A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

TL;DR

This paper introduces a comprehensive, annotated aerial imagery dataset with installation metadata for over 28,000 photovoltaic arrays, aiming to improve machine learning models for PV mapping across different regions.

Contribution

The paper presents a large, diverse dataset of aerial images, annotations, and metadata for PV installations, addressing domain shift issues in PV mapping models.

Findings

Provides ground truth segmentation masks for 13,000 installations.

Includes installation metadata for over 8,000 installations.

Facilitates development of robust PV mapping pipelines.

Abstract

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.