UAV-Photogrammetry, Deeplearning, and Structure From Motion (SFM) to Assess Solar Resources in Urban Areas

Abstract

This study introduces a high-resolution approach for assessing solar energy potential in urban areas using UAV-based photogrammetry and Structure-from-Motion (SfM) techniques. Digital Surface Models (DSMs) at 4 cm, 10 cm, 50 cm, and 100 cm resolutions were analyzed to evaluate the impact of spatial resolution on solar irradiance estimations. Utilizing ESRI's Solar Analyst tool, incident solar radiation was calculated, revealing that finer resolutions yield greater accuracy, with a 15% reduction in solar potential estimates at 100 cm compared to 4 cm. These results underscore the importance of high-resolution DSMs, particularly for complex rooftop geometries where coarser resolutions fail to capture critical shading effects. This scalable methodology has substantial implications for photovoltaic optimization in urban planning.