GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery

TL;DR

This paper introduces GloSoFarID, a comprehensive global multispectral satellite dataset of solar farms designed to enable machine learning models for accurate solar farm identification and monitoring worldwide.

Contribution

It presents the first extensive global multispectral satellite dataset for solar farm identification, facilitating improved machine learning-based monitoring of solar energy infrastructure.

Findings

Dataset enables accurate solar farm detection models

Supports global analysis of solar farm distribution

Enhances monitoring for sustainable energy planning

Abstract

Solar Photovoltaic (PV) technology is increasingly recognized as a pivotal solution in the global pursuit of clean and renewable energy. This technology addresses the urgent need for sustainable energy alternatives by converting solar power into electricity without greenhouse gas emissions. It not only curtails global carbon emissions but also reduces reliance on finite, non-renewable energy sources. In this context, monitoring solar panel farms becomes essential for understanding and facilitating the worldwide shift toward clean energy. This study contributes to this effort by developing the first comprehensive global dataset of multispectral satellite imagery of solar panel farms. This dataset is intended to form the basis for training robust machine learning models, which can accurately map and analyze the expansion and distribution of solar panel farms globally. The insights gained from this endeavor will be instrumental in guiding informed decision-making for a sustainable energy future. https://github.com/yzyly1992/GloSoFarID