# HyperionSolarNet: Solar Panel Detection from Aerial Images

arXiv: https://arxiv.org/abs/2201.02107 · HTML: https://mlsanity.com/paper/2201.02107/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [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)
- [PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction](https://mlsanity.com/paper/1902.01453/) (2019)

## Built on by (most cited first, 8 of 8)

- [Towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://mlsanity.com/paper/2207.07466/) (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)
- [GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery](https://mlsanity.com/paper/2404.05180/) (2024)
- [Solar Panel Mapping via Oriented Object Detection](https://mlsanity.com/paper/2502.03592/) (2025)
- [SolarFormer: Multi-scale Transformer for Solar PV Profiling](https://mlsanity.com/paper/2310.20057/) (2023)
- [S3Former: Self-supervised High-resolution Transformer for Solar PV Profiling](https://mlsanity.com/paper/2405.04489/) (2024)
- [When Bigger is Worse: A Practitioner's Guide to Model Selection Under Data Scarcity](https://mlsanity.com/paper/2603.02142/) (2026)
- [Nationally Consistent, Locally Incomplete: A Bayesian Remote-Sensing Audit of Rooftop Photovoltaic Registries](https://mlsanity.com/paper/2609.16294/) (2026)

## Abstract (arXiv)

With the effects of global climate change impacting the world, collective efforts are needed to reduce greenhouse gas emissions. The energy sector is the single largest contributor to climate change and many efforts are focused on reducing dependence on carbon-emitting power plants and moving to renewable energy sources, such as solar power. A comprehensive database of the location of solar panels is important to assist analysts and policymakers in defining strategies for further expansion of solar energy. In this paper we focus on creating a world map of solar panels. We identify locations and total surface area of solar panels within a given geographic area. We use deep learning methods for automated detection of solar panel locations and their surface area using aerial imagery. The framework, which consists of a two-branch model using an image classifier in tandem with a semantic segmentation model, is trained on our created dataset of satellite images. Our work provides an efficient and scalable method for detecting solar panels, achieving an accuracy of 0.96 for classification and an IoU score of 0.82 for segmentation performance.