Per-point processing for detailed urban solar estimation with aerial laser scanning and distributed computing

Abstract This paper presents a complete data processing pipeline for improved urban solar potential estimation by applying solar irradiation estimation directly to individual aerial laser scanning (ALS) points in a distributed computing environment. Solar potential is often measured by solar irradiation – the amount of the Sun’s radiant energy received at the Earth’s surface over a period of time. To overcome previous limits of solar radiation estimations based on either two-and-a-half-dimensional raster models or overly simplistic, manually-generated, geometric models, an alternative approach is proposed using dense, urban aerial laser scanning data to enable the incorporation of the true, complex, and heterogeneous elements common in most urban areas. The approach introduces a direct, per-point analysis to fully exploit all details provided by the input point cloud data. To address the resulting computational demands required by the thousands of calculations needed per point for a full-year analysis, a distributed data processing strategy is employed that introduces an atypical data partition strategy. The scalability and performance of the approach are demonstrated on a 1.4-billion-point dataset covering more than 2 km2 of Dublin, Ireland. The reliability and realism of the simulation results are rigorously confirmed with (1) an aerial image collected concurrently with the laser scanning, (2) a terrestrial image acquired from an online source, and (3) a four-day, direct solar radiation collection experiment.