DORA is the first end-to-end agentic benchmark for LLM-based disaster response, covering perception, spatial analysis, evacuation planning, temporal reasoning, and report generation over heterogeneous geospatial data, with evaluations of 13 frontier models revealing tool-use and composition failures
Crasar-u-droids: A large scale benchmark dataset for building alignment and damage assessment in georectified suas imagery
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A pipeline uses Mask2Former flood masks and DEMs to compute a single water surface elevation then derives local depths under hydrostatic equilibrium.
A survey providing an overview of publicly available image-based datasets for ML/DL-based disaster management pipelines covering pre-disaster, during, and post-disaster phases.
citing papers explorer
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Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations
DORA is the first end-to-end agentic benchmark for LLM-based disaster response, covering perception, spatial analysis, evacuation planning, temporal reasoning, and report generation over heterogeneous geospatial data, with evaluations of 13 frontier models revealing tool-use and composition failures
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Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models
A pipeline uses Mask2Former flood masks and DEMs to compute a single water surface elevation then derives local depths under hydrostatic equilibrium.
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Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications
A survey providing an overview of publicly available image-based datasets for ML/DL-based disaster management pipelines covering pre-disaster, during, and post-disaster phases.