An Urban Information System to Assess Neighborhood Climate Risk and Daily Exposures in Cities
Description
Developing ML models (e.g., random forests approaches) to capture climate responses in urban settings within NYC
Detailed example
AI system is being used to develop flood models and heat exposure models for NYC. Outputs include flood depths and flood frequency analysis, as well as heat exposure for standard routes of walking/cycling/jogging through the city.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Shows how NASA tools can inform risk management in cities, with direct interest and engagement from local governments, community groups, and private sector partners. Many of these tools are in development (low TRL/ARL) but show great promise for wider improvements and applications.
Controls / human review
ATO: Not reported; PIA: Not published