Performance Evaluation of Mini-UAV Photogrammetry for Rooftop Area Measurement

This study evaluates the efficacy of mini unmanned aerial vehicle (UAV) photogrammetry for measuring rooftop areas by integrating UAV-derived data with high-precision terrestrial surveying data. The research conducted at Uttaradit Rajabhat University in Uttaradit province, Thailand, included the extraction of 38 selected rooftop footprint samples from a 2D orthomosaic image and a 3D Digital Surface Model (DSM). The findings indicate that both measurement approaches exhibit high precision in comparison to ground surveying, with the 2D approach achieving 98.63 % accuracy and the 3D approach achieving 95.00% accuracy. Both methods demonstrated a strong positive correlation with ground-truth data (correlation coefficient = 0.99). The 2D method exhibited lower error metrics, with a Mean Absolute Error (MAE) of 2.14 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a Mean Relative Error (MRE) of 1.36%, compared to the 3D method's MAE of 6.42 m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and MRE of 4.99%. The use of Real- Time Kinematic (RTK) positioning significantly improved measurement accuracy. This study confirms that UAV photogrammetry provides reliable, efficient, and precise data comparable to terrestrial surveying for measuring rooftop areas. These findings present significant insights for applications in urban planning, construction, and planning and installation of renewable energy systems, including solar panel positioning and rooftop photovoltaic panel installation.