Remote Sensing Image Understanding

The field develops methods to extract useful information from satellite, aerial, and other overhead images, including optical, multispectral, radar, and hyperspectral data. Common tasks include scene classification, object detection, segmentation, change analysis, and answering questions about image content. Approaches range from convolutional and transformer models to multimodal vision-language systems, often using sensor fusion, geospatial priors, self-supervised pretraining, or tools for inspecting high-resolution scenes. A central challenge is making systems reliable across different sensors, locations, scales, and time periods while grounding fine-grained claims in visual evidence and controlling computational cost.