A robust and transferable AI workflow for segmenting ground-mounted Photovoltaic Systems

Abstract. The given contribution describes an efficient artificial intelligence (AI) workflow for the detection and segmentation of ground-mounted photovoltaic (PV) systems in Bavaria (Germany), which can be transferred to any region. A two-stage approach was developed based on digital orthophotos (DOP) with a resolution of 20 cm (DOP20) or 100 cm (DOP100). Two different AI models, U-Net and YOLO, are used to identify and segment PV systems. The combined approach, which first analyses low-resolution DOP100 images and then uses targeted high-resolution DOP20 tiles, increases efficiency, by processing only relevant image areas with high resolution. Initial tests in three Bavarian districts show a high level of accuracy for both AI models. The approach is designed to be used for area-wide segmentation in Bavaria and thus contribute to change detection and quality assurance of the Digital Basic Landscape Model (ATKIS®Base-DLM). Furthermore, the generalisation capability of the workflow was validated using an independent high-resolution dataset from the Piedmont region in Italy, where the models achieved promising recognition rates even without applying the post-processing pipeline.