Rapid advancements in Earth observation (EO) systems, combined with geospatial artificial intelligence (Geo-AI), large language models, foundational models, deep learning, and machine learning, are transforming how environmental data are analyzed and applied to address complex environmental challenges. Yet, a persistent gap remains between technical innovation and the capacity of scientists, practitioners, and decision-makers to effectively utilize these tools. This session focuses on bridging that gap through scalable capacity-building approaches that integrate EO data with applied Geo-AI methodologies. We highlight the role of structured knowledge-sharing mechanisms including open-access books, handbooks, training curricula, community-driven educational resources, and open-source software to democratize access to advanced analytical capabilities. We aim to foster collaboration in leveraging cutting-edge Geo-AI technologies for practical applications, emphasizing designed capacity-building and knowledge-sharing strategies for the geospatial community and  workforce. The session will showcase pathways for enabling broader participation in EO-driven decision-making and accelerating the translation of data into actionable insights.

• EP - Earth and Planetary Surface Processes
• GC - Global Environmental Change
• IN - Informatics
• SY - Science and Society