From inundation rasters to draft action briefs: integrating spatial computing and LLM agents for urban waterlogging emergency decision support
Year: 2026
Type: article
Abstract: Extreme urban waterlogging poses substantial challenges to response systems because inundation mapping, accessibility assessment, and resource instructions are often generated through separate workflows. This study presents MAEDF-UW, a decision-support framework integrating event-specific Earth observation and deterministic spatial computation with a large language model (LLM)-based multi-agent layer. For the 20 July 2021 Zhengzhou rainstorm, a 5 m GF-3 inundation footprint and a native 12.5 m ALOS PALSAR DEM were used. Water depths were estimated under a local hydrostatic assumption after resampling the DEM to an aligned 5 m grid without introducing additional topographic information. Operational depth thresholds were applied to a directed road graph to identify inaccessible road segments and modelled rescue islands. The resulting spatial facts were encoded in a structured dictionary and combined with local contingency-plan rules through retrieval-augmented agents. These agents generated schema- and rule-checked JSON action briefs for human review. The case analysis illustrated how event-specific spatial evidence and local response rules informed draft evacuation and resource-allocation instructions. The evaluation framework treats instruction consistency, spatial-input accuracy and processing latency as separate dimensions. Independent validation is still needed to establish the performance and operational effectiveness of the full workflow.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
OpenAlex ID: W7225567791