用激光雷达数据提升城市3D建模精度，关键在分割准确率
LOD1 3D City Model from LiDAR: The Impact of Segmentation Accuracy on Quality of Urban 3D Modeling and Morphology Extraction
- 用U-Net3+等模型从激光雷达中提取建筑轮廓
- 分割准确率直接影响3D模型质量和建筑特征估算
- 90%分位数和中位数法可更准估算建筑高度
三维建筑重建，尤其是层级细节1（LOD1），在城市规划、环境研究和交通网络优化中至关重要。本研究评估激光雷达数据在实现高精度LOD1建筑重建及形态特征提取方面的潜力。采用四种深度语义分割模型——U-Net、Attention U-Net、U-Net3+和DeepLabV3+，结合迁移学习从激光雷达数据中提取建筑底面轮廓。结果显示，U-Net3+与Attention U-Net表现最优，交并比（IoU）分别达到0.833和0.814。通过最大值、范围、众数、中位数及90%分位数等统计指标估算建筑高度，生成了LOD1级三维模型。研究核心在于分析分割精度对3D建模质量及建筑面积、外墙表面积等形态特征提取的影响。结果表明，建筑识别准确性显著影响3D模型质量与形态参数估算，具体效果取决于高度计算方法。总体而言，使用90%分位数与中位数的U-Net3+方法能实现建筑高度精准估计，并有效提取形态特征。
原文摘要 · Abstract (English)
Three-dimensional reconstruction of buildings, particularly at Level of Detail 1 (LOD1), plays a crucial role in various applications such as urban planning, urban environmental studies, and designing optimized transportation networks. This study focuses on assessing the potential of LiDAR data for accurate 3D building reconstruction at LOD1 and extracting morphological features from these models. Four deep semantic segmentation models, U-Net, Attention U-Net, U-Net3+, and DeepLabV3+, were used, applying transfer learning to extract building footprints from LiDAR data. The results showed that U-Net3+ and Attention U-Net outperformed the others, achieving IoU scores of 0.833 and 0.814, respectively. Various statistical measures, including maximum, range, mode, median, and the 90th percentile, were used to estimate building heights, resulting in the generation of 3D models at LOD1. As the main contribution of the research, the impact of segmentation accuracy on the quality of 3D building modeling and the accuracy of morphological features like building area and external wall surface area was investigated. The results showed that the accuracy of building identification (segmentation performance) significantly affects the 3D model quality and the estimation of morphological features, depending on the height calculation method. Overall, the UNet3+ method, utilizing the 90th percentile and median measures, leads to accurate height estimation of buildings and the extraction of morphological features.
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