## 3 Use Case: (Com)2 INet for Levee Piping Hazard Inspection via Remote Sensing  
Piping leakage is a critical factor leading to levee breaches and catastrophic flooding disasters. Levee breaches often result in mass casualties, making piping leakage a major threat to flood disaster prevention and mitigation in China. According to statistics, China's river levees extend over 328,000 km, protecting a population of 650 million and 42 million hectares of farmland. However, more than 90% of these river levees consist of aging earth-rock dams, which are highly susceptible to severe hazards during the annual flood season due to high water levels. UAV-based remote sensing technology has demonstrated significant potential in the inspection and identification of piping hazards in levees. UAV platforms are characterized by their portability, lightweight design, and low cost, enabling rapid acquisition of levee data. Furthermore, various sensors can capture diverse morphological data of levees, providing abundant information sources for identifying piping hazards. The remote sensing data processing techniques designed based on the (Com)2 INet architecture can achieve rapid, accurate, and large-scale inspections of levee hazards. However, current UAV-based remote sensing approaches for levee piping hazard inspection face challenges such as low recognition accuracy and poor scalability. Although piping hazards are visually prominent in thermal infrared imagery, such imagery is heavily influenced by weather conditions. The piping features extracted from thermal infrared imagery exhibit significant variability under different weather and environmental conditions, leading to poor stability in distinguishing piping hazards from other surface objects. Moreover, the complex flood-season levee environment introduces interference to recognition algorithms. The levee surface is covered by diverse natural and human-made objects, and the harsh conditions during the flood season further complicate the scene. Particularly in large-scale, multi-sensor, and multi-object application scenarios, uncertain objects often affect the performance of recognition algorithms. Currently, deep-learning-based object recognition algorithms typically rely on large sample datasets. However, due to the difficulty in obtaining piping hazard data, the demand for extensive training samples cannot be met. This study proposes an unsupervised machine-learning-based method, the SC-based stepwise adaptive clustering method (SACM), for piping hazard segmentation using UAV thermal infrared imagery. The SC-based SACM method extracts piping hazards through iterative clustering and adaptive strategies. First, thermal infrared imagery is converted into temperature maps. Second, a series of binary classifications is applied to the temperature maps to progressively ex clude background regions unrelated to piping hazards, thereby mitigating interference caused by complex levee environments. Finally, the elbow method is introduced to determine the hyperparameters of the clustering algorithm, overcoming the dependence on subjective manual settings for cluster centers and achieving adaptive clustering based on data features. SC-based SACM is an unsupervised clustering algorithm specifically designed for piping hazard segmentation in UAV thermal infrared imagery. Unlike traditional clustering algorithms, SC-based SACM employs a progressive and adaptive strategy to iteratively refine the segmentation process. By leveraging temperature field analysis and stepwise clustering, SC-based SACM effectively eliminates irrelevant background regions and accurately extracts piping hazard targets. This method is particularly robust in complex environments, ensuring high precision and reliability in hazard detection. The SC-based SACM algorithm is composed of three key steps: temperature field preprocessing, background removal, and stepwise adaptive clustering.