Automatic Electrical energy plays a critical role in the economic and technological development of modern societies. High voltage lines are utilized for the efficient distribution of this energy. These lines enhance energy efficiency and sustainability by minimizing energy transmission losses. Ensuring the safe and uninterrupted transmission of electrical energy is of paramount importance for the robustness and maintenance of the infrastructure. Consequently, the role of high-voltage poles in the effective and efficient transmission of energy is of paramount importance. The objective of this study is to utilize remote sensing and various shallow learning techniques to identify high voltage line poles in the vicinity of Batman. The Sentinel-1 Synthetic Aperture Radar (SAR) satellite was utilized in this study. Sentinel-1 synthetic aperture radar (SAR) imagery was processed on the Google Earth Engine (GEE) platform to derive backscatter information for infrastructure and terrain analysis. Dual-polarization data (VV and VH) from both ascending and descending orbits were acquired between January and June 2025. Standard preprocessing steps, including thermal noise removal, radiometric calibration, and terrain correction using the SRTM 30 m digital elevation model, were automatically applied through the GEE Sentinel-1 GRD pipeline. Median composites were generated to minimize speckle noise and temporal variability. The derived VV and VH backscatter coefficients were subsequently used to extract spatial texture and backscatter difference features that characterize electrical pylons and transformer areas. The dataset was composed of VH (Vertical-Horizontal) and VV (Vertical-Vertical) polarization modes. The analysis of these datasets was conducted using algorithms belonging to the supervised learning model, which included the support vector machine, KNN, decision tree, quadratic discriminant, and naive Bayes algorithms. The findings indicate that the support vector machine model demonstrated an 85.0% success rate, the quadratic discriminant model exhibited an 82.5% success rate, the KNN model achieved an 82.2% success rate, the decision tree model attained a 76.8% success rate, and the naive Bayes model registered a 74.0% success rate. The study demonstrates the efficacy of artificial intelligence in facilitating more precise, expeditious, and systematic control of high-voltage poles. In light of the fact that conventional methods tend to be both time-consuming and costly when it comes to the maintenance of high voltage lines, the objective of this study is to enhance the efficiency of the energy infrastructure by means of contributing to the improvement of the monitoring and maintenance processes of power transmission lines.
Sarikaya et al. (Thu,) studied this question.