ABSTRACT Urban villages (UVs) are commonly found in many Asian cities. These villages contain many closely packed buildings constructed decades ago without proper urban planning. There is a need for those buildings to be identified and put into statistics. In this paper, we present a segmentation framework that invokes multiple machine learning techniques and point cloud/image processing algorithms to segment individual closely packed buildings from large urban scenes. The presented framework consists of two major segmentation processes. The framework first filters out the non-ground objects from the point cloud, then it classified them by using the Random Forest classifier to isolate buildings from the entire scene. After that, the building point clouds will be segmented based on several building attribute analysis methods. This is followed by using the Random Sample Consensus (RANSAC) plane filtering method to expand the space between two closely packed buildings, so that the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique can be used to more accurately segment each individual building from the closely packed building areas. Two airborne Light Detection and Ranging (LiDAR) datasets collected in two different cities with some typical closely packed buildings were used to verify the proposed framework. The results show that the framework can effectively identify the closely packed buildings with unified structures from large airborne LiDAR datasets. The overall segmentation accuracy reaches 84% for the two datasets. The proposed framework can serve as a basis for analysis and segmentation of closely packed buildings with a more complicated structure.

中文翻译：

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机载 LiDAR 点云密集建筑物的强大分割框架

摘要 城中村 (UVs) 在许多亚洲城市中很常见。这些村庄包含许多几十年前建造的密集建筑，没有适当的城市规划。需要对这些建筑物进行识别并纳入统计数据。在本文中，我们提出了一个分割框架，该框架调用多种机器学习技术和点云/图像处理算法，从大型城市场景中分割出单个紧密排列的建筑物。所提出的框架由两个主要的分割过程组成。该框架首先从点云中过滤掉非地面物体，然后使用随机森林分类器对它们进行分类，将建筑物与整个场景隔离。之后，将基于多种建筑物属性分析方法对建筑物点云进行分割。随后使用随机样本共识（RANSAC）平面过滤方法来扩展两个紧密排列的建筑物之间的空间，以便可以使用基于密度的噪声应用空间聚类（DBSCAN）聚类技术更准确地分割每个来自密集建筑区域的独立建筑。在两个不同城市收集的两个机载光探测和测距 (LiDAR) 数据集以及一些典型的密密麻麻的建筑物用于验证所提出的框架。结果表明，该框架可以有效地从大型机载 LiDAR 数据集中识别具有统一结构的密集建筑物。两个数据集的整体分割精度达到 84%。