Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence

The research attempted to provide a fast and preliminary approach for elements-at-risk mapping by developing a semi-automated detection and characterisation method. The mapping and characterisation of building footprints is a challenging task due to inaccessibility and incompleteness of the required data, thus hindering the estimation of loss caused by natural and anthropogenic hazards. Major advancements have been made in the collaborative mapping of buildings with platforms like OpenStreetMap, however, many parts of the world still lack this information or the information is outdated.

The authors notice certain shortcomings in our research particularly, the need for a local expert and good quality auxiliary data to obtain reasonable building occupancy information, however, our research contributes to developing a rapid method for generating a building elements-at-risk (EaR) database in data-scarce regions with attributes of occupancy types, thus supporting regional risk assessment, disaster risk mitigation, risk reduction initiatives, and policy developments.