Building Extraction from Airborne LiDAR Data Based on Multi-Constraints Graph Segmentation

Abstract

Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes or varying building sizes, these methods cannot achieve satisfactory building extraction results. To address these challenges, a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation was proposed in this paper. The proposed method mainly converted point-based building extraction into object-based building extraction through multi-constraints graph segmentation. The initial extracted building points were derived according to the spatial geometric features of different object primitives. Finally, a multi-scale progressive growth optimization method was proposed to recover some omitted building points and improve the completeness of building extraction. The proposed method was tested and validated using three datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that the proposed method can achieve the best building extraction results. It was also found that no matter the average quality or the average F1 score, the proposed method outperformed ten other investigated building extraction methods.

Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

“…In the literature, the building point cloud filtering procedure is applied in two contexts. First, in the context of automatic classification of a point cloud when the classification approach aims to detect the building point clouds, the filtering operation is applied on the building mask to improve its quality [9,11]. Second, in the building modelling algorithm, the filtering operation is applied to the building point cloud before starting the construction of the building model.…”

“…Despite this, the influence of the employed filtering operation will appear in the quality of the final product (building mask or building model). This is why the accuracy of the suggested approach is compared with the accuracy of the approaches [7,9,11,[34][35][36] in the context of comparing the efficacity of the suggested approach with other similar approaches suggested in the literature To clarify how this comparison can be achieved, let us take this example. First, the result of an urban point cloud classification is a building mask named m 1 .…”

“…In another way, Varlik and Uray [8] prefer to project the LiDAR point cloud firstly onto a 2D plane before employing a U-net architecture deep-learning network. Alternatively, after detecting the building class, Hui et al [9] apply multiscale progressive growth to optimize the obtained result.…”

Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network

“…In humanitarian responses, the completeness and correctness of the generated dwellings are more important than the pixel accuracy, as every further subsequent decision is based on the numbers and types (size) of the dwellings. To serve this purpose, the completeness, correctness, and quality metrics were computed as in [75] and [76], and are provided in Equations ( 7)- (10):…”

Building extraction from oblique photogrammetry point clouds based on PointNet++ with attention mechanism

“…Building extraction has facilitated numerous applications, including population density estimation, natural disaster prevention and warning, and urban planning (Freire et al., 2014; Griffiths & Boehm, 2019; Lafarge et al., 2008; Liu, Zhu, et al., 2023; Wei et al., 2004). Airborne light detection and ranging (LiDAR) has developed very rapidly in recent years and has become an attractive choice for building extraction, due to its high efficiency and measurement accuracy, lower interference by the external environment and strong initiative (Hui et al., 2021; Liu, Wang, et al., 2023; Zhou & Gong, 2018). Meanwhile, unmanned aerial vehicle (UAV) photogrammetry has been used in a growing number of diverse applications across different scientific disciplines, and as oblique photogrammetry technology continues to advance, its application prospects in digital cities and smart city development are becoming increasingly vast.…”