ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geoinformatics
Volume 13 - 2025 | doi: 10.3389/feart.2025.1490356
This article is part of the Research TopicApplications of Remote Sensing Over Plateau Mountainous AreasView all 3 articles
Research on solar photovoltaic panel layout based on compressed LiDAR point cloud
Provisionally accepted
- 1School of Environment and Spatial Informatics, China University of Mining & Technology, Beijing, China
- 2Xuchang University, Jiangguanchi, China
- 3Kunming Metallurgy College, Kunming, Yunnan Province, China
- 4Yunnan Aerial Survey Technology Co., Ltd, Kunming, China
- 5Yunnan Institute of Water and Hydropower Engineering Investigation, Design, Kunming, Yunnan Province, China
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Solar photovoltaic (PV) panels convert solar energy into clean, renewable electricity, and their performance and sustainable use heavily depend on accurate terrain analysis and optimal site selection. While LiDAR technology provides dense point cloud data that offers precise terrain information, the high density and volume of the data pose significant challenges in processing efficiency. This study aims to generate high-resolution digital terrain models (DTMs) through point cloud compression and interpolation techniques, combined with multi-factor analysis to identify the optimal PV panel installation areas. The approach includes octree-based compression of the point cloud with an 80% compression rate, leveraging an elevation zoning strategy. Four interpolation methods-IDW, RBF, TPS, and EBK-are applied to generate the DTMs from the compressed data, with the EBK-generated DTM selected for terrain analysis. The results show that by integrating slope (S), aspect (AS), and maximum solar radiation (SR), optimal installation areas were identified in three test zones, measuring 82,360 m² , 302,462 m² , and 97,464 m² , respectively. The proposed framework ensures both the efficiency and accuracy of DTM generation and provides scientific support for PV site selection. By combining point cloud compression and multi-factor analysis, the study effectively addresses the challenges of large-scale terrain data processing and PV site optimization. This method offers a new approach and technological reference for solar energy development in complex terrains. Although LiDAR technology provides accurate terrain data, challenges remain in constructing precise models, and future research will focus on improving data processing techniques for effective PV panel placement.
Keywords: LiDAR point cloud, Point Cloud compression, Terrain analysis, Solar photovoltaic panels, DTM
Received: 03 Sep 2024; Accepted: 31 Jul 2025.
Copyright: © 2025 Wan, Qin, Shi, Zhang, Huan and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Baofeng Wan, School of Environment and Spatial Informatics, China University of Mining & Technology, Beijing, China
Kai Qin, School of Environment and Spatial Informatics, China University of Mining & Technology, Beijing, China
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