Solar Panel Mapping via Oriented Object Detection
Not on the map. Why?
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%.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 5 Feb 2025.
Builds on
8 of 15 references.
References · 8
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2017Deformable Convolutional Networkscited by 1,481
- 2017Arbitrary-Oriented Scene Text Detection via Rotation Proposalscited by 176
- 2022HyperionSolarNet: Solar Panel Detection from Aerial Imagescited by 8
- 2018Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagerycited by 4
- 2019SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagerycited by 4
- 2022SolarDK: A high-resolution urban solar panel image classification and localization datasetcited by 3
Built on by
Citing papers per year: 2025: 2.
2 papers.