AlleyFloodNet: A Ground-Level Image Dataset for Rapid Flood Detection in Economically and Flood-Vulnerable Areas

Abstract

Urban flooding in economically and environmentally vulnerable areas-such as alleyways, lowlands, and semi-basement residences-poses serious threats. Previous studies on flood detection have largely relied on aerial or satellite-based imagery. While some studies used ground-level images, datasets capturing localized flooding in economically vulnerable urban areas remain limited. To address this, we constructed AlleyFloodNet, a dataset designed for rapid flood detection in flood-vulnerable urban areas, with ground-level images collected from diverse regions worldwide. In particular, this dataset includes data from flood-vulnerable urban areas under diverse realistic conditions, such as varying water levels, colors, and lighting. By fine-tuning several deep learning models on AlleyFloodNet, the ConvNeXt-Large model achieved excellent performance, with an accuracy of 96.56%, precision of 95.45%, recall of 97.67%, and an F1 score of 96.55%. Comparative experiments with existing ground-level image datasets confirmed that datasets specifically designed for economically and flood-vulnerable urban areas, like AlleyFloodNet, are more effective for detecting floods in these regions. By successfully fine-tuning deep learning models, AlleyFloodNet not only addresses the limitations of existing flood monitoring datasets but also provides foundational resources for developing practical, real-time flood detection and alert systems for urban populations vulnerable to flooding.