A Deep Learning Pipeline for Urban Flood Depth Estimation from Street-Level Imagery.

Flooding remains one of the most frequent and damaging natural disasters globally, exacerbated by climate changes and rapid urbanization. Understanding and mitigating urban flood risk requires near-real-time, fine-scale monitoring, including flood depth estimation. This study introduces a deep learning-based pipeline for estimating urban flood depth using device-independent street-level imagery, to complement existing remote, in-situ and hydrological approaches. By leveraging opportunistic sensing, this method exploits open-source tools to enhance spatial granularity and accessibility in flood monitoring.The dataset, derived from a publicly available source, consisted of 3,367 annotated images of submerged vehicles, categorized into five flood levels based on water height relative to vehicle features (e.g., tires, chassis, windows). Cars were selected as reference objects due to their standardized dimensions and prevalence in urban environments, enabling consistent and reliable flood depth estimation.The proposed pipeline processes images through an end-to-end workflow designed for real-time inference. It consists of four sequential stages: (1) vehicles are detected in street-level images using a pre-trained YOLO-World model; (2) detected vehicle regions are cropped and resized with a 20% bounding box enlargement to include flood visual indicators and additional context cues for the classification.; (3) images are super-resolved using pre-trained Enhanced Deep Super-Resolution (EDSR) networks to improve low-resolution imagery; (4) images are classified according the flood depth level using a ResNet50 model fine-tuned on the annotated dataset.The classifier demonstrated robust performance across the five flood levels. The confusion matrix revealed minor misclassifications between adjacent classes, particularly Levels 0 and Level 1. One-vs-all area under the receiver operating characteristic curves (AUC) values exceeded 0.85 for all classes, with the highest performance observed for Level 4 (AUC = 0.98) and Level 0 (AUC = 0.94). Real-world validation using crowdsourced images from the 2021 flood in Central Europe confirmed the pipeline's reliability, delivering accurate and consistent flood level predictions in near-real-time.This research advances urban flood monitoring by introducing a cost-effective and adaptable method for flood depth estimation that leverages existing devices without specialized hardware. The pipeline’s modular design ensures scalability and seamless integration into early warning systems and disaster response platforms. Future work will explore its application to aerial and drone imagery with oblique perspectives and develop cross-view geolocalization of flood depth measurements to improve spatial coverage and accuracy.