Aerial 3D Building Detection and Modeling From Airborne LiDAR Point Clouds

Sun, Shaohui; Salvaggio, Carl

doi:10.1109/jstars.2013.2251457

Cited by 142 publications

(87 citation statements)

References 22 publications

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“…Kim and Shan [28] also segmented the normal vectors, but they applied a multiphase level set technique. Sun and Salvaggio [29] proposed an automated method to generate 2.5D watertight building models from LIDAR point clouds. Only visual results were presented, so there was no objective evaluation of this method.…”

Section: Related Workmentioning

confidence: 99%

Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

2014

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Automatic extraction of building roofs from remote sensing data is important for many applications, including 3D city modeling. This paper proposes a new method for automatic segmentation of raw LIDAR (light detection and ranging) data. Using the ground height from a DEM (digital elevation model), the raw LIDAR points are separated into two groups. The first group contains the ground points that form a "building mask". The second group contains non-ground points that are clustered using the building mask. A cluster of points usually represents an individual building or tree. During segmentation, the planar roof segments are extracted from each cluster of points and refined using rules, such as the coplanarity of points and their locality. Planes on trees are removed using information, such as area and point height difference. Experimental results on nine areas of six different data sets show that the proposed method can successfully remove vegetation and, so, offers a high success rate for building detection (about 90% correctness and completeness) and roof plane extraction (about 80% correctness and completeness), when LIDAR point density is as low as four points/m 2 . Thus, the proposed method can be exploited in various applications.

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Section: Related Workmentioning

confidence: 99%

Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

2014

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“…The mostly used plane detection techniques from 3D point clouds are region growing, RANSAC and Hough methods. Vosselman et al (2004) and Sun and Salvaggio (2013) proposed region growing for the segmentation of LIDAR point clouds. Furthermore, interesting and efficient studies have been implemented that segment building roofs (Lafarge et al, 2010;Sampath and Shan, 2010;Kada and Wichmann, 2012;Awrangjeb and Fraser, 2013;Verdie et al, 2015) and man-made scenes (Lafarge and Alliez, 2013;Monszpart et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Building Roof Planes From Airborne Lidar Data Applying an Extended 3d Randomized Hough Transform

Maltezos

Ioannidis

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This study aims to extract automatically building roof planes from airborne LIDAR data applying an extended 3D Randomized Hough Transform (RHT). The proposed methodology consists of three main steps, namely detection of building points, plane detection and refinement. For the detection of the building points, the vegetative areas are first segmented from the scene content and the bare earth is extracted afterwards. The automatic plane detection of each building is performed applying extensions of the RHT associated with additional constraint criteria during the random selection of the 3 points aiming at the optimum adaptation to the building rooftops as well as using a simple design of the accumulator that efficiently detects the prominent planes. The refinement of the plane detection is conducted based on the relationship between neighbouring planes, the locality of the point and the use of additional information. An indicative experimental comparison to verify the advantages of the extended RHT compared to the 3D Standard Hough Transform (SHT) is implemented as well as the sensitivity of the proposed extensions and accumulator design is examined in the view of quality and computational time compared to the default RHT. Further, a comparison between the extended RHT and the RANSAC is carried out. The plane detection results illustrate the potential of the proposed extended RHT in terms of robustness and efficiency for several applications.

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“…In contrast, surface elevation measurements obtained from airborne LiDAR are quicker and more effective than traditional photogrammetric techniques [17,18]. Since then, a number of studies have been conducted using LiDAR data on the urban 3D building modeling [19][20][21][22][23], urban vegetation identification and green volume estimation [24,25], and solar potential evaluation in the urban area [26][27][28][29][30]. For example, Yu, et al [26] demonstrated that the integration of urban Digital Surface Model (DSM) from LiDAR data and a solar radiation flux model is able to investigate the spatio-temporal variations of solar radiation in downtown Houston.…”

Section: Introductionmentioning

confidence: 99%

Estimating Roof Solar Energy Potential in the Downtown Area Using a GPU-Accelerated Solar Radiation Model and Airborne LiDAR Data

Huang

Chen

et al. 2015

Remote Sensing

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Solar energy, as a clean and renewable resource is becoming increasingly important in the global context of climate change and energy crisis. Utilization of solar energy in urban areas is of great importance in urban energy planning, environmental conservation, and sustainable development. However, available spaces for solar panel installation in cities are quite limited except for building roofs. Furthermore, complex urban 3D morphology greatly affects sunlit patterns on building roofs, especially in downtown areas, which makes the determination of roof solar energy potential a challenging task. The object of this study is to estimate the solar radiation on building roofs in an urban area in Shanghai, China, and select suitable spaces for installing solar panels that can effectively utilize solar energy. A Graphic Processing Unit (GPU)-based solar radiation model named SHORTWAVE-C simulating direct and non-direct solar radiation intensity was developed by adding the capability of considering cloud influence into the previous SHORTWAVE model. Airborne Light Detection and Ranging (LiDAR) data was used as the input of the SHORTWAVE-C model and to investigate the morphological characteristics of the study area. The results show that the SHORTWAVE-C model can accurately estimate the solar radiation intensity in a complex urban environment under cloudy conditions, and the GPU acceleration method can reduce the computation time by up to 46%. Two sites with different building densities and rooftop structures were selected to illustrate the influence of urban morphology on the solar radiation and solar illumination duration. Based on the findings, an object-based method was implemented to identify suitable places for rooftop solar panel installation that can fully utilize the solar energy potential. Our study provides useful strategic guidelines for the selection and assessment of roof solar energy potential for urban energy planning.

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Aerial 3D Building Detection and Modeling From Airborne LiDAR Point Clouds

Cited by 142 publications

References 22 publications

Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

Automatic Extraction of Building Roof Planes From Airborne Lidar Data Applying an Extended 3d Randomized Hough Transform

Estimating Roof Solar Energy Potential in the Downtown Area Using a GPU-Accelerated Solar Radiation Model and Airborne LiDAR Data

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