[2603.22518] High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.22518 (cs) [Submitted on 23 Mar 2026] Title:High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels Authors:Azizbek Nuriddinov, Ebrahim Ahmadisharaf, Mohammad Reza Alizadeh View a PDF of the paper titled High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels, by Azizbek Nuriddinov and 1 other authors View PDF HTML (experimental) Abstract:Validation of flood models, used to support risk mitigation strategies, remains challenging due to limited observations during extreme events. High-frequency, high-resolution optical imagery (~3 m), such as PlanetScope, offers new opportunities for flood mapping, although applications remain limited by cloud cover and the lack of labeled training data during disasters. To address this, we develop a flood mapping framework that integrates PlanetScope optical imagery with topographic features using machine learning (ML) and deep learning (DL) algorithms. A Random Forest model was applied to expert-annotated flood masks to generate training labels for DL models, U-Net. Two U-Net models with ResNet18 backbone were trained using optical imagery only (4 bands) and optical imagery combined with Height Above Nearest Drainage (HAND) and topographic slope (6 bands). Hurricane Ida (September 2021), which caused catastrophic flooding across the eastern United States, including the New York C...