  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/.
