Geospatial Foundation Model

Geospatial foundation models (GFMs) are large, pre-trained AI models designed to improve the efficiency and generalizability of geospatial image analysis. Current research focuses on developing GFMs using architectures like transformers and graph neural networks, often incorporating self-supervised learning techniques to leverage vast amounts of unlabeled satellite and sensor data. These models aim to improve performance across diverse downstream tasks, such as flood mapping, biomass estimation, and traffic prediction, while reducing the need for extensive task-specific training data. The resulting advancements promise to significantly accelerate progress in various fields relying on geospatial data analysis.