Abstract: A system and method for generating visual representations of climate hazard risk identifies geographic regions associated with flood hazard risks or risks of other extreme weather events. Disclosed embodiments may identify geographic regions associated with climate hazard risk using geolocation data such as geo-coordinates or postal zones. In a climate hazard risk platform, a climate hazard dashboard generates dashboards and reports providing visual representations of climate hazards and real estate assets within a selected geographic region. These dashboards and reports enable users to understand risk in different scenarios, such as projections over various time frames, physical climate hazard types, current and future time frames, and probabilities of occurrence. The platform includes a cloud data warehouse and a business intelligence (BI) analytics component. A climate hazard risk map may overlay a map of estimated climate hazard and a map of real estate assets within the selected geographic region.

Abstract: A system and method for flood hazard estimation inputs a satellite elevation map and applies a machine learning model to output a geographic map representing flood hazard areas. The machine learning model is trained using a generative adversarial network (GAN) to produce an output of a deterministic hazard mapping algorithm. A GAN objective applies a loss function, reweighted to increase the importance of high hazard areas. The method retrieves a DEM topography file representing elevation data of an identified terrain, and applies a sink-filling algorithm to detect and fill sinks in the DEM topography. The algorithm subtracts the DEM elevation data to generate a filled topography, and identifies flattest regions of the filled topography. The algorithm then generates a flood hazard map by merging the filled topography and the DEM elevation data, using a weighting function that balances the detected sinks and the flattest regions of the filled topography.