# Automatic Detection of Solar Photovoltaic Arrays in High Resolution Aerial Imagery

arXiv: https://arxiv.org/abs/1607.06029 · HTML: https://mlsanity.com/paper/1607.06029/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2016.

## Built on by (most cited first, 14 of 14)

- [Segment anything, from space?](https://mlsanity.com/paper/2304.13000/) (2023)
- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata](https://mlsanity.com/paper/2209.03726/) (2022)
- [Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis](https://mlsanity.com/paper/2202.12939/) (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)
- [GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery](https://mlsanity.com/paper/2101.06390/) (2021)
- [An Artificial Intelligence Dataset for Solar Energy Locations in India](https://mlsanity.com/paper/2202.01340/) (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)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [S3Former: Self-supervised High-resolution Transformer for Solar PV Profiling](https://mlsanity.com/paper/2405.04489/) (2024)
- [Deformable Attention Mechanisms Applied to Object Detection, case of Remote Sensing](https://mlsanity.com/paper/2505.24489/) (2025)
- [Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery](https://mlsanity.com/paper/2608.10801/) (2026)

## Abstract (arXiv)

The quantity of small scale solar photovoltaic (PV) arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such arrays, including at a high spatial resolution (e.g., counties, cities, or even smaller regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV array location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135 km^2) collection of publicly available [1] aerial imagery, with over 2,700 human annotated PV array locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV array detection, but with significant potential for improvement in estimating the precise shape/size of the PV arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.