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 localising solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported values from the Danish national building registry. We explore performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach. Furthermore we train models of newer architectures, and provide benchmark baselines to our dataset in a number of different scenarios. We believe the release of this dataset may improve future research in both local and global geospatial domains for the identification and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/RELEASED-AT-TIME-OF-PUBLICATION