Xian Sun

Remote sensing disaster assessment has long stopped at segmentation masks, bounding boxes and category labels, while responders need checkable written reports. DisasterAgent feeds bitemporal imagery to a vision expert toolkit, organizes the tool outputs into a spatiotemporal perception graph, and draws inline-cited evidence from that graph to drive a vision-language model that was never fine-tuned on disasters. On DisasterM3 its average disaster report generation score is 13.5% above the Qwen3.5-27B model serving as its central brain; on RSCC, ST5-SCS is 15.34% above that same base model.

Remote sensing disaster assessment has long stopped at segmentation masks, bounding boxes and category labels, while responders need checkable written reports. DisasterAgent feeds bitemporal imagery to a vision expert toolkit, organizes the tool outputs into a spatiotemporal perception graph, and draws inline-cited evidence from that graph to drive a vision-language model that was never fine-tuned on disasters. On DisasterM3 its average disaster report generation score is 13.5% above the Qwen3.5-27B model serving as its central brain; on RSCC, ST5-SCS is 15.34% above that same base model.