K-Plane-Based Classification of Airborne LiDAR Data for Accurate Building Roof Measurement

2014 ◽  
Vol 63 (5) ◽  
pp. 1200-1214 ◽  
Author(s):  
Deming Kong ◽  
Lijun Xu ◽  
Xiaolu Li ◽  
Shuyang Li
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Building Roof ◽  
Airborne Lidar Data

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

Classification of Airborne Lidar Data by Echo

2012 ◽  
Vol 500 ◽  
pp. 696-700 ◽  
Author(s):  
Sheng Yao Wang ◽  
Xi Min Cui ◽  
De Bao Yuan ◽  
Jing Jing Jin ◽  
Qiang Zhang
Keyword(s):  
Data Collection ◽  
Current Data ◽  
Airborne Lidar ◽  
Classification Method ◽  
Lidar Data ◽  
Collection System ◽  
Continuous Development ◽  
Data Collection System ◽  
Airborne Lidar Data

With the continuous development of Airborne Lidar hardware, the current data collection system will not only collect information on a single echo, multiple echo information also can be available. Through the analysis and discussion of echo principle, this paper compares and elaborates the characteristics of single-echo and multiple echo information, and introduces a filter classification method based on echo information, and illustrates that the method is simple and effective according to an example.

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 LOD0 classification of airborne LiDAR data in urban and non-urban areas

2018 ◽  
Vol 51 (1) ◽  
pp. 978-990
Author(s):  
J. Balado ◽  
L. Díaz-Vilariño ◽  
P. Arias ◽  
L. M. González-Desantos
Keyword(s):  
Urban Areas ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

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.

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