GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence
GeoDisaster is a new benchmark for operational disaster geo-intelligence, containing 2,921 instances across 43 question types and five task families (deforestation monitoring, multi-hazard analysis, building-damage assessment, flood-safe routing, and Sentinel-1 SAR flood monitoring). It integrates heterogeneous EO/GIS data and uses executable workflows for ground truth. The paper also proposes an orchestrated multi-agent framework with 18 disaster tools and Role-Contract Expectation Alignment (RCEA) to improve tool use and decision making.
[2606.17246] GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence
[Submitted on 15 Jun 2026]
Title:GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence
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Abstract:Remote-sensing vision-language models (RS-VLMs) have advanced Earth-observation analysis toward visual interpretation and instruction-following, yet fall short of operational geo-intelligence, which demands tool-grounded spatial reasoning and structured, evidence-backed decisions. We introduce GeoDisaster, an operational geospatial disaster reasoning benchmark with 2,921 verified instances across 43 question types and five task families: deforestation monitoring, multi-hazard analysis, building-damage assessment, flood-safe routing, and Sentinel-1 SAR flood monitoring. Instances integrate heterogeneous EO/GIS evidence-optical and SAR imagery, raster masks, vector geometries, road networks, and exposure layers-spanning hazard detection, damage assessment, exposure estimation, and diagnostic report generation. Ground-truth answers are grounded in executable geospatial workflows and deterministic consistency checks, removing the need for language-model annotation. We further propose an orchestrated multi-agent framework with 18 disaster-oriented tools, where role-specialized agents coordinate through explicit execution contracts, aligned via Role-Contract Expectation Alignment (RCEA): failure-aware supervised fine-tuning combined with contract-grounded reinforcement learning over dense step-level signals. Experiments show that GeoDisaster challenges existing RS-VLMs and agentic systems, while RCEA improves tool use, evidence grounding, state consistency, and decision generation.
Comments: 28 pages, 11 Figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA)
Cite as: arXiv:2606.17246 [cs.CV]
(or arXiv:2606.17246v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.17246
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Maram Hasan [view email] [v1] Mon, 15 Jun 2026 19:46:10 UTC (31,522 KB)
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