Wed. Sep 27th, 2023
Solar Panel Detection Dataset for Sustainable Energy Monitoring

Effectively supporting the United Nations’ Sustainable Development Goals requires reliable, substantial, and timely data. However, the documentation for solar panel installations, which play a key role in green energy production and sustainable energy access, remains inconsistent. To address this issue, we have created a solar panel dataset derived from satellite imagery to facilitate accurate and rapid detection at regional and international scales.

Advancements in remote sensing technology have made it possible to collect valuable information through satellite imagery, supporting the goals set by the United Nations. Small target detection, in particular, has become crucial in addressing the Sustainable Development Goals, such as affordable and clean energy and actions to combat climate change.

Solar panel detection and monitoring can greatly benefit from high-resolution satellite imagery. However, existing datasets often focus on large-scale installations, limiting their applicability to residential installations. Our dataset includes annotations of primarily residential solar panels, providing a valuable resource for developing detection models that can be applied to different types of imagery.

By utilizing this dataset, researchers and policy-makers can make more informed decisions regarding renewable energy access and combatting climate change. The dataset can be used independently or in conjunction with other datasets to develop robust detection models that generalize well across different types of imagery.

In addition to the native resolution satellite imagery, our dataset also includes high-definition imagery, allowing for studies on the effects of spatial resolution on small object detection. This comprehensive dataset, comprising annotated solar panels and corresponding imagery, is a unique resource for the research community.

To facilitate the use of this dataset, we have provided the images and labels in a compatible format for the You Only Look Twice version 4 (YOLTv4) object detection framework. The dataset is openly available and can be accessed on GitHub.

By utilizing this solar panel detection dataset, we can improve the monitoring and reporting of residential-scale renewable energy installations, contributing to the achievement of global sustainable energy goals.