Urban Landscape Recovery and LULC Analysis: A Deep Learning Approach to Post-Extreme Rainfall Impacts in Dubai

Abstract. From April 14 to 18, 2024, the United Arab Emirates (UAE) experienced its heaviest rainfall in 75 years, resulting in widespread flooding across multiple emirates, including Dubai. This study utilizes high-resolution PlanetScope imagery and a U-Net deep learning model to assess the flood impact and analyze post-rainfall recovery patterns in Dubai’s urban landscape. By integrating Sentinel-2-derived land use and land cover (LULC) data to refine the training dataset, a high-accuracy U-Net model was developed through transfer learning that effectively classified pre- and post-rainfall LULC. Post-rainfall LULC change detections indicate that 23.8 km² of land was flooded, which is equivalent to about 10 times the area of Downtown Dubai. Bare ground (66% of the flooded land) and built area (32% of the flooded land) were the most affected, while vegetation (2% of the flooded land) presented greater flood resilience. Post-event monitoring revealed that 95% of flooded areas remained submerged after three days and 37% were still underwater even ten days post-event. These findings highlight the prolonged impact of extreme rainfall on urban infrastructure in an arid environment. This study contributes to remote sensing-based flood impact assessment by leveraging high-resolution PlanetScope imagery and deep learning techniques. It demonstrates the effectiveness of transfer learning in improving LULC classification, particularly for minority classes such as small-patch vegetation. The results provide critical insights into urban flood recovery dynamics and offer valuable information for disaster management, flood resilience planning, and future urban adaptation strategies.