How can foundation models be trained for diverse geospatial data types beyond SAR?

Foundation models can be trained for diverse geospatial data types beyond SAR by leveraging multi-modal datasets that incorporate various forms of geospatial information, such as optical imagery, LiDAR, and topographic data. This approach works by integrating different data modalities into a unified training framework, allowing the model to learn shared representations and features across diverse geospatial contexts. For example, research has shown that models trained on both optical and SAR imagery can improve land cover classification accuracy by utilizing complementary information, as demonstrated in studies like "Multi-Modal Deep Learning for Land Cover Classification" (Zhang et al., 2020), which highlights the effectiveness of combining different data types to enhance model performance in complex environments.