  Photovoltaic (PV) energy generation plays a crucial role in the energy
transition. Small-scale PV installations are deployed at an unprecedented pace,
and their integration into the grid can be challenging since public authorities
often lack quality data about them. Overhead imagery is increasingly used to
improve the knowledge of residential PV installations with machine learning
models capable of automatically mapping these installations. However, these
models cannot be easily transferred from one region or data source to another
due to differences in image acquisition. To address this issue known as domain
shift and foster the development of PV array mapping pipelines, we propose a
dataset containing aerial images, annotations, and segmentation masks. We
provide installation metadata for more than 28,000 installations. We provide
ground truth segmentation masks for 13,000 installations, including 7,000 with
annotations for two different image providers. Finally, we provide installation
metadata that matches the annotation for more than 8,000 installations. Dataset
applications include end-to-end PV registry construction, robust PV
installations mapping, and analysis of crowdsourced datasets.
