Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

Abstract

Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and F-score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision > 0.9 and favorable Recall and F-score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities.

Building Footprint Extraction in Dense Area from LiDAR Data using Mask R-CNN

“…The image structure dictates how many points are added or removed. The structuring element is a set of coordinates that determines the performance [1,32].…”

“…In [5] Integrated spectral signatures from diverse sensors into LiDAR point cloud classification utilizing multiple feature spaces using machine learning algorithms. Furthermore, several efforts were conducted [1,4,5] to assess LiDAR data in building footprint extraction, varying between semiautomatic to automatic. Extracting a building footprint can be divided into three phases: isolating nonground points, segmenting building points, and extracting the building outline from the building footprint segmentation, as shown in Fig.…”

“…With default parameter settings, the Bayesian optimization method was run 100 times. The weighted voting is given in Eq (1).…”

“…The identification and extraction of urban footprint has become an important research topic and tool in city planning, transportation planning, urban simulation, 3D city modelling, and building change detection [1][2][3]. Automatic building footprint extraction is needed to meet the rising demand for precise city building outlines.…”

Content-Aware Point Cloud Simplification of Natural Scenes

“…The eventual utilisation of the 3D clouds dictates the point density during acquisition and is then applied to the entire scene. As an example, to extract buildings within urban regions, Guo et al [9] used scans with up to 40 pts/m 2 density, though only the building boundaries benefit from such a resolution. Similar density of 40 pts/m 2 was also set to scan and analyse rockfall activity [5]; and a density of 2,000 pts/m 2 was applied to monitor Paleolithic archaeological excavations [4].…”