# High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels Source: [High Resolution Flood Extent Detection Using Deep Learning with Random Forest Derived Training Labels]() Collection index: [llms.txt]() Data updated: 2026-03-26T05:00:04.122Z Authors: Azizbek Nuriddinov, Ebrahim Ahmadisharaf, Mohammad Reza Alizadeh Published: 2026-03-23T19:26:46.000Z ## 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 City metropolitan area, was used as an example to evaluate the framework. Results demonstrate that the U-Net model with topographic features achieved very close performance to the optical-only configuration (F1=0.92 and IoU=0.85 by both modeling scenarios), indicating that HAND and slope provide only marginal value to inundation extent detection. The proposed framework offers a scalable and label-efficient approach for mapping inundation extent that enables modeling under data-scarce flood scenarios. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on PlanetScope Hurricane Ida (test)](): Semantic segmentation; PlanetScope Hurricane Ida (test); mIoU: 85.2. ## Original sources and implementation [Original paper]() [PDF]()