scholarly journals 3D building modeling from airborne Lidar data by building model regularization

2012 ◽  
Vol 30 (4) ◽  
pp. 353-362 ◽  
Author(s):  
Jeong Ho Lee ◽  
Chill Ol Ga ◽  
Yong Il Kim ◽  
Byung Gil Lee
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Building Model ◽  
Building Modeling ◽  
Airborne Lidar Data

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.

Reconstruction of Three Dimensional City Model Based on LIDAR

2012 ◽  
Vol 518-523 ◽  
pp. 5648-5655
Author(s):  
Hui Lin ◽  
Ya Zhou Ji ◽  
Liang Liang ◽  
Wei Liu ◽  
Zhao Ling Hu
Keyword(s):  
Three Dimensional ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Model ◽  
3D Data ◽  
Topography Model ◽  
City Model ◽  
Three Dimensional Coordinate ◽  
Airborne Lidar Data ◽  
Acquisition Method

The research of Three Dimensional City Model (3DCM) has become a hot topic in GIS field in recent years, and it also has played an important role in traffic, land, mining, surveying and mapping, and other fields, especially in urban planning. However, the difficulty to acquire 3D data is the key obstacle to the further development of 3DCM. Airborne LIDAR, integrating GPS, INS and scanning laser rangefinder, can rapidly acquire the 3D position of ground by airplane, which is very economical, efficient and convenient to acquire 3D data. Because traditional three-dimensional data acquisition method can’t meet the need of the city’s fast development, airborne LIDAR technology is regarded as a convenient, swift, high-efficient three-dimensional data acquisition method. Compared with traditional methods, the airborne LIDAR technology has the following characteristics: 1) High efficiency: in 12 hours, the airborne LIDAR can scan 1000 square kilometers, next, with the help of the related post-processing software, LIDAR cloud data can transform them into GIS format or other receivable format in certain automatic or semiautomatic mode. 2) High precision: because the pulse of laser light isn’t easily subject to shadow and solar angle, it greatly improves the data quality. The flight height limit has no influence on its elevation data precision, which is superior to the conventional photogrammetry. The plane precision may achieve 0.15 to 1 meter, the elevation precision may achieve 10 centimeters. 3) All-weather feature: airborne LIDAR is active remote sensing without considering the digital aerial photogrammetry. 4) Rich information: with the aid of airborne LIDAR ,we can obtains not only the three dimensional coordinate of ground point, but also the three dimensional coordinate of terrain details, such as trees, buildings, roads. If it is integrated with CCD, it could gains image information. We acquired the airborne LIDAR data of 20 square kilometers in the central area of Shanghai using ALTM3100 airborne LIDAR system of the Optech company in 2006.This paper introduces the data processing procedure of the airborne LIDAR data, LIDAR system uses random commercial software to process plane GPS tracking data、plane attitude data、 laser ranging data and the swinging angle data of laser scanning mirror, finally, obtaining the three-dimensional coordinates(X,Y,Z) data of various surveying points. Which three-dimensional discrete dot matrix data is without attribute suspending in the air namely LIDAR original data, named “point cloud”. LIDAR data performs pre-processing to obtain digital surface model (DSM), which is classified and extracted, we acquire topography and object related to modeling, preparing for three-dimensional city model. Data pre-processing includes abnormal point deletion, coordinate transformation and flight strip combination. At present, we used famous business software TerraSolid, developed by Company of Finland to realize the classification and extraction from the LIDAR data TerraSolid depends on MicroStation platform, on the basis of classification and extraction algorithms presented by Axelsson, et al. of Swedish, including a lot of module such as TerraScan, TerraModeler and TerraPhoto. TerraScan is used in the field of LIDAR data classification and extraction, TerraModeler is used for producing and dealing with various planes, TerraPhoto is used for dealing with the primitive image, topography model and building model are got by using this software, complicated artificial building (Oriental Pearl, Jin Mao mansion etc.) need artificial repair and disposal, data processing of 20 sq. km. takes more than one month, efficiency has been improved greatly on the premise of guaranteeing the precision. Topography model and building model can be obtained by using TerraSolid and combining a few manual intervention based on DSM, The topography model is expressed with the triangulated irregular network (TIN), the building model is expressed with 3ds format, three dimensional model of non - texture of Lujiazui region of Shanghai was gained by LIDAR data. In order to achieving better visualization effect, the topography model overlaps orthophoto, and stuck true texture to building model, true city landscape of Lujiazui region of Shanghai is established. This paper has introduce post-processing procedure of airborne LIDAR data systematically, has realized the fast reconstruction of three-dimension urban model based on LIDAR data, enable this technology to serve the information construction of the city better.

Automatic Construction of 3-D Building Model From Airborne LIDAR Data Through 2-D Snake Algorithm

2015 ◽  
Vol 53 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Jianhua Yan ◽  
Keqi Zhang ◽  
Chengcui Zhang ◽  
Shu-Ching Chen ◽  
Giri Narasimhan
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Automatic Construction ◽  
Building Model ◽  
Airborne Lidar Data ◽  
Snake Algorithm

scholarly journals AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA

2017 ◽  
Vol IV-4/W4 ◽  
pp. 411-414 ◽  
Author(s):  
N. Yastikli ◽  
Z. Cetin
Keyword(s):  
Point Cloud ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Model Generation ◽  
Automatic Production ◽  
Building Model ◽  
Building Models ◽  
Wide Range ◽  
3D Building Model ◽  
Airborne Lidar Data

LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data.

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.

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