Selected papers from the Urban Resilience.AI Lab on Disaster AI, geospatial reasoning, flood and wildfire intelligence, and disaster decision support. Each entry links to the paper of record.
3,000 rigorously verified questions across eight disaster types. A comprehensive test of whether language models can answer the questions disaster management actually asks.
Read “DisastQA: Benchmarking Question Answering in Disaster Management” on arXiv →Can LLMs orchestrate 26 disaster-response tools into executable multi-step workflows? A benchmark with step-level failure diagnosis across 14 models.
Read “DisasterBench: LLM Planning under Typed Tool Interface Constraints” on arXiv →A concept-to-schema knowledge graph that grounds AI reasoning over disaster data in expert knowledge and connects concepts to the geospatial schemas that answer them.
Read “DisasterLex: An Expert Knowledge Graph for Geospatial Disaster Analytics” on arXiv →Domain-aware coreset selection lets a tabular foundation model match supervised flood-depth accuracy using 0.7% of the training data and transfer to unseen watersheds.
Read “Data-Efficient Flood Depth Prediction with Tabular Foundation Models” on arXiv →Zero-shot damage classification from multi-view ground-level imagery, applied to the 2025 Eaton and Palisades fires, accelerating a critical recovery bottleneck.
Read “Automated Wildfire Damage Assessment via Vision-Language Models” on arXiv →A vision for collective human–machine intelligence for augmented resilience and the conceptual blueprint behind our agentic Disaster AI research program.
Read “Disaster Management in the Era of Agentic AI Systems” on arXiv →Parcel-level occupancy after Hurricane Helene, F1 0.848 — linking street-view imagery to recovery signals via vision-language models.
Read “Recov-Vision: Street View + VLMs for Post-Disaster Recovery” on arXiv →Instruction-tuned LLM for multi-label social-media text classification in disaster informatics.
Read “CrisisSense-LLM: Instruction Fine-Tuned Multi-label Classification” on arXiv →These papers sit inside the Disaster AI Research Program, directed by Dr. Ali Mostafavi.