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 develop a machine learning model to map utility-scale solar projects across India using freely available satellite imagery. Model predictions were validated by human experts to obtain a total of 1438 solar farms. We also estimate the solar footprint across India and quantified the degree of land modification associated with land cover types that may cause conflicts. Our analysis indicates that over 74% of solar development in India was built on landcover types that have natural ecosystem preservation, and agricultural values. Our work increases the feasibility of long-term monitoring of renewable energy deployment targets.