# Nationally Consistent, Locally Incomplete: A Bayesian Remote-Sensing Audit of Rooftop Photovoltaic Registries

arXiv: https://arxiv.org/abs/2609.16294 · HTML: https://mlsanity.com/paper/2609.16294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 60 references that are in the map.
- First posted on arXiv 14 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata](https://mlsanity.com/paper/2209.03726/) (2022)
- [HyperionSolarNet: Solar Panel Detection from Aerial Images](https://mlsanity.com/paper/2201.02107/) (2022)
- [Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://mlsanity.com/paper/2207.07466/) (2022)
- [An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data](https://mlsanity.com/paper/2012.03690/) (2020)
- [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)
- [Monitoring Germany's Core Energy System Dataset: A Data Quality Analysis of the Marktstammdatenregister](https://mlsanity.com/paper/2304.10581/) (2023)
- [PyPVRoof: a Python package for extracting the characteristics of rooftop PV installations using remote sensing data](https://mlsanity.com/paper/2309.07143/) (2023)
- [OpenPVMapper: A Multi-source, Nationwide Database of Rooftop Photovoltaic Systems in France](https://mlsanity.com/paper/2607.25153/) (2026)

## Abstract (arXiv)

Tracking the energy transition requires reliable statistics on renewable deployment. Rooftop photovoltaics (PV) are especially hard to track, owing to their decentralised nature, and the resulting inaccuracies in official statistics are known but not quantified. Remote sensing offers an independent way to identify rooftop PV systems. We introduce a Bayesian framework to estimate the ground-truth rooftop PV capacity from remote sensing detections, turning an imperfect detector into an uncertainty-aware measurement instrument. Applied to France, the corrected detections estimate a capacity of 4.03 GWp [3.96--4.11] (99% credible interval) of rooftop PV below 36 kWp, matching the transmission system operator's connection data within 3.3% nationally, while identifying local under-reports of up to 61% of local capacity. We also document and quantify a significant truncation bias in French rooftop PV open data. Beyond France, the approach paves the way for more reliable estimates of rooftop PV capacity worldwide.