Taylor Geospatial launched the Geospatial Innovation for Food Security (GIFS) Challenge to promote advanced research in three critical food systems problem spaces
- Enabling agri-food supply chain resilience
- Informing crop shifting
- 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 and pilot projects 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.
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.
Early Warning Systems for Hunger & Malnutrition
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.
Predicting Food System Instability
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.
Water-First Nitrogen Management
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.
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
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).
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.
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).
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.
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).
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.
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.
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.
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.