Artificial Intelligence (AI) has fundamentally transformed how we address pressing environmental sustainability challenges from local to global. Today, satellite and airborne platforms deliver multi-modal Earth observation data, including multispectral, radar, hyperspectral, and LiDAR data, at unprecedented resolutions. In parallel, rapid advances in AI, ranging from specialized deep learning architectures to large-scale foundation models, offer scalable tools to translate these diverse observations into actionable scientific knowledge.

This session welcomes contributions spanning the entire pipeline from data to impact. Topics include, but are not limited to: constructing benchmark datasets, designing novel architectures, and deploying AI-led innovations for ecosystem monitoring, land-use/cover and urban dynamics, biodiversity and conservation, carbon and water cycle, precision agriculture, extreme weather forecasting, and global environmental changes.