TEMCA-Net: A Texture-Enhanced Deep Learning Network for Automatic Solar Panel Extraction in High Groundwater Table Mining Areas

Abstract

Long-term coal mining has led to a series of ecological problems, constraining society's sustainable development. Ecological restoration is a crucial component of achieving sustainability, and with the continuous advancement of photovoltaic technology, the comprehensive utilization of photovoltaics has become one of the important restoration methods in mining areas. The area and location of solar panels, as key indicators for assessing the ecological restoration approach, require precise extraction and positioning. This article proposes a texture-enhanced multicontext attention network (TEMCA-Net). In the encoding part, the network utilizes residual connections in conjunction with the convolutional block attention module to preliminarily extract contextual information. Then, low-level features were input into the statistical texture learning (STL) texture enhancement module and high-level features into the horizontal atrous spatial pyramid pooling (H-ASPP) module. In the decoding part, the high-level features processed by the H-ASPP were combined module with the texture-enhanced features from the STL module. Experiments were conducted in the Peibei mining region located in Xuzhou City, Jiangsu Province. We established the solar panels of Peibei mining region (SPPMR) dataset. The experimental results on the SPPMR dataset demonstrate TEMCA-Net's outstanding performance in solar panel extraction, with precision at 90.24%, recall at 93.07%, an F1-score of 91.63%, and a mean intersection over union of 92.21%. It significantly outperforms three classic deep learning networks: Deeplabv3+, U-net, and PSPnet. In summary, this study provides an efficient and feasible solution for the extraction of solar panels in mining areas with high water tables.

Keywords

• Semantic segmentation
• solar panel extraction
• texture enhancement