Back in time: A novel time series and deep learning framework for mapping solar installations

Globally, solar photovoltaic installations have been increasing at an exponential rate. This trend towards renewable energy is expected to continue for the foreseeable future to reduce greenhouse gas emissions. However, with the expansion of energy comes a greater land use footprint to accommodate this new infrastructure, i.e., energy sprawl. Given the effect of land conversion on habitat loss and fragmentation, it is imperative to better understand where large-scale solar installations are in our landscape. This study presents a new framework for combining deep learning and time series analysis to map and date large-scale solar installations in a complex temperate landscape. We first employed a U-Net model on a synthetic clear Sentinel-2 image to accurately classify solar facilities, achieving a user's accuracy of 97.33 % and a producer's accuracy of 100.00 %. Leveraging the extensive temporal coverage of Landsat data, we then implemented a reverse application of the Continuous monitoring of Land Disturbance (COLD) algorithm. By running COLD backward in time (2022–1985) over classified solar areas, we exploited the temporal stability of these features to reconstruct installation dates. This method proved to be highly accurate, with a mean difference of 0.125 years between the reference year and COLD-detected installation year. This study provides a high-quality solar dataset for Connecticut as of June 2021 and validates a novel framework with demonstrated potential for application in other regions. These results provide valuable information for land use planning and environmental impact assessment, as well as a powerful proof-of-concept for a methodology that can be used in future solar documentation work.