Urban Flood-Depth Estimation from Crowdsourced Image–Text Data Using Reference-Object Reasoning and Conditional Fusion

Urban pluvial flooding is a rapidly evolving disaster that requires timely and fine-grained water-depth information for emergency response and impact assessment. However, street-scale water-depth observations are often difficult to obtain during short-duration flood events due to limited coverage and insufficient spatial detail of conventional monitoring systems. To alleviate this problem, this study proposes an image-led framework for urban street-scale flood-depth estimation from crowdsourced image–text data. The proposed method integrates reference-object-based visual evidence construction, textual depth extraction, and conditional image–text fusion to estimate water depth from crowdsourced image–text records. Specifically, image-based depth evidence is constructed by identifying reference objects, determining submerged parts, and establishing object-depth relationships through knowledge mapping. Textual information is further analysed through rule-based extraction and semantic verification to identify quantitative depth cues, including explicit values, ranges, and relative water-level descriptions. Considering the potential inconsistency between image and text observations, a conditional fusion strategy is designed to incorporate textual information only when quantifiability, directional, and image–text discrepancy conditions are satisfied. Experiments demonstrate that the proposed method can effectively extract quantitative flood-depth information from crowdsourced multimodal data and improve the utilization of heterogeneous disaster observations. The resulting depth estimates are intended as auxiliary evidence for post-disaster situational assessment rather than as substitutes for in situ water-level measurements.