Resolving the accuracy-scale-cost trilemma: Bridging inequitable access to flood information with AI generated flood maps

The mapping of flood-prone areas is crucial for disaster response and adaptation. With a flood map, communities, insurers, and planners can prepare and be insured against risk damages. However, hindered by high resource demand, open-access flood maps, such as the FEMA National Flood Hazard Layer (NFHL) in the US, suffer from incomplete information. Currently, only one-third of all river channels in the country is complete, leaving population and structures in the other two-thirds vulnerable. To address this data gap, we present a methodology for completing the missing flood zones in the FEMA NFHL based on generative AI. The model learns patterns from the mapped areas and generates flood maps at 30 meters resolution in the unmapped areas with up to 81% and 65% in national scale studies. With the new flood data covering all river channels, we found that there are 11 million exposed population, and 4.1 million buildings located in flood zones that are unmapped by the NFHL as of 2023. This quantifies the inequitable access to open flood information across the country. Additionally, we found that 40% of all urban areas are inadequately mapped by the NFHL, with 103 out of the total 917 Core-based statistical areas having little to no FEMA maps even though there are at least more than 1% of the population at risk. More alarmingly, demographic analyses have revealed that these cities also have the highest percentage of elderly and young populations, who are more vulnerable to crises. This study highlights the use of generative AIs in scaling up mapping processes. With the completed NFHL flood map, exposed communities that were previously left out of the NFHL can begin planning for flood resilience today.