Optimizing Photovoltaic Detection in High-Resolution Satellite Imagery Using GIS, DeepLabv3+, and Transformer-Based Models: A Case Study of the Marrakesh-Safi Region

Abstract. Solar energy has become a major contributor to global renewable energy strategies, offering a sustainable alternative to fossil fuels. Photovoltaic (PV) systems, which convert sunlight into electricity, play a central role in this transition. As the demand for large-scale solar energy projects grows, Geographic Information Systems (GIS) and advanced deep learning models have become critical for accurately detecting and mapping PV installations, particularly from satellite imagery. However, challenges remain, especially in regions with suboptimal satellite data quality. This study focuses on the Marrakesh-Safi region of Morocco, where the potential for solar energy is high but hindered by limitations in available satellite imagery. We employ advanced transformer-based models, including Mask2Former, SegFormer, and DeepLabV3+, to enhance the semantic segmentation of PV systems from high-resolution satellite images. By integrating GIS with these deep learning models, we aim to improve the accuracy and scalability of PV detection, even in complex and diverse geographical settings. Our methodology involves training and testing these models on annotated satellite imagery, with performance evaluated using key metrics such as Intersection over Union (IoU), precision, recall, and F1 score. Mask2Former achieved notable results with a recall of 0.95 and an F1 score of 0.936, excelling in the detection of smaller and more complex PV layouts. DeepLabV3+ demonstrated strong overall performance, with an IoU of 0.89 and precision of 0.93, while also being the most computationally efficient model, processing 28 samples per second. This research highlights the effectiveness of integrating GIS with deep learning, particularly transformer-based architectures, for the accurate detection and mapping of PV systems. The results contribute to the broader efforts in renewable energy optimization, supporting more efficient solar energy deployment, especially in regions like Morocco where data quality poses significant challenges.