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.

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 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 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 Parameter-Free Half-Spaces Based 3D Building Reconstruction Using Ground and Segmented Building Points from Airborne LiDAR Data with 2D Outlines

2021 ◽  
Vol 13 (21) ◽  
pp. 4430
Author(s):  
Marko Bizjak ◽  
Borut Žalik ◽  
Niko Lukač
Keyword(s):  
Large Scale ◽  
Airborne Lidar ◽  
Lidar Data ◽  
New Approach ◽  
Building Model ◽  
Building Models ◽  
3D Building Model ◽  
Two Stages ◽  
Building Level ◽  
Airborne Lidar Data

This paper aims to automatically reconstruct 3D building models on a large scale using a new approach on the basis of half-spaces, while making no assumptions about the building layout and keeping the number of input parameters to a minimum. The proposed algorithm is performed in two stages. First, the airborne LiDAR data and buildings’ outlines are preprocessed to generate buildings’ base models and the corresponding half-spaces. In the second stage, the half-spaces are analysed and used for shaping the final 3D building model using 3D Boolean operations. In experiments, the proposed algorithm was applied on a large scale, and its’ performance was inspected on a city level and on a single building level. Accurate reconstruction of buildings with various layouts were demonstrated and limitations were identified for large-scale applications. Finally, the proposed algorithm was validated on an ISPRS benchmark dataset, where a RMSE of 1.31 m and completeness of 98.9 % were obtained.

Simulating and Mapping the Variations of Solar Radiation at the Lujiazui Region of Shanghai Using Airborne LiDAR Data

2012 ◽  
Vol 500 ◽  
pp. 511-516 ◽  
Author(s):  
Zhi Ming Hu ◽  
Jian Ping Wu ◽  
Bin Wu ◽  
Song Shu ◽  
Bai Lang Yu
Keyword(s):  
Solar Radiation ◽  
Three Dimensional ◽  
Temporal Variations ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Total Solar Radiation ◽  
Solar Radiation Intensity ◽  
Flux Model ◽  
Spatio Temporal ◽  
Airborne Lidar Data

This study utilizes high resolution airborne LiDAR data and topographic solar radiation model to quantify the impacts of three-dimensional morphology on the spatio-temporal variations of solar radiation at the Lujiazui Region, Shanghai, China. Monthly direct and non-direct (diffuse and reflection) plus seasonal total solar radiation distributions are simulated and mapped by using a radiation flux model. The results show that the crowded buildings at the Lujiazui Region have severely changed the spatial pattern of solar radiation intensity and duration. The derived monthly and seasonal solar radiation maps would benefit the understanding of the impacts of urban 3D morphology on the environmental factors and be the scientific basic for the further research.

Sign in / Sign up

Export Citation Format

Share Document