| Issue |
E3S Web Conf.
Volume 708, 2026
7th International Conference on Smart Applications and Water Information Systems: “Intelligent Systems, Geospatial Technologies and Modeling for the Sustainable Management of Water Resources” (SAWIS 2025)
|
|
|---|---|---|
| Article Number | 03012 | |
| Number of page(s) | 17 | |
| Section | GIS, AI Applications, and Risk Assessment | |
| DOI | https://doi.org/10.1051/e3sconf/202670803012 | |
| Published online | 30 April 2026 | |
UAV-Photogrammetry, Deeplearning, and Structure From Motion (SFM) to Assess Solar Resources in Urban Areas
1 Laboratory of Engineering Sciences, National School of Applied Sciences, Ibn Tofaïl University, Kenitra 14000, Morocco.
2 Laboratory of Natural Resources and Sustainable Development, Ibn Tofaïl University, Kenitra 14000, Morocco.
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
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.
Key words: SFM / deep learning / solar analysis / building detection
© The Authors, published by EDP Sciences, 2026
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