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In April 2024, the United Arab Emirates (UAE) experienced its heaviest rainfall in 75 years, which triggered widespread urban flooding and prolonged disruptions. This study assesses the spatial impact and post-rainfall recovery of the event using the U-Net deep learning architecture and daily PlanetScope satellite imagery captured on a pre-rainfall date and on a series of post-rainfall dates between 14 April 2024 and 27 April 2024. Multi-temporal land use and land cover (LULC) classifications were generated using a U-Net model trained via transfer learning, and the LULC categories were water, vegetation, built area, and bare ground. The multi-temporal LULC classifications were further used for change detection analyses to map flooded areas and track temporal recovery across LULC categories. The U-Net model for LULC classification achieved over 95% overall accuracy and a Kappa statistic of 0.927. The change detection and inundation recovery showed that approximately 23.8 km2 – nearly ten times the area of Downtown Dubai – was flooded. The flood affected the built area and bare ground most, while vegetation exhibited higher flood resilience. Although rainfall ended by 17 April, 95% of flooded areas remained submerged three days later, and 37% were still underwater by day 10. These findings reveal the limitations of urban drainage systems in Dubai and the value of high-temporal-resolution remote sensing and deep learning for flood monitoring. This study offers a practical and replicable framework to support urban flood risk assessment, resilience planning, and climate adaptation in the Persian Gulf region, characterized by rapid urbanization and fragile arid environments similar to Dubai.
Xin Hong (2026) studied this question.