scholarly journals EXTRACTION OF BUILDING BOUNDARY LINES FROM AIRBORNE LIDAR POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 957-962 ◽  
Author(s):  
Yi-Hsing Tseng ◽  
Hsiao-Chu Hung
Keyword(s):  
Spatial Information ◽  
Tall Buildings ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Topographic Maps ◽  
Lidar Data ◽  
Boundary Line ◽  
Boundary Points ◽  
Line Features ◽  
Boundary Lines

Building boundary lines are important spatial features that characterize the topographic maps and three-dimensional (3D) city models. Airborne LiDAR Point clouds provide adequate 3D spatial information for building boundary mapping. However, information of boundary features contained in point clouds is implicit. This study focuses on developing an automatic algorithm of building boundary line extraction from airborne LiDAR data. In an airborne LiDAR dataset, top surfaces of buildings, such as roofs, tend to have densely distributed points, but vertical surfaces, such as walls, usually have sparsely distributed points or even no points. The intersection lines of roof and wall planes are, therefore, not clearly defined in point clouds. This paper proposes a novel method to extract those boundary lines of building edges. The extracted line features can be used as fundamental data to generate topographic maps of 3D city model for an urban area. The proposed method includes two major process steps. The first step is to extract building boundary points from point clouds. Then the second step is followed to form building boundary line features based on the extracted boundary points. In this step, a line fitting algorithm is developed to improve the edge extraction from LiDAR data. Eight test objects, including 4 simple low buildings and 4 complicated tall buildings, were selected from the buildings in NCKU campus. The test results demonstrate the feasibility of the proposed method in extracting complicate building boundary lines. Some results which are not as good as expected suggest the need of further improvement of the method.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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

2021 ◽  
Vol 13 (18) ◽  
pp. 3766
Author(s):  
Zhenyang Hui ◽  
Zhuoxuan Li ◽  
Penggen Cheng ◽  
Yao Yevenyo Ziggah ◽  
JunLin Fan
Keyword(s):  
Point Clouds ◽  
Optimization Method ◽  
Extraction Methods ◽  
Urban Environments ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Extraction ◽  
Existing Building ◽  
Significant Step ◽  
Airborne Lidar Data

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.

scholarly journals An energy minimization approach to automated extraction of regular building footprints from airborne LiDAR data

2014 ◽  
Vol II-3 ◽  
pp. 65-72 ◽  
Author(s):  
Y. He ◽  
C. Zhang ◽  
C. S. Fraser
Keyword(s):  
Energy Function ◽  
Energy Minimization ◽  
Large Scale ◽  
Spatial Information ◽  
Structural Complexity ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Urban Scenes ◽  
Irregular Point ◽  
Airborne Lidar Data

This paper presents an automated approach to the extraction of building footprints from airborne LiDAR data based on energy minimization. Automated 3D building reconstruction in complex urban scenes has been a long-standing challenge in photogrammetry and computer vision. Building footprints constitute a fundamental component of a 3D building model and they are useful for a variety of applications. Airborne LiDAR provides large-scale elevation representation of urban scene and as such is an important data source for object reconstruction in spatial information systems. However, LiDAR points on building edges often exhibit a jagged pattern, partially due to either occlusion from neighbouring objects, such as overhanging trees, or to the nature of the data itself, including unavoidable noise and irregular point distributions. The explicit 3D reconstruction may thus result in irregular or incomplete building polygons. In the presented work, a vertex-driven Douglas-Peucker method is developed to generate polygonal hypotheses from points forming initial building outlines. The energy function is adopted to examine and evaluate each hypothesis and the optimal polygon is determined through energy minimization. The energy minimization also plays a key role in bridging gaps, where the building outlines are ambiguous due to insufficient LiDAR points. In formulating the energy function, hard constraints such as parallelism and perpendicularity of building edges are imposed, and local and global adjustments are applied. The developed approach has been extensively tested and evaluated on datasets with varying point cloud density over different terrain types. Results are presented and analysed. The successful reconstruction of building footprints, of varying structural complexity, along with a quantitative assessment employing accurate reference data, demonstrate the practical potential of the proposed approach.

scholarly journals INTEGRATING SMARTPHONE IMAGES AND AIRBORNE LIDAR DATA FOR COMPLETE URBAN BUILDING MODELLING

2016 ◽  
Vol XLI-B5 ◽  
pp. 741-747 ◽  
Author(s):  
Shenman Zhang ◽  
Jie Shan ◽  
Zhichao Zhang ◽  
Jixing Yan ◽  
Yaolin Hou
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Point ◽  
Lidar Data ◽  
Building Model ◽  
Building Facades ◽  
Single Data ◽  
Airborne Lidar Data ◽  
Building Reconstruction

A complete building model reconstruction needs data collected from both air and ground. The former often has sparse coverage on building façades, while the latter usually is unable to observe the building rooftops. Attempting to solve the missing data issues in building reconstruction from single data source, we describe an approach for complete building reconstruction that integrates airborne LiDAR data and ground smartphone imagery. First, by taking advantages of GPS and digital compass information embedded in the image metadata of smartphones, we are able to find airborne LiDAR point clouds for the corresponding buildings in the images. In the next step, Structure-from-Motion and dense multi-view stereo algorithms are applied to generate building point cloud from multiple ground images. The third step extracts building outlines respectively from the LiDAR point cloud and the ground image point cloud. An automated correspondence between these two sets of building outlines allows us to achieve a precise registration and combination of the two point clouds, which ultimately results in a complete and full resolution building model. The developed approach overcomes the problem of sparse points on building façades in airborne LiDAR and the deficiency of rooftops in ground images such that the merits of both datasets are utilized.

scholarly journals Determination of Building Model Key Points Using Multidirectional Shaded Relief Images Generated from Airborne LiDAR Data

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Dong-Cheon Lee ◽  
David H. Lee ◽  
Dae Geon Lee
Keyword(s):  
Light Source ◽  
Laser Scanner ◽  
Point Clouds ◽  
Shape From Shading ◽  
Scale Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Surface Patches ◽  
Key Points ◽  
Shaded Relief

Light detection and ranging (LiDAR) data collected from airborne laser scanner system is one of the major sources to reconstruct Earth’s surface features. This paper presents a method for detecting model key points (MKPs) of the buildings using LiDAR point clouds. The proposed approach utilizes shaded relief images (SRIs) derived from the LiDAR data. The SRIs based on the concept of the shape from shading could provide unique information about individual surface patches of the building roofs. The main advantage of the proposed approach is to detect directly MKPs, which are primitives for 3D building modeling, without segmenting point clouds. Depending on the location of the light source, the SRIs are created differently. Therefore, integration of the multidirectional SRIs created from different locations of the light source could provide more reliable results. In addition, the vertical exaggeration (i.e., scaling Z-coordinates) is also beneficial because constituent surface patches of the roofs in the SRIs created with vertically exaggerated LiDAR data are more distinguishable. To determine the MKPs of the roofs, building data was separated from other objects using modified marker-controlled watershed algorithm in accordance with criteria to specify buildings such as area, height, and standard deviation. This process could remove the unnecessary objects such as trees, vegetation, and cars. The curvature scale space (CSS) corner detector was used to determine MKP since this method is robust to geometric changes such as rotation, translation, and scale. The proposed method was applied to simulated and real LiDAR datasets with various roof types. The experimental results show that the proposed method is effective in determining MKPs of various roof types with high level of detail (LoD).

scholarly journals INTEGRATING SMARTPHONE IMAGES AND AIRBORNE LIDAR DATA FOR COMPLETE URBAN BUILDING MODELLING

2016 ◽  
Vol XLI-B5 ◽  
pp. 741-747 ◽  
Author(s):  
Shenman Zhang ◽  
Jie Shan ◽  
Zhichao Zhang ◽  
Jixing Yan ◽  
Yaolin Hou
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Point ◽  
Lidar Data ◽  
Building Model ◽  
Building Facades ◽  
Single Data ◽  
Airborne Lidar Data ◽  
Building Reconstruction

A complete building model reconstruction needs data collected from both air and ground. The former often has sparse coverage on building façades, while the latter usually is unable to observe the building rooftops. Attempting to solve the missing data issues in building reconstruction from single data source, we describe an approach for complete building reconstruction that integrates airborne LiDAR data and ground smartphone imagery. First, by taking advantages of GPS and digital compass information embedded in the image metadata of smartphones, we are able to find airborne LiDAR point clouds for the corresponding buildings in the images. In the next step, Structure-from-Motion and dense multi-view stereo algorithms are applied to generate building point cloud from multiple ground images. The third step extracts building outlines respectively from the LiDAR point cloud and the ground image point cloud. An automated correspondence between these two sets of building outlines allows us to achieve a precise registration and combination of the two point clouds, which ultimately results in a complete and full resolution building model. The developed approach overcomes the problem of sparse points on building façades in airborne LiDAR and the deficiency of rooftops in ground images such that the merits of both datasets are utilized.

scholarly journals Tree Extraction from Airborne Laser Scanning Data in Urban Areas

2021 ◽  
Vol 13 (17) ◽  
pp. 3428
Author(s):  
Hangkai You ◽  
Shihua Li ◽  
Yifan Xu ◽  
Ze He ◽  
Di Wang
Keyword(s):  
Urban Areas ◽  
Spatial Information ◽  
3D Structure ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Neighborhood Search ◽  
Lidar Data ◽  
Structure Information ◽  
Airborne Lidar Data ◽  
Tree Extraction

Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.

scholarly journals Application of Lidar Data and 3D-City Models in Visual Impact Simulations of Tall Buildings

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1359-1366 ◽  
Author(s):  
K. Czyńska
Keyword(s):  
Large City ◽  
Tall Buildings ◽  
General Assumption ◽  
Airborne Lidar ◽  
Town Planning ◽  
Lidar Data ◽  
3D City Models ◽  
Simulation Techniques ◽  
Visual Impact ◽  
City Models

The paper examines possibilities and limitations of application of Lidar data and digital 3D-city models to provide specialist urban analyses of tall buildings. The location and height of tall buildings is a subject of discussions, conflicts and controversies in many cities. The most important aspect is the visual influence of tall buildings to the city landscape, significant panoramas and other strategic city views. It is an actual issue in contemporary town planning worldwide. Over 50% of high-rise buildings on Earth were built in last 15 years. Tall buildings may be a threat especially for historically developed cities – typical for Europe. Contemporary Earth observation, more and more available Lidar scanning and 3D city models are a new tool for more accurate urban analysis of the tall buildings impact. The article presents appropriate simulation techniques, general assumption of geometric and computational algorithms – available methodologies and individual methods develop by author. The goal is to develop the geometric computation methods for GIS representation of the visual impact of a selected tall building to the structure of large city. In reference to this, the article introduce a Visual Impact Size method (VIS). Presented analyses were developed by application of airborne Lidar / DSM model and more processed models (like CityGML), containing the geometry and it’s semantics. Included simulations were carried out on an example of the agglomeration of Berlin.

A Priori-Knowledge Method of Bridge Outline Extraction Based on Airborne LiDAR Data

2013 ◽  
Vol 718-720 ◽  
pp. 2438-2442
Author(s):  
Yi Zheng ◽  
Cheng Yi Wang
Keyword(s):  
Spatial Data ◽  
Structural Characteristics ◽  
A Priori ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Boundary Line ◽  
Digital City ◽  
Research Focus ◽  
Airborne Lidar Data ◽  
Priori Knowledge

Bridge information is the most basic spatial data to build 3D digital city and virtual earth, and it plays an important role as clues and reference during extraction of other surface targets. The crucial problem is the extraction of the boundary line of the bridge. It is the research focus of many experts and scholars. This paper presented a method of bridge extraction using airborne LiDAR data. The biggest advantage of the method is based on priori-knowledge and by analyzing the spatial structural characteristics and geometric characteristics of the bridge. Experiments show that this method has a good accuracy compared with the result of expert interpretation.

scholarly journals Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

2020 ◽  
Vol 12 (9) ◽  
pp. 1363 ◽  
Author(s):  
Li Li ◽  
Jian Yao ◽  
Jingmin Tu ◽  
Xinyi Liu ◽  
Yinxuan Li ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Models ◽  
Novel Approach ◽  
Airborne Lidar Data ◽  
Seed Points

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

Sign in / Sign up

Export Citation Format

Share Document