are delayed or accelerated, and where roads, ports, or airfields may no longer be usable. These maps can also reveal shifts in what farmers choose to grow—for example, moving from staple food crops to faster‑maturing or illicit alternatives under conflict pressure. ![Three pairs of satellite images show seasonal changes in a river and surrounding landscape, labeled JAN-MAR, JAN-JUN, and JAN-DEC, with varying colors indicating vegetation, water, and urban areas.](https://taylorgeospatial.org/wp-content/uploads/2026/05/Predicting-Food-System-Instability_Research-Graphic.png) Examples of quarterly, semi-annual, and annual satellite-based embeddings from Al Jazirah, Sudan, in 2024, that allow users to identify critical food security signals, such as burn scars, field preparation, and harvest activity, using natural language. Credit: Arizona State University. Embeddings generated with OlmoEarth.We combine these satellite‑based insights with openly available, regularly updated data on food prices, trade flows, population displacement, conflict events, and infrastructure networks. Together, these data streams produce practical indicators that can guide food‑assistance planning, supply‑chain routing, and early warning efforts. We will test and refine this capability using real‑world conflict situations in places such as Sudan, Ukraine, Syria, and Haiti, working alongside established monitoring programs. After demonstrating its accuracy and usefulness, we aim to integrate this technology directly into operational systems used by agencies that track food security and humanitarian risk worldwide. ## Outcomes This project will produce open source sample data, code, models, and scientific publications documenting the methodology developed. Updates on these products will be added as the project progresses. ## Team - Dr. Ana M. Tárano (PI) — Arizona State University - Dr. Hannah Kerner — Arizona State University - Dr. Nathan Jacobs — Washington University in St. Louis - Dr. Catherine Nakalembe — University of Maryland College Park - Dr. Kiersten Johnson — FEWS NET (Famine Early Warning Systems Network), U.S. Department of State - Dr. Karyn Tabor — NASA Goddard Space Flight Center - Dr. Amy McNally — NASA Goddard Space Flight Center - Dr. Inbal Becker-Reshef — NASA Harvest, University of Maryland College Park (also Managing Director, Microsoft AI for Good Lab) - Dr. Yehuda Magid — Independent Consultant (former Conflict and Peacebuilding Advisor to USAID) [Geospatial Innovation for Food Security Home](https://taylorgeospatial.org/initiatives/gifs/ "GIFS")[Water-First Nitrogen Management](https://taylorgeospatial.org/initiatives/gifs/water-first/ "Water-First Agricultural Nitrogen Management")[Early Warning Systems for Hunger & Malnutrition](https://taylorgeospatial.org/initiatives/gifs/af-pulse/ "Early Warning Systems for Hunger & Malnutrition") --- ### [Water-First Agricultural Nitrogen Management](https://taylorgeospatial.org/innovation/gifs/water-first/) **Published:** May 27, 2026 **Author:** Allison Braun **Content:** ##### Improving Agricultural N Management with GeoAI-based Plant-Available Soil Water Capacity Mapping Modern agriculture has access to sophisticated machinery, sensors, and decision‑support tools, yet farmers still struggle to apply nitrogen fertilizer efficiently and at the right time. A key reason is that most recommendation systems overlook a fundamental constraint on crop growth: how much water the soil can actually store and supply to plants. Without understanding soil water availability, even the best nitrogen advice can miss the mark. This project starts from a simple but powerful idea: improving nitrogen management first requires understanding water. In rain‑fed farming systems, crop yields are determined by the balance between sunlight and plant‑available water. Periods of water stress—especially during critical growth stages—can sharply reduce yields and limit the crop’s ability to use applied nitrogen effectively. To address this gap, this project is developing a new, widely applicable way to model how soil water‑holding capacity varies within individual farm fields. Using satellite data collected over many years, the model will identifies consistent patterns of crop water stress that reveal underlying differences in soil properties. Information from multispectral and thermal imagery, radar data, evapotranspiration estimates, topography, and existing soil surveys will be combined to create detailed maps that divide fields into management zones based on their ability to store and supply water. ![Aerial vegetation index maps show varying plant health in a field, with colors from blue (low) to red/yellow (high). Main map is labeled UAV-M30T-AUG 14; two smaller maps show PlanetScope-NDRE-AUG 15 and PlanetScope-NDRE-AUG 30.](https://taylorgeospatial.org/wp-content/uploads/2026/05/Focus-on-Water-First_Graphic-960x386.png) *Comparison of PlanetScope NDRE map (left) with thermal imagery (right) captured using DJI Matrice 30T Thermal UAV. This graphic shows how soil temperature (left) and proxies for overall crop nitrogen status and plant health (small images, right) vary across a field. Temperature and moisture work together to control how plants take up nitrogen, a key nutrient for growth. While measuring temperature is possible, measuring plant available water is challenging The ‘Focus on Water First’ project is developing an improved GeoAI method for modelling plant available water holding capacity across agricultural fields. This method will enable agronomists, university extensions and other advisors to improve the accuracy and detail of their nitrogen management recommendations to farmers.*These zones can then be translated into quantitative estimates of soil water‑holding capacity using crop growth models that simulate how maize responds to weather, soil conditions, and management. By calibrating the models with real farm yield data and satellite observations, the system can estimate how much water is available to crops in each zone and predict realistic yield potential under water‑limited conditions. The calibrated models can then be used to test different nitrogen rates, producing recommendations that are better matched to each part of the field. To make this work scalable and practical, the project is also building a modern data infrastructure that brings together weather records, satellite products, soil information, and yield histories into a unified, cloud‑based geospatial system. This platform is designed to work with existing agricultural tools and standards, allowing the results to be easily shared and reused. The initial demonstration focuses on Missouri and Iowa’s claypan soils, using hundreds of on‑farm trials to validate the approach. While tested in this region, the method is designed to work anywhere rain‑fed crops are grown. By providing reliable, field‑scale estimates of plant‑available soil water, this project will help close a long‑standing gap between nitrogen science and on‑farm practice—supporting higher yields, better nitrogen efficiency, and reduced environmental losses. ## Outcomes This project will produce open source sample data for claypan regions in Missouri and Iowa, code supporting the modeling of plant-available soil water capacity, updated and optimized DSSAT software, and scientific publications detailing the methodology developed. Updates on these products will be added as the project progresses. ## Team - **Dr. Timothy Haithcoat** (PI) — University of Missouri - **Dr. John Lory** — University of Missouri Agricultural Extension - **Dr. Michael Sunde** — University of Missouri - **Dr. Andre Reis** — University of Missouri Agricultural Extension - **Dr. Hatef Dastour** — University of Missouri - **Dr. Zachary Leasor** — University of Missouri Agricultural Extension - **Dr. Jasmine Neupane** — University of Missouri [Geospatial Innovation for Food Security Home](https://taylorgeospatial.org/initiatives/gifs/ "GIFS")[Predicting Food System Instability](https://taylorgeospatial.org/initiatives/gifs/predicting-food-system-instability/ "Predicting Food System Instability")[Early Warning Systems for Hunger & Malnutrition](https://taylorgeospatial.org/initiatives/gifs/af-pulse/ "Early Warning Systems for Hunger & Malnutrition") --- ### [Early Warning Systems for Hunger & Malnutrition](https://taylorgeospatial.org/innovation/gifs/af-pulse/) **Published:** May 26, 2026 **Author:** Allison Braun **Content:** Afghanistan is facing multiple, overlapping crises that threaten how food moves from producers to people who need it. Climate extremes such as floods and droughts, ongoing conflict, and unstable markets are disrupting roads, bridges, and supply corridors. As a result, millions of Afghans struggle to access enough food, while humanitarian organizations must operate with shrinking resources and limited on‑the‑ground access. This project focuses on enhancing the toolkit used by aid agencies to anticipate and respond to problems before they become full‑scale emergencies. Led by the [United Nations World Food Programme](https://www.wfp.org/countries/afghanistan) and the [REACH Initiative](https://www.impact-initiatives.org/what-we-do/reach/), it builds on an existing platform called AF‑PULSE, a real‑time mapping system that tracks risks to food supply chains across Afghanistan. The next generation of AF‑PULSE will bring together satellite data, weather forecasts, field observations, and machine‑learning tools to detect early warning signs of disruption. ![A map of Afghanistan and surrounding countries shows regions shaded in red and blue, indicating varying levels of food insecurity, natural hazards, and nutrition stressors. Title: Afghanistan Pulse.](https://taylorgeospatial.org/wp-content/uploads/2026/05/AF-PULSE_Graphic-960x461.png) *The AF-PULSE platform is transitioning from models with limited real-time data to a GeoAI-enhanced system that integrates multi-source field and satellite data for improved food aid delivery.*A key innovation is the use of GeoAI to enable the system to interpret information coming directly from communities, associate this information with specific locations, and verify the information against other data about those places. Using this system, field staff and local reporters can send short text messages and photos using mobile data‑collection tools. Artificial‑intelligence models will automatically extract key information—such as reports of flooded roads or damaged bridges—in multiple languages, and analyze images to identify hazards like debris or standing water. These reports can then be checked against satellite and meteorological data, assigned confidence scores, and added to live operational maps. Over time, the system will learns from each verified report, steadily improving its ability to recognize real disruptions and filter out noise. The result is a continuously improving, community‑informed early‑warning system that again leverages GeoAI models to simulate hazard-to-impact pathways and show where food supply routes are at risk and predict where alternative routes may still be open. ## Outcomes This project will produce an updated operational prototype of the AF-PULSE Platform and a set of open source resources to support the humanitarian community in building on this work. Planned open source resources include sample data schema, open-source Python scripts for data ingestion, analysis, and API integration, technical documentation of methods, models, and data standards, and training materials. The project will also make research brief of findings conflict and market disruption pathways publicly available. Updates on these products will be added as the project progresses. ## Team - Ms. Gabriela Luz — World Food Programme - Mr. Moataz Elmasry — World Food Programme - Mr. Michael Manalili — World Food Programme - Ms. Wahida Azizi — World Food Programme - Mr. Berry Swana — World Food Programme - Mr. Imran Ahmedani Khan — World Food Programme - Mr. Homayoon Yousofi — World Food Programme - Mr. Hikmatullah Saqib — World Food Programme - Ms. Meike Palinkas — REACH Initiative - Mr. William Paja — REACH Initiative - Mr. Jawad Keshawarz — REACH Initiative [Geospatial Innovation for Food Security Home](https://taylorgeospatial.org/initiatives/gifs/ "GIFS")[Predicting Food System Instability](https://taylorgeospatial.org/initiatives/gifs/predicting-food-system-instability/ "Predicting Food System Instability")[Water-First Nitrogen Management](https://taylorgeospatial.org/initiatives/gifs/water-first/ "Water-First Agricultural Nitrogen Management") --- ### [Allison Testing](https://taylorgeospatial.org/allison-testing/) **Published:** June 18, 2026 **Author:** Allison Braun **Content:** ## Notes - No option to… - Have text only (must have icon or image) - Lock row height - Make corners square or rounded - Turn off animation on hover state; seems like too much for a non-clickable element ### Heading --- ### [Ethical Statement](https://taylorgeospatial.org/ethical-statement/) **Published:** April 6, 2026 **Author:** Allison Braun **Content:** Taylor Geospatial approaches AI with the understanding that powerful geospatial technologies carry both immense opportunity and responsibility. Our approach to ethical AI centers on ensuring that geospatial intelligence is developed and deployed in ways that are transparent, accountable, and aligned with the public good. This means prioritizing responsible data stewardship, safeguarding privacy and human rights, and designing systems that augment human expertise rather than replace it. By combining rigorous scientific research with cross-sector collaboration among academia, industry, and the public sector, Taylor Geospatial works to advance geospatial AI that is trustworthy, inclusive, and capable of addressing complex societal challenges from climate resilience to humanitarian response. Here are just a few ways we’re operationalizing ethical AI. Moving forward, we’re doing even more, including guidance for sensitive use cases and feedback loops. **Open Access for Public Good Uses:** With an open-source approach, we can better ensure that the benefits reach those who need them most. If you’re a government estimating crop exposure to drought or a researcher tracking land-use change or a start-up with parametric insurance models, you can benefit from these digital public goods. **Transparent Training Data and Methods**: We publish clear documentation about how the resources we share are generated. This helps governments, NGOs, and researchers understand when the data should and should not be used for policy or programming. **Equitable Coverage in Training Data**: Most AI models perform best in wealthier regions where data is abundant. Ethical deployment means deliberately prioritizing underserved geographies. This helps ensure that resources do not unintentionally favor commercial interests over humanitarian ones, for example. ---