scholarly journals AUTOMATIC EXTRACTION OF BUILDING ROOF PLANES FROM AIRBORNE LIDAR DATA APPLYING AN EXTENDED 3D RANDOMIZED HOUGH TRANSFORM

2016 ◽  
Vol III-3 ◽  
pp. 209-216 ◽  
Author(s):  
Evangelos Maltezos ◽  
Charalabos Ioannidis
Keyword(s):  
Hough Transform ◽  
Airborne Lidar ◽  
Computational Time ◽  
Experimental Comparison ◽  
Lidar Data ◽  
Building Roof ◽  
Additional Information ◽  
Randomized Hough Transform ◽  
Airborne Lidar Data ◽  
Scene Content

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.

scholarly journals AUTOMATIC EXTRACTION OF BUILDING ROOF PLANES FROM AIRBORNE LIDAR DATA APPLYING AN EXTENDED 3D RANDOMIZED HOUGH TRANSFORM

2016 ◽  
Vol III-3 ◽  
pp. 209-216 ◽  
Author(s):  
Evangelos Maltezos ◽  
Charalabos Ioannidis
Keyword(s):  
Hough Transform ◽  
Airborne Lidar ◽  
Computational Time ◽  
Experimental Comparison ◽  
Lidar Data ◽  
Building Roof ◽  
Additional Information ◽  
Randomized Hough Transform ◽  
Airborne Lidar Data ◽  
Scene Content

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.

scholarly journals Synergy Between LiDAR and Image Data in Context of Building Extraction

2014 ◽  
Vol XL-1 ◽  
pp. 89-93
Author(s):  
A. P. Dal Poz
Keyword(s):  
Image Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Theoretical Studies ◽  
Building Extraction ◽  
Building Roof ◽  
Photogrammetric Method ◽  
Airborne Lidar Data

This paper compares the paradigms of LiDAR and aerophotogrammetry in the context of building extraction and briefly discusses a photogrammetric strategy for refining building roof polyhedrons previously extracted from LiDAR data. In general, empirical and theoretical studies have confirmed that LiDAR-based methodologies are more suitable in extracting planar roof faces and ridges of the roof, whereas the aerophotogrammetry are more suitable in extracting building roof outlines. In order to exemplify how to explore these properties, it is presented a photogrammetric method for refining 3D building roof contours extracted from airborne LiDAR data. Examples of application are provided for this refining approach.

scholarly journals Regularization of Building Roof Boundaries from Airborne LiDAR Data Using an Iterative CD-Spline

2020 ◽  
Vol 12 (12) ◽  
pp. 1904
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
Ayman F. Habib
Keyword(s):  
Model Building ◽  
Real Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Roof ◽  
Building Models ◽  
The Many ◽  
Straight Lines ◽  
Airborne Lidar Data ◽  
Selection Of

Building boundaries play an essential role in many applications such as urban planning and production of 3D realistic views. In this context, airborne LiDAR data have been explored for the generation of digital building models. Despite the many developed strategies, there is no method capable of encompassing all the complexities in an urban environment. In general, the vast majority of existing regularization methods are based on building boundaries that are made up of straight lines. Therefore, the development of a strategy able to model building boundaries, regardless of their degree of complexity is of high importance. To overcome the limitations of existing strategies, an iterative CD-spline (changeable degree spline) regularization method is proposed. The main contribution is the automated selection of the polynomial function that best models each segment of the building roof boundaries. Conducted experiments with real data verified the ability of the proposed approach in modeling boundaries with different levels of complexities, including buildings composed of complex curved segments and point cloud with different densities, presenting Fscore and PoLiS around 95% and 0.30 m, respectively.

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.

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa
Keyword(s):  
Urban Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data

scholarly journals A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

2021 ◽  
Vol 13 (4) ◽  
pp. 559
Author(s):  
Milto Miltiadou ◽  
Neill D. F. Campbell ◽  
Darren Cosker ◽  
Michael G. Grant
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Airborne Lidar ◽  
Memory Allocation ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Airborne Lidar Data ◽  
Full Waveform Lidar ◽  
Polygonal Model

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

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