Research on Fine-Scale Terrain Construction in High Vegetation Coverage Areas Based on Implicit Neural Representations

Yi Zhang, Peipei He (), Haihang Jing, Bin He, Weibo Yin, Junzhen Meng, Yuntian Ma, Haifeng Zhang, Bo Zhang and Haoxiang Shen
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Yi Zhang: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
Peipei He: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
Haihang Jing: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
Bin He: Sinohydro Bureau 11 Co., Ltd., Zhengzhou 450001, China
Weibo Yin: School of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
Junzhen Meng: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
Yuntian Ma: College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
Haifeng Zhang: Sinohydro Bureau 11 Co., Ltd., Zhengzhou 450001, China
Bo Zhang: Sinohydro Bureau 11 Co., Ltd., Zhengzhou 450001, China
Haoxiang Shen: Sinohydro Bureau 11 Co., Ltd., Zhengzhou 450001, China

Sustainability, 2025, vol. 17, issue 3, 1-23

Abstract: Due to the high-density coverage of vegetation, the complexity of terrain, and occlusion issues, ground point extraction faces significant challenges. Airborne Light Detection and Ranging (LiDAR) technology plays a crucial role in complex mountainous areas. This article proposes a method for constructing fine terrain in high vegetation coverage areas based on implicit neural representation. This method consists of data preprocessing, multi-scale and multi-feature high-difference point cloud initial filtering, and an upsampling module based on implicit neural representation. Firstly, preprocess the regional point cloud data is preprocessed; then, K-dimensional trees (K-d trees) are used to construct spatial indexes, and spherical neighborhood methods are applied to capture the geometric and physical information of point clouds for multi-feature fusion, enhancing the distinction between terrain and non-terrain elements. Subsequently, a differential model is constructed based on DSM (Digital Surface Model) at different scales, and the elevation variation coefficient is calculated to determine the threshold for extracting the initial set of ground points. Finally, the upsampling module using implicit neural representation is used to finely process the initial ground point set, providing a complete and uniformly dense ground point set for the subsequent construction of fine terrain. To validate the performance of the proposed method, three sets of point cloud data from mountainous terrain with different features are selected as the experimental area. The experimental results indicate that, from a qualitative perspective, the proposed method significantly improves the classification of vegetation, buildings, and roads, with clear boundaries between different types of terrain. From a quantitative perspective, the Type I errors of the three selected regions are 4.3445%, 5.0623%, and 5.9436%, respectively. The Type II errors are 5.7827%, 6.8516%, and 7.3478%, respectively. The overall errors are 5.3361%, 6.4882%, and 6.7168%, respectively. The Kappa coefficients of the measurement areas all exceed 80%, indicating that the proposed method performs well in complex mountainous environments. Provide point cloud data support for the construction of wind and photovoltaic bases in China, reduce potential damage to the ecological environment caused by construction activities, and contribute to the sustainable development of ecology and energy.

Keywords: multi-scale; multi-feature; implicit neural representation; complex mountainous areas; high vegetation coverage areas; wind; photovoltaic infrastructure (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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