An Artificial Intelligence Dataset for Solar Energy Locations in India
Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker,, Shivaprakash K Nagaraju, Caleb Robinson, Priyal Bhatia, Aditi Khurana, Jane, Wang, Felipe Oviedo, Juan Lavista Ferres

TL;DR
This paper presents a machine learning model that uses satellite imagery to accurately map utility-scale solar farms across India, providing essential geo-spatial data to support land use planning and mitigate environmental conflicts.
Contribution
It introduces a novel spatially explicit machine learning approach to identify solar PV infrastructure in India using satellite data, validated by experts, with a comprehensive dataset of 1363 solar farms.
Findings
Mean model accuracy of 92% in mapping solar farms
Over 74% of solar development occurs on ecologically or agriculturally valuable land
Provides a new dataset for solar infrastructure in India
Abstract
Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental values is high. To expedite development of solar energy, land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. In this work, we developed a spatially explicit machine learning model to map utility-scale solar projects across India using freely available satellite imagery with a mean accuracy of 92%. Our model predictions were validated by human experts to obtain a dataset of 1363 solar PV farms. Using this dataset, we measure the solar footprint across India and quantified the degree…
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Taxonomy
TopicsEnergy and Environment Impacts · Solar Radiation and Photovoltaics · Energy, Environment, Economic Growth
