arXiv cs.AI· Zhijian Li, Jiawei Song, Chao Ren, Yidan Zhang, Peijin Wang, Xian Sun, Kun Fu·· 3 小时前AI 评分35
DisasterAgent：基于证据驱动的遥感灾害报告生成智能体
DisasterAgent: An Evidence-Driven Agent for Verifiable Disaster Report Generation From Remote Sensing Imagery
AI 导读
arXiv 论文提出 DisasterAgent，一个证据驱动的智能体，通过感知-增强-生成工作流，从双时相光学影像生成可验证的灾害报告。它先用视觉专家工具构建时空感知图（STPG），再通过证据增强（EA）附加内联引用，形成从陈述到视觉证据的闭环。在 DisasterM3 和 RSCC 基准上，其灾害报告生成平均分较原模型提升 13.5%，ST5-SCS 提升 15.34%，代码将开源。
正文 · 原文
Abstract:Remote sensing disaster assessment is moving from simple damage classification toward comprehensive report-level analysis. In this setting, responders need not only damage descriptions but also verifiable evidence to guide response and recovery. Recent vision-language models (VLMs) and tool-augmented agents offer strong multimodal reasoning and perception, but they struggle to provide the evidence required for high-stakes verification. To address these limitations, we propose DisasterAgent, an evidence-driven agent that uses a Perception-Augmentation-Generation workflow to generate verifiable disaster reports from bitemporal optical imagery. DisasterAgent first invokes a specialized vision expert toolkit in the perception phase to build a Spatiotemporal Perception Graph (STPG), serving as a structured memory to store and organize disaster-induced changes across space and time. Evidence-based Augmentation (EA) then extracts evidence from the STPG, attaching inline citations that form a closed evidence loop from each statement to the supporting visual evidence. Extensive experiments on the DisasterM3 and RSCC benchmarks demonstrate that DisasterAgent consistently outperforms general-domain VLMs, remote sensing-specialized models, and existing remote sensing agents. Notably, compared with the original model, it improves the average score for disaster report generation on DisasterM3 by 13.5\% and ST5-SCS on RSCC by 15.34\%, providing a more accurate and verifiable solution for disaster emergency response. The code will be available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13776 [cs.CV] |
| (or arXiv:2611.13776v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13776 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhijian Li [view email]
[v1]
Tue, 10 Nov 2026 09:15:06 UTC (20,900 KB)
来源：arXiv cs.AI · arxiv.org