Technology
Geospatial AI for disaster and resilience intelligence
Geospatial AI applies artificial intelligence to information associated with locations. EmergenCITY AI uses natural-language interaction and SQL-based geospatial assessment to help teams examine how hazards, people, infrastructure, and community vulnerability intersect. The resulting evidence supports preparedness and assessment of developing threats.
Location connects the evidence
Disaster questions often require more than one layer of information. A hazard footprint describes one condition; community data and infrastructure locations add context about what may be exposed. Shared geography allows those layers to be compared in a way that relates to the question being asked.
For example, “Which facilities may be exposed to a developing hazard?” requires an area of interest, an appropriate hazard layer, facility locations, and a clear definition of overlap. Each choice affects the interpretation of the answer.
From a plain-English question to geospatial computation
ResilUS is the resilience analyst AI agent inside EmergenCITY AI. It connects the user’s question with curated geospatial data and SQL-based computation. The objective is a data-grounded assessment with an understandable geographic basis.
Understanding the assessment’s scope and evidence helps teams interpret its findings. A map alone is not the whole answer: the numerical findings and methods also matter.
Read the numbers, geography, and assumptions together
Math describes the quantified findings. Map shows where conditions intersect. Memo explains the assessment in plain language. Audit records sources, methods, and assumptions. Together, these outputs help teams review both the result and how it was obtained.
Interpretation depends on geographic coverage, data resolution, source timing, and the methods used. A screening assessment should not be presented as a confirmed account of conditions at every location.