Artificial intelligence that helps communities anticipate disaster impacts, understand evolving conditions, and make better decisions under uncertainty.
The UrbanResilience.AI Lab develops the scientific foundations of Disaster AI. Working at the interface of artificial intelligence, urban science, and civil infrastructure systems, we build AI methods that reason over geospatial data, infrastructure dependencies, and evolving evidence — and that keep uncertainty, provenance, and human judgment visible.
Disaster intelligence today is fragmented across static hazard maps, dashboards, and one-off prediction tools. We are building what comes next: AI systems that learn transferable representations of hazard, risk, and resilience; maintain an auditable, evolving picture of community conditions; seek the evidence that matters for a decision; and connect that evidence to the choices communities actually face.
Representations feed world models, world models ground agents, and agents support decision flows — with hazard-specific tools plugging in throughout.
Can AI answer complex disaster analytics questions with sound spatial logic and traceable evidence? We develop context graphs, disaster ontologies, and benchmarks that test whether AI systems reason correctly over geospatial data, infrastructure networks, hazard layers, and vulnerability indicators — measuring decision value, not just answer accuracy.
We study foundation models that learn transferable representations of hazard, risk, resilience, exposure, and infrastructure state — so disaster intelligence can transfer across places, hazards, and impact types instead of being rebuilt from scratch for every task and region.
Disaster information arrives as partial, delayed, and sometimes contradictory claims. We develop world models that maintain an evolving, auditable representation of community conditions — tracking sources, timestamps, confidence, and contradictions as new information emerges.
More information is not always better. We study AI agents that decide which evidence to seek, verify, or escalate next — optimizing for whether a new source would materially change a disaster decision, not for retrieval volume.
Disasters propagate through connected systems: flooding disrupts roads, road loss cuts hospital access, outages endanger vulnerable facilities. We model these cascading pathways and develop decision-flow AI that connects them to real decision points — evacuation timing, resource prepositioning, facility protection, lifeline restoration — with assumptions, constraints, and human agency kept visible.
Flood and wildfire are our core application domains and the proving grounds for the broader program. Our work spans flood depth and impact prediction, road and facility access loss, lifeline disruption, wildfire risk and spread analysis, damage assessment, and evacuation and response support.
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 PAPER →Can LLMs orchestrate 26 disaster-response tools into executable multi-step workflows? A benchmark with step-level failure diagnosis across 14 models.
READ PAPER →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 PAPER →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 PAPER →Zero-shot damage classification from multi-view ground-level imagery, applied to the 2025 Eaton and Palisades fires, accelerating a critical recovery bottleneck.
READ PAPER →A vision for collective human–machine intelligence for augmented resilience and the conceptual blueprint behind our agentic Disaster AI research program.
READ PAPER →Parcel-level occupancy after Hurricane Helene, F1 0.848 — linking street-view imagery to recovery signals via vision-language models.
READ PAPER →Instruction-tuned LLM for multi-label social-media text classification in disaster informatics.
READ PAPER →The Disaster AI program builds on more than a decade of the lab's research in urban resilience — including pioneering work on the Disaster City Digital Twin, human-infrastructure systems resilience to flooding, equity-aware infrastructure resilience assessment, and urban resilience to health emergencies. That foundation in how communities and infrastructure behave under stress is what grounds our AI research in real disaster dynamics.
We welcome collaborations with researchers, agencies, communities, and organizations working on disaster science, resilient infrastructure, geospatial intelligence, and human-centered AI.
Contact Dr. Mostafavi