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

Year: 2026

Type: article

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.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/e3sconf/202670803012/pdf

Authors: Youssef Rissouni, Elhassan Jamal, Hicham Jamil, Rachid El Ansari, Jamal Chao, Aniss Moumen

Institution: Université Ibn-Tofail

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7160336010

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