Geospatial Foundation Models: Algorithms and Applications

Tutorial Description: Foundation models are deep learning models trained on massive datasets and high-end computing resources. Recent advances have enabled them to perform a broad range of general tasks, including language processing, summarization, question answering, code generation, problem-solving, and reasoning. Geospatial foundation models are specifically trained on large-scale geospatial and temporal data. While general-purpose foundation models have demonstrated their capabilities in numerous popular applications, such as natural language generation, question answering, and text summarization, applications of geospatial foundation models are just beginning to emerge. This tutorial will first summarize recent advancements in geospatial foundation models and then describe their various applications.

Sample Papers:

• R. Bommasani et al. “On the opportunities and risks of foundation models.” (https://arxiv.org/abs/2108.07258).
• D. Szwarcman et al. “Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications.” (https://arxiv.org/abs/2412.02732).