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Geospatial Innovation for Food Security

Unlocking AI-driven geospatial breakthroughs for sustainable agriculture and food security. Applying earth observation, machine learning, and spatial analytics to improve global agri-food systems resilience.

Rows of crops grow in neat lines on a farm, with some green plants and some bare soil. Rolling hills and a clear blue sky are visible in the background.

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, and increasing nitrogen use efficiency.

We are proud to support a strong cohort of projects 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.

Research Partners
Amazon Web Services
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Read the press release

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.

Early Warning Signs 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.

Woman kneeling by a cultivated bed, planting green onion seedlings in dark soil next to a stone wall.

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.

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.

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 Project Characteristics

01
Research

Teams must include at least one research and one implementing partner organization.

02
Advancing

Advance geospatial technologies, methods and models at Technology Readiness Levels (TRLs) 3-7.

03
No Proprietary Data

This project does not rely on proprietary data or proprietary software for essential capabilities.

04
Innovation

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.

05
Engaging

This project actively engages with the ongoing emergence of geospatial artificial intelligence (GeoAI) and its implications for geospatial technologies, methods, and models.

Pilot Projects

Exploring new approaches and creating building blocks for research-to-practice translation over 12-to-18 months.

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.

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Satellites for Peace: Why the Vatican Cares About Earth Observation

International experts and humanitarian leaders recently convened at a Vatican workshop to explore how democratizing access to satellite imagery can create a transparent, community-governed public infrastructure for global humanitarian efforts.
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June 25, 2026

Hannah Kerner Named U.S. Nominee for the APEC ASPIRE Prize

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Co-investment with AWS expands GIFS program

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AI project aims to help humanitarian organizations identify food security risks in conflict zones

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Mapping Every Field on Earth

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Convening a National Roadmap on Agricultural Nitrous Oxide MMRV Research

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.

Other Inititatives

Aerial view of a rural landscape with colorful patchwork fields, a winding road, scattered trees, and distant hills under a partly cloudy sky.

Fields of the World

Expanding the world’s first open ecosystem for global agricultural field boundary detection from satellite imagery — combining a global benchmark dataset, baseline ML models, inference tools, and web applications.

Aerial view of a densely packed neighborhood with red-roofed houses, green trees, and narrow roads winding through the area.

Features of the World

Applying the field-boundary approach to a broader set of physical features visible from space — training models capable of detecting roads, buildings, solar panels, trees, and more at global scale.