Inland communities face escalating flood risks as residential development continues within regulatory floodplains, Douglas, and Sarpy Counties in eastern Nebraska, an area heavily impacted by the 2019 Midwest floods. We develop a geospatial artificial intelligence (Geo-AI) framework integrating bi-temporal National Agricultural Imagery Program (NAIP) imagery, parcel-level assessment data, building footprints, and flood hazard maps, and apply a Siamese U-Net deep learning model to detect new, demolished, and existing residential structures. Results show a substantial increase in floodplain exposure, with over 2,250 new residential buildings constructed more than 21 times the number of demolitions primarily in high-assessed-value areas, while removals are concentrated in disadvantaged neighborhoods. The model achieved 98% overall accuracy, demonstrating strong capability for detecting building-scale change. These findings highlight growing inequities in flood-risk exposure and demonstrate the value of Geo-AI for spatially explicit flood-risk monitoring, risk-informed planning, and equitable land-use policy development.
Lee et al. (Tue,) studied this question.