# Solar Panel Mapping via Oriented Object Detection

arXiv: https://arxiv.org/abs/2502.03592 · HTML: https://mlsanity.com/paper/2502.03592/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 5 Feb 2025.

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

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Arbitrary-Oriented Scene Text Detection via Rotation Proposals](https://mlsanity.com/paper/1703.01086/) (2017)
- [HyperionSolarNet: Solar Panel Detection from Aerial Images](https://mlsanity.com/paper/2201.02107/) (2022)
- [Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery](https://mlsanity.com/paper/1801.04018/) (2018)
- [SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagery](https://mlsanity.com/paper/1912.03685/) (2019)
- [SolarDK: A high-resolution urban solar panel image classification and localization dataset](https://mlsanity.com/paper/2212.01260/) (2022)

## Built on by (most cited first, 2 of 2)

- [InspectVLM: Unified in Theory, Unreliable in Practice](https://mlsanity.com/paper/2508.01921/) (2025)
- [Aerial Infrared Health Monitoring of Solar Photovoltaic Farms at Scale](https://mlsanity.com/paper/2503.02128/) (2025)

## Abstract (arXiv)

Maintaining the integrity of solar power plants is a vital component in dealing with the current climate crisis. This process begins with analysts creating a detailed map of a plant with the coordinates of every solar panel, making it possible to quickly locate and mitigate potential faulty solar panels. However, this task is extremely tedious and is not scalable for the ever increasing capacity of solar power across the globe. Therefore, we propose an end-to-end deep learning framework for detecting individual solar panels using a rotated object detection architecture. We evaluate our approach on a diverse dataset of solar power plants collected from across the United States and report a mAP score of 83.3%.