# SolarDK: A high-resolution urban solar panel image classification and localization dataset

arXiv: https://arxiv.org/abs/2212.01260 · HTML: https://mlsanity.com/paper/2212.01260/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata](https://mlsanity.com/paper/2209.03726/) (2022)

## Built on by (most cited first, 3 of 3)

- [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)
- [Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets](https://mlsanity.com/paper/2402.12843/) (2024)

## Abstract (arXiv)

The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/.