Document Type
Conference Proceeding
Source of Publication
International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives
Publication Date
1-27-2026
Abstract
The United Arab Emirates (UAE) experienced an extreme rainfall event between April 15 and 17, 2024, and that resulted in severe flooding in its coastal regions. Dubai was among the most affected regions. This study applies a hierarchical deep learning model on PlanetScope imagery to detect flood inundation, quantify flood extent by land cover, and examine short-term recovery dynamics. While earlier work detailed the methodological development of a hierarchical U-Net model (Hong et al., in press), here we emphasize its application for monitoring resilience trajectories in an arid urban environment. Results show that approximately 22 km2 of land was flooded, with bare ground and built area most affected, while vegetation demonstrated greater resilience. Recovery dynamics reveal that vegetation and built area recovered rapidly within the first week, whereas bare ground recovered more slowly but continued to improve through the ten-day monitoring period. These findings highlight the importance of integrating fine-resolution satellite monitoring with deep learning approaches to better understand disaster recovery and inform urban resilience planning in desert cities.
ISSN
Publisher
Copernicus GmbH
Volume
48
Issue
4/W18-2025
First Page
161
Last Page
166
Disciplines
Computer Sciences | Earth Sciences
Keywords
deep learning, LULC classification, PlanetScope, urban flooding, urban resilience assessment
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Hong, Xin, "Mapping Post-Rainfall Recovery in Arid Regions Using a Hierarchical U-Net" (2026). All Works. 7938.
https://zuscholars.zu.ac.ae/works/7938
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series