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

arXiv: https://arxiv.org/abs/2209.03726 · HTML: https://mlsanity.com/paper/2209.03726/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data](https://mlsanity.com/paper/2104.07778/) (2021)
- [Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery](https://mlsanity.com/paper/1607.06029/) (2016)
- [Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://mlsanity.com/paper/2207.07466/) (2022)
- [Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators](https://mlsanity.com/paper/1810.04881/) (2018)
- [What you get is not always what you see: pitfalls in solar array assessment using overhead imagery](https://mlsanity.com/paper/1902.10895/) (2019)

## Built on by (most cited first, 9 of 9)

- [Assessment of the Reliablity of a Model's Decision by Generalizing Attribution to the Wavelet Domain](https://mlsanity.com/paper/2305.14979/) (2023)
- [Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems](https://mlsanity.com/paper/2408.07828/) (2024)
- [SolarDK: A high-resolution urban solar panel image classification and localization dataset](https://mlsanity.com/paper/2212.01260/) (2022)
- [Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems?](https://mlsanity.com/paper/2309.12214/) (2023)
- [SolarFormer: Multi-scale Transformer for Solar PV Profiling](https://mlsanity.com/paper/2310.20057/) (2023)
- [OpenPVMapper: A Multi-source, Nationwide Database of Rooftop Photovoltaic Systems in France](https://mlsanity.com/paper/2607.25153/) (2026)
- [S3Former: Self-supervised High-resolution Transformer for Solar PV Profiling](https://mlsanity.com/paper/2405.04489/) (2024)
- [When Bigger is Worse: A Practitioner's Guide to Model Selection Under Data Scarcity](https://mlsanity.com/paper/2603.02142/) (2026)
- [Nationally Consistent, Locally Incomplete: A Bayesian Remote-Sensing Audit of Rooftop Photovoltaic Registries](https://mlsanity.com/paper/2609.16294/) (2026)

## Abstract (arXiv)

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.