  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
