Primary Convener:
Biplov Bhandari, Earth Resources Technology Inc

Convener:
Yuchi Ma, University of Wisconsin Madison
Sherrie Wang, Stanford University
Ignacio Ciampitti, Purdue University

Early Career Convener:
Kshitiz Khanal, University of North Carolina at Chapel Hill

Chair:
Biplov Bhandari, Earth Resources Technology Inc
Yuchi Ma, University of Wisconsin Madison
Sherrie Wang, Stanford University
Ignacio Ciampitti, Purdue University

As environmental monitoring evolves from research prototypes to real-world systems, the need for scalable and operational GeoAI solutions is more urgent than ever. This session focuses on scientifically innovative machine learning (ML), deep learning (DL), and geospatial artificial intelligence (GeoAI) applications built for implementation at scale. We welcome contributions that bridge cutting-edge research and practical deployment—solutions that leverage Earth observations, foundational models, open data, cloud computing, high-performance computing, and cloud-native geospatial infrastructure to address pressing environmental challenges such as climate resilience, disaster response, sustainable agriculture, energy transition, and natural resource management. Emphasis will be placed on scalable and reproducible solutions adhering to open and FAIR data principles with applications in data-sparse or high-risk regions. This session brings together applied scientists, researchers, NGOs, and industry experts working to translate innovation into measurable environmental impact through responsible AI.

Index Terms
0402 Agricultural systems
1622 Earth system modeling
1640 Remote sensing
1942 Machine learning

Cross-Listed:
NH - Natural Hazards
IN - Informatics
EP - Earth and Planetary Surface Processes

Neighborhoods:
3. Earth Covering