Abstract

Crowdsourced street-level images offer a practical source of street-scale evidence for urban flood monitoring, yet floodwater-level recognition remains difficult because such images are captured from heterogeneous viewpoints and are often affected by occlusion, reflections, nighttime glare, and cluttered backgrounds. This study proposes a Mask-guided Enhanced Object Detection (MEOD) pipeline for vehicle-based floodwater-level recognition, which first uses floodwater segmentation to suppress unstable water-surface appearance and generate water-masked inputs, and then adapts YOLO11-based object detection to focus on vehicle-submersion cues to obtain floodwater levels. We compiled a four-level vehicle-centered dataset comprising 3110 crowdsourced street-level flood images, and evaluated the effectiveness of MEOD with four controlled settings. Results showed that MEOD achieved an F1-score of 0.89 and an mAP@0.5 of 0.93, compared with 0.77 and 0.84 for the original object-detection baseline, respectively. The class-wise correct recognition rates indicated that MEOD reduced interference-driven ambiguity between neighboring floodwater levels under the evaluated dataset and experimental protocol. These results indicate that floodwater segmentation can serve as a task-oriented intermediate representation for detector-based floodwater-level recognition in vehicle-centered street-level flood images.

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