Optimizing PV Panel Segmentation in Complex Environments Using Pre-Training and Simulated Annealing Algorithm: The JSWPVI

Photovoltaic (PV) technology, as a crucial source of clean energy, can effectively mitigate the impact of climate change caused by fossil fuel-based power generation. However, improper use of PV installations may encroach upon agricultural land, grasslands, and other land uses, thereby affecting local ecosystems. Exploring the spatial characteristics of centralized or distributed PV installations is essential for quantifying the development of clean energy and protecting agricultural land. Due to the distinct characteristics of centralized and distributed PV installations, large-scale mapping methods based on satellite remote sensing are insufficient for creating detailed PV distribution maps. This study proposes a model called Joint Semi-Supervised Weighted Adaptive PV Panel Recognition Model (JSWPVI)to achieve reliable PV mapping using UAV datasets. The JSWPVI employs a semi-supervised approach to construct and optimize a comprehensive segmentation network, incorporating the Spatial and Channel Weight Adaptive Model (SCWA) module to integrate different feature layers by reconstructing the spatial and channel weights of feature maps. Finally, a guided filtering algorithm is used to minimize non-edge noise while preserving edge integrity. Our results demonstrate that JSWPVI can accurately extract PV panels in both centralized and distributed scenarios, with an average extraction accuracy of 91.1% and a mean Intersection over Union of 77.7%. The findings of this study will assist regional policymakers in better quantifying renewable energy potential and assessing environmental impacts.