| Summary: | Having a complete and high-quality geospatial catalogue of existing large-scale photovoltaic (PV) panels is very important nowadays, due to the rapid increase in the use of this type of installations. This catalogue could be used to estimate, with a higher level of granularity, the energy that can be produced from solar radiation forecasts, to operate the electricity system efficiently by adjusting supply to demand, as well as for fiscal reasons. However, most existing works in the specialized literature focus on the extraction of PV panels in reduced, favourable scenes. In this study, a processing strategy to obtain PV panel arrays geometries from aerial orthoimages at a very large scale, is proposed. The processing strategy includes operations for PV panel array classification and semantic extraction, and algorithmic improvement and simplification of the vectorization results. The processing workflow was trained and tested on two datasets containing aerial images of 256 × 256 pixels with the studied polygonal feature at two different spatial resolutions (with more than 185,000 and 690,000 images for spatial resolutions of 0.5 m and 0.25 m, respectively). The image tiles were labelled at pixel level with PV panel information for both spatial resolutions and cover representative, extended urban and rural regions of Spain. The accuracies achieved in the PV panel arrays classification are superior to 0.999, while the semantic segmentation performance is superior to 0.90 in the Intersection over Union score. The assessment of the proposed procedure of fifteen new, unseen areas distributed over the Spanish territory (covering approximately 8125 km2) demonstrated its suitability (in the analysis, percentage of incompletely detected installations of only 11 % were achieved) and indicated that future works focused on the improvement of the initial predictions are should be carried out.
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