2022/12/02 by Maxim Khomiakov, Julius Holbech Radzikowski, Khomiakov, Maxim +9
Computer Science · Engineering · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #Energy and Environment Impacts #FOS: Computer and information sciences #Machine Learning (cs.LG) #Remote-Sensing Image Classification #Solar Radiation and Photovoltaics
paper · pdf · doi:10.48550/arxiv.2212.01260
openalex publication_date 2022/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/.