Remote Sensing for Utility-Scale Solar Siting: A Novel AI-Assisted Approach for Infrastructure Optimization in High-Irradiance U.S. Regions

Title: Remote Sensing for Utility-Scale Solar Siting: A Novel AI-Assisted Approach for Infrastructure Optimization in High-Irradiance U.S. Regions Author: Vrushabhraj Tanawade (MountBay Energy LLC) Keywords: Remote sensing; solar energy; utility-scale siting; artificial intelligence; machine learning; ensemble models; GIS; high-irradiance regions; energy equity; climate-resilient infrastructure This paper introduces a scalable, empirically validated framework that leverages multisource remote sensing data and advanced AI modeling to optimize land siting for utility-scale solar energy projects. Against the backdrop of surging global energy demand, urgent climate imperatives, and rapid advances in artificial intelligence, our work delivers a high-impact contribution—demonstrating a 42% improvement in solar site screening efficiency and a 35% increase in investment accuracy compared to conventional GIS-based methods. Our results extend the literature at the intersection of geospatial analytics, machine learning, and renewable energy project development, offering a practical bridge between academic innovation and real-world industry deployment. In addition, our system advances policy goals such as energy equity, with specific reference to U.S. initiatives like the Justice40 program, by bringing high-renewable-potential zones into underserved communities—thereby enabling faster, fairer, and more resilient energy infrastructure growth. This manuscript is original, has not been published elsewhere, and is not under consideration by any other journal. All authors have read and approved the manuscript and consent to its submission.