## 3 Use Case: (Com)2 INet for Levee Piping Hazard Inspection via Remote Sensing
Each step can be modeled as a VNF and chained into an SC, which can be executed sequentially on multiple computing devices to achieve the scalable levee piping hazard inspection. The SC-based SACM algorithm is designed to overcome challenges such as interference from complex levee environments, variability in thermal infrared imagery due to weather conditions, and the subjectivity of manual parameter selection. By introducing the elbow method for hyperparameter determination, the SC-based SACM achieves fully adaptive clustering based on data characteristics, without requiring predefined cluster centers. The specific process is illustrated as Figure 5, and the main pseudo-code is as shown as Algorithm 1. **Input**: UAV thermal infrared imagery \(I\); Convergence threshold \(\epsilon\); Maximum iterations \(T\). **Process**:  
1. **Temperature Field Preparation**:  
**. **: Convert the thermal infrared imagery \(I\) into a temperature map \(T\) using the thermal infrared radiative transfer mechanism. **. **: Normalize the temperature map \(T\) to ensure consistent data scaling across different images. 2. **Background Removal**:  
**. **: Perform a series of binary segmentations on the temperature map \(T\) to isolate potential piping hazard regions:  
**. **: Calculate the temperature threshold \(\tau\) for binary segmentation using statistical analysis of the temperature distribution. **. **: Segment the image into background regions and suspected target regions based on \(\tau\). **. **: Iteratively refine the segmentation by updating \(\tau\) to progressively exclude irrelevant background regions. 3. **Stepwise Adaptive Clustering**:  
**. **: Apply adaptive clustering to the refined target regions:  
**. **: Use the elbow method to determine the optimal number of clusters \(k\) based on the within-cluster sum of squares (WCSS)  
**. **: Initialize cluster centroids adaptively based on the temperature distribution of the target regions. **. **: Perform iterative clustering to assign each data point to the nearest cluster and update cluster centroids. **. **: Continue the clustering to assign each data point to the nearest cluster and update cluster centroids. 4. **Output**:  
**. **: Extracted piping hazard regions with precise boundaries. **. **: Cluster assignments \(U=\{u_{1},u_{2},\ldots,u_{k}\}\) representing the segmented target areas. (i) _Land surface temperature retrieval from UAV thermal infrared imagery_. Utilizing thermal infrared (TIR) imagery acquired by UAVs, the land surface temperature is retrieved according to the thermal infrared radiative transfer mechanism. Specifically, for UAV-based single-band TIR imagery, the land surface temperature \(\mathbb{T}_{s}\) is estimated using the radiative transfer equation. \[L_{\lambda}=\varepsilon_{\lambda}\mathbb{B}_{\lambda}(\mathbb{T}_{s})\tau_{ \lambda}+(1-\varepsilon_{\lambda})\mathbb{L}_{\lambda}^{\downarrow}\tau_{ \lambda}+\mathbb{L}_{\lambda}^{\uparrow}, \tag{3}\]  
where \(\mathbb{L}_{\lambda}\) is the at-sensor radiance, \(\varepsilon_{\lambda}\) is the surface emissivity (set to 0.95), \(\mathbb{B}_{\lambda}(\mathbb{T}_{s})\) is the blackbody radiance at surface temperature \(\mathbb{T}_{s}\), \(\tau_{\lambda}\) is the atmospheric transmittance, \(\mathbb{L}_{\lambda}^{\uparrow}\) is the upwelling atmospheric radiance, and \(\mathbb{L}_{\lambda}^{\downarrow}\) is the downwelling atmospheric radiance. The parameters are set based on sensor calibration and local atmospheric conditions. After inversion, the resulting temperature map \(\mathbb{T}\) is normalized to the range \([0,1]\) using the following formula. \[\mathbb{T}_{norm}=\frac{\mathbb{T}-\mathbb{T}_{min}}{\mathbb{T}_{max}- \mathbb{T}_{min}}, \tag{4}\]  
Figure 5: Illustration of SC-based SACM for levee piping hazard inspection via remote sensing. where \(\mathbb{T}_{min}\) and \(\mathbb{T}_{max}\) are the minimum and maximum pixel values in the temperature map, respectively.