Automated geovisualization of flood disaster impacts in the global South cities with open geospatial data sets and ICEYE SAR flood data

Flooding is a highly destructive natural disaster affecting millions of people annually, causing substantial global economic losses. Especially in the Global South, vulnerable populations with limited resources for flood protection and recovery suffer the most. However, effective flood impact analysis and standardized geospatial data processing and visualization is lacking, hindering effective disaster risk reduction. This study employs Python to develop a standardized, automated analysis and visualization model to address flood impacts after a high magnitude event anywhere in the world by using global geospatial data sets. In order to test its applicability two relevant cases, with different locations and scales, were selected, (Bangkok, Thailand, and Tula de Allende, Mexico). In both cases the automated flood model efficiently processed flood extent and depth data from ICEYE SAR images, added the population estimates from the German Aerospace Center, and key infrastructure elements from OpenStreetMap. The output of the automated process is presented as web-based interactive maps, offering insights into flooding severity and impacts with minimal user input. This quick approach (processing time between 41 s and 10,5 min) facilitates timely responses by first responders, with great potential to aid and improve the efficiency of international humanitarian efforts. By leveraging global high-resolution geospatial data for local-level analysis, this study demonstrates the versatility and time-saving benefits of this automated analysis and visualization. Automated models standardize results, minimizing human errors, and enabling consistent historical flood data comparison. Even though the databases are gathered from trusted sources, additional evaluation of uncertainties of the results with field data should be considered in further development of this tool. Nevertheless, this research highlights the worldwide potential, and especially the Global South, of automated global geospatial analysis to improve disaster impact reduction by enhancing efficient response efforts.