Unsupervised Estimation of Post-Event Standing Urban Floodwater Depth Using Aerial Imagery and Digital Terrain Models

Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis

Effective flood monitoring is critical for minimizing the impacts of flood disasters on populations and infrastructure. Yet reliable remote sensing across extensive and environmentally diverse regions remains challenging, as most segmentation algorithms lack the generalisation capacity required for large-scale application, while annotated flood data are scarce and unevenly distributed. This study presents an end-to-end multimodal framework for Russian Federation territories sustainable flood monitoring and damage assessment based on synthetic aperture radar data, multispectral imagery, and digital elevation models with their derivatives, forming a 21-channel input. Using a self-collected multimodal dataset covering seven Russian regions, two strategies for water surface detection under limited data conditions were compared: a supervised U-Net++ model and the self-supervised AnySat architecture pre-trained and fine-tuned for the segmentation task. Under the data conditions of this study, supervised learning proved more effective, while the AnySat-based approach offered greater stability and retains advantages for settings where larger unlabelled data or missing modalities at inference are expected. The best flood area predictions were used to estimate flood impact in urban areas in terms of the area affected, material damage, casualties, and ecological and agricultural impact. The estimations were conducted following the official methodology of the Russian Ministry of Emergency Situations. Applied to the 2019 Tulun flood, the obtained results closely matched official assessments, except for material damage, due to the open-source databases usage. The results demonstrate the potential of deep learning and multimodal satellite data integration for scalable, reliable flood monitoring across diverse environmental and data-limited conditions.

A vision-enhanced framework for urban flood modelling and monitoring under data scarcity.

Urban flood risk is increasingly exacerbated by climate change, while traditional models face accuracy issues from low-quality DEM and land use data and inefficient monitoring. This study proposes a computer vision-based collaborative framework for urban flood simulation and monitoring, which is demonstrated in a typical study area in Zhongshan City, China. In the one-dimensional drainage network modelling stage, the use of road buffer zoning, building-footprint elevation refinement, and combined slope-width-roughness calibration resulted in Nash-Sutcliffe efficiency of 0.802-0.917 for three observed heavy-rainfall events, with peak timing error ≤ 30 min and peak water-level error ≤ 0.12 m. By constructing a U-Net network for single-class segmentation of building footprints from satellite imagery and updating land use, the NSE values increased to 0.884-0.922, with peak-timing errors and peak value errors reduced to 15 min and 0.1 m, respectively, thereby improving model performance. A coupled SWMM-ITF-FLOOD model was developed to simulate inundation extent and depth. The improved CBAM-YOLOv8 was then used to automatically identify the inundation extent, with an area error below 8%. A novel visual water-level gauge was also designed, with inundation depth recognition error of 2 cm using U-Net binarization for reading. The results demonstrate that computer vision can supplement incomplete surface and monitoring data and improve the representation of model inputs and enable high-precision real-time monitoring, providing viable technical support for smart water management and urban flood control.

Near Real-Time Flood Mapping from Sentinel Data Using Machine Learning Techniques

A near-real-time flood-mapping approach that integrates satellite-based Earth observation data, digital elevation models (DEMs), and machine-learning techniques, which demonstrates strong potential for operational deployment in near-real-time flood monitoring.

Enhancing flood inundation mapping in data-scarce regions by addressing spatial uncertainty in global DEMs

Comparative Analysis of Urban Flood Mapping Using Sentinel‐1 SAR Imagery and Machine Learning

Flood mapping using remote sensing data has the potential to play a crucial role in monitoring and assessing flood events, particularly in urban environments, where complex infrastructure creates significant challenges. While Synthetic Aperture Radar (SAR) has promising potential for flood mapping, current methods struggle in the urban landscape due to the presence of heterogeneous surfaces and complex infrastructure, which can create double‐bounce effects and alter reflectance. This research advances urban flood mapping through a comprehensive comparative analysis of traditional change detection and Machine Learning (ML) methods, including the MultiSenseRF multi‐source feature‐fusion workflow. The study analyzes five geographically distinct urban areas—Houston, Texas, USA, Dhaka, Bangladesh, Iwaki, Japan, Lumberton, North Carolina, USA, and Beira, Mozambique—during flood events to evaluate method performance across diverse urban settings. Traditional change detection methods and ML techniques were evaluated alongside integrated approaches. Combining temporal change‐detection signals with landscape features using ML improved flood‐mapping performance, with average F1 scores of about 0.75 and a range of 0.58 in dense urban areas to 0.93 in peri‐urban regions. These findings provide a robust framework for improving urban flood mapping accuracy and have significant implications for urban flood management.

Flood Detection from Sentinel-1 SAR Image using Multi-Scale Features and Distance-based Classification

Floods are some of the most devastating natural disasters that damagingly affect infrastructures, agriculture, and human life. The traditional method of flood mapping is mostly based on optical satellite imagery, which is affected by severe drawbacks such as blockage by clouds, inadequate lighting and weather conditions during heavy rainfalls conditions when such events are most direly needed, flood assessment-wise. The paper proposes an automated flood monitoring system based on Sentinel-1 Synthetic Aperture Radar (SAR) imagery in urban flood settings, which is known to have known limitations in the complex backscatter geometry. The proposed methodology presents Multi-Scale Flood Feature (MSFF) extraction, a technique that calculates the mean backscatter intensity and local variance statistics in 3×3, 5×5 and 7×7 adaptive neighbourhood windows, providing a feature descriptor of six dimensions per pixel. A distance-based prototype classifier classifies flood or non-flood by the minimum Euclidean distance to class-specific mean feature prototype with a normalised confidence score based on the prototype distance margin. The framework has two modes: dataset-level analysis to quantitatively evaluate performance and single-image inference to map floods in real-time. The experimental results indicate a total classification accuracy of about 70 percent and steady prediction of the probability of flooding across the levels of severity.