The increasing frequency of extreme climate events poses significant risks to slope infrastructure, while traditional inspection methods are often inefficient and unsafe. Although unmanned aerial vehicles (UAVs) combined with structure‐from‐motion (SfM) provide high‐fidelity 3D models, they lack the semantic understanding necessary for automated damage assessment. This study addresses this gap by developing and validating an anomaly‐enhanced digital twin (AEDT) framework. The proposed system integrates multiview UAV imagery, SfM‐based 3D reconstruction, and a convolutional neural network (CNN) for automated anomaly classification. This information is then fused into an interactive, geographic information system (GIS)‐compatible DT platform for lifecycle management. A case study on a soil and water conservation (SWC) structure in central Taiwan was conducted for verification. The deep learning module achieved a macroaverage F1‐score of 0.81, demonstrating balanced performance across erosion, spalling, siltation, and collapse classes. This was validated on a held‐out test set derived from a total of 2000 annotated images spanning four anomaly types with three severity levels. Furthermore, the AEDT‐based workflow reduced on‐site inspection time by ~63% compared to conventional manual methods. The resulting AEDT model provides a dynamic, semantically enriched 3D representation of the infrastructure, linking geometric data with damage attributes and historical maintenance records. This research demonstrates a feasible and scalable solution for intelligent infrastructure monitoring, offering a robust tool for enhancing climate resilience and enabling proactive asset management.
Pan et al. (Thu,) studied this question.
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