## 3 Use Case: (Com)2 INet for Levee Piping Hazard Inspection via Remote Sensing
Unlike classical methods that cluster the entire image and require a preset cluster number, SACM first eliminates irrelevant background and then adaptively determines the optimal number of clusters based on data characteristics. This reduces the influence of noise and background interference, leading to more accurate and robust extraction of piping hazard regions. Additionally, SACM's initialization based on thermal histogram peaks further enhances clustering stability. These domain-specific adaptations enable SACM to outperform general-purpose algorithms, especially in complex and noisy scenarios, as demonstrated in our comparative results. In this study, a comprehensive set of evaluation metrics--including precision, recall, accuracy, and F1-score--was used to assess the performance of the proposed SACM algorithm and several classical clustering algorithms (KM, GMM, FCM). As shown in Table 3, the SACM algorithm achieved a recall of 0.70 and an F1-score of 0.26, which are significantly higher than those of the benchmark algorithms (recall: 0.06-0.07, F1-score: 0.11-0.12). This demonstrates SACM's strong ability to identify true hazard regions and minimize missed detections, which is critical in practical levee piping hazard inspection scenarios, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Piping Number** & **KM** & **GMM** & **FCM** & **SACM** \\ \hline
1 & 0.0015 & 0.0266 & 0.0000 & 0.4694 \\
2 & 0.0173 & 0.0078 & 0.0019 & 0.0198 \\
3 & 0.0157 & 0.0013 & 0.0148 & 0.1094 \\
4 & 0.0546 & 0.0600 & 0.0604 & 0.1662 \\
5 & 0.1016 & 0.0019 & 0.0908 & 0.0741 \\
6 & 0.0712 & 0.0788 & 0.0747 & 0.0755 \\
7 & 0.0213 & 0.0182 & 0.0000 & 0.0165 \\
8 & 0.0550 & 0.0208 & 0.0000 & 0.2441 \\ Mean & 0.0423 & 0.0269 & 0.0303 & 0.1469 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of target extraction (mIoU). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Piping Number** & **KM** & **GMM** & **FCM** & **SACM** \\ \hline
1 & 6.9079 & 99.7843 & 227.2507 & 0.6001 \\
2 & 6.21663 & 93.2404 & 214.3872 & 0.1907 \\
3 & 5.6533 & 83.7551 & 217.9142 & 0.2122 \\
4 & 6.6272 & 101.6670 & 203.7636 & 0.1717  
where missing a hazard could have severe consequences. Although SACM's precision (0.16) is lower than that of the other methods, this trade-off is acceptable in safety-critical applications, as a higher recall is generally preferred to ensure that as many true hazards as possible are detected. The high overall accuracy (0.96-0.97) across all methods reflects the class imbalance in the dataset, where non-hazard regions dominate. Therefore, precision, recall, and F1-score provide a more meaningful evaluation than accuracy alone. The main strength of SACM lies in its domain-adaptive design, which effectively handles background noise and subtle thermal anomalies in UAV imagery. Its high recall and F1-score indicate robust practical applicability for hazard identification. However, the relatively low precision suggests that further optimization may be needed to reduce false positives, especially in large-scale deployments. Future work will focus on enhancing precision by incorporating additional contextual or spatial information. **Implementing Levee Piping Hazard Inspection on the (Com)\({}^{2}\)INet Platform**. First, the TIR images captured by UAVs and transmitted using a multi-path transmission mechanism that adaptively integrates a space-based network, air-based network, and ground-based network to route data to the (Com)\({}^{2}\)INet platform. Furthermore, the network state is monitored by a network observer module installed inside the centralized SDN controller through a northbound API and an in-band network telemetry (INT) collector. Based on the observations, the serverless platform of (Com)\({}^{2}\)INet constructs a SC-based SACM for the arrival service of levee piping hazard inspection, and schedules resources from a heterogeneous infrastructure pool.