# Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping

arXiv: https://arxiv.org/abs/2207.07466 · HTML: https://mlsanity.com/paper/2207.07466/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [HyperionSolarNet: Solar Panel Detection from Aerial Images](https://mlsanity.com/paper/2201.02107/) (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)

## Built on by (most cited first, 7 of 7)

- [A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata](https://mlsanity.com/paper/2209.03726/) (2022)
- [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)
- [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)
- [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 is rapidly growing and key to mitigating the energy crisis. However, distributed PV generation, which amounts to half of the PV installed capacity, is typically unavailable to transmission system operators (TSOs), making it increasingly difficult to balance the load and supply and avoid grid congestions. To assess distributed PV generation, TSOs need precise knowledge regarding the metadata of distributed PV installations. Many remote sensing-based approaches have been proposed to map these installations in recent years. However, to use these methods in industrial processes, assessing their accuracy over the mapping area, i.e., the area covered by the model during deployment, is necessary. We define the downstream task accuracy (DTA) as the accuracy over the mapping area, automatically computed using publicly available data sources and the model's outputs and expressed in an interpretable way for operators. We benchmark existing models for distributed PV mapping and show how they perform in terms of DTA. We show that the accuracy computed on the test set overestimates by about 30 percentage points the accuracy on the mapping area. Our approach paves the way for safer integration of deep-learning-based pipelines for remote PV mapping. Code is available at https://github.com/gabrielkasmi/deeppvmapper.