This project aims to improve precision agriculture in California's San Joaquin Valley by developing a cross-modal geospatial learning toolkit. By leveraging Graph Neural Networks (GNNs) and integrating multimodal sensor data, the project seeks to create high-resolution, fine-grained maps of farmland, enhancing climate resilience and resource management in the region. The research will provide farmers with actionable insights to mitigate the impacts of severe climate events, thereby supporting sustainable agriculture in disadvantaged communities.
Cross-Modal Artificial Intelligence Toolkit for Augmented Geospatial Mapping
