## 3 Use Case: (Com)2 INet for Levee Piping Hazard Inspection via Remote Sensing
This normalization ensures consistent data scaling across different images and provides a solid foundation for subsequent analysis. (ii) _Levee piping temperature field analysis for image background removal_. Based on the retrieved temperature map, the thermal characteristics of piping targets are analyzed. To separate the image into background and suspected target regions, a statistical thresholding method is employed. The initial segmentation threshold is calculated as below. \[\tau=\mu_{\mathbb{T}}+k\cdot\rho_{\mathbb{T}}, \tag{5}\]  
where \(\mu_{\mathbb{T}}\) is the mean temperature, \(\rho_{\mathbb{T}}\) is the standard deviation of the temperature distribution, and \(k\) is an empirical parameter (typically set to 1.5 based on experiments). Binary segmentation is performed using this threshold to isolate potential piping hazard regions. The process is iteratively refined: after each segmentation, the threshold \(\tau\) is updated according to the statistics of the remaining suspected target region. The iteration continues until the difference between the mean temperatures of the background and target regions falls below a predefined threshold (e.g., 1.5 K). This step effectively removes irrelevant background areas, reducing interference for subsequent clustering analysis. (iii) _Feature construction using stepwise adaptive clustering_. Following background removal, stepwise adaptive clustering is employed to construct the thermal field features of the piping targets. The number of clusters \(k\) for the suspected target region is determined by the elbow method, which calculates the within-cluster sum of squares (WCSS) for a range of \(k\) values and selects the point where the decrease in WCSS becomes marginal, indicating the optimal cluster number. Initial cluster centroids are selected based on the peaks of the temperature histogram within the target region to enhance clustering stability. This process further refines the segmented target regions to extract suspected piping areas, avoiding the subjectivity associated with manual threshold selection. The SC-based SACM approach for levee piping hazard inspection is particularly robust and adaptable for extracting piping targets in complex scenarios, ensuring high scalability and accuracy in feature construction. **Benchmarks. ** (1) _k-Medoids_ (KM) algorithm is a classical partitioning clustering algorithm widely used in data mining. Unlike k-Means, which relies on centroids as cluster representatives, KM selects actual data points (medoids) to represent clusters, making it more robust to noise and outliers. The algorithm aims to minimize the sum of dissimilarities between data points and their corresponding medoids, ensuring the formation of compact and well-separated clusters. The KM algorithm is particularly suitable for scenarios with non-Euclidean distance metrics or when the dataset contains outliers that could significantly distort the results of k-Means. By iteratively refining the medoids and cluster assignments, KM seeks to achieve optimal clustering with respect to the chosen dissimilarity measure. Because of the global dependence on the central object, the KM cannot be split and can only be executed as a whole function on a separate computing device. The main pseudo-code is as shown as Algorithm 2. (2) _Fuzzy C-Means_ (FCM) is a widely used unsupervised clustering algorithm based on fuzzy set theory. Unlike traditional hard clustering methods, such as k-Means, where each data point is assigned to exactly one cluster, FCM allows a data point to belong to multiple clusters with varying degrees of membership. This flexibility makes FCM particularly suitable for applications where data points exhibit overlapping characteristics or uncertainty in cluster boundaries. The objective of FCM is to minimize the weighted sum of squared errors between data points and cluster centers, with the weights determined by the membership degrees. The algorithm iteratively updates cluster centers and membership degrees until convergence.