scholarly journals Top-Down Approach to the Automatic Extraction of Individual Trees from Scanned Scene Point Cloud Data

2019 ◽  
Vol 19 (3) ◽  
pp. 11-18 ◽  
Author(s):  
X. NING ◽  
G. TIAN ◽  
Y. WANG
Keyword(s):  
Point Cloud ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Top Down ◽  
Cloud Data ◽  
Individual Trees

scholarly journals Automatic extraction of pavement markings on streets from point cloud data of mobile LiDAR

2017 ◽  
Vol 28 (8) ◽  
pp. 085203 ◽  
Author(s):  
Yang Gao ◽  
Ruofei Zhong ◽  
Tao Tang ◽  
Liuzhao Wang ◽  
Xianlin Liu
Keyword(s):  
Point Cloud ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Pavement Markings

scholarly journals A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

2017 ◽  
Vol 9 (3) ◽  
pp. 277 ◽  
Author(s):  
Martin Weinmann ◽  
Michael Weinmann ◽  
Clément Mallet ◽  
Mathieu Brédif
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Segmentation Framework ◽  
Individual Trees

scholarly journals AUTOMATIC EXTRACTION AND TOPOLOGY RECONSTRUCTION OF URBAN VIADUCTS FROM LIDAR DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 131-135
Author(s):  
Y. Wang ◽  
X. Hu
Keyword(s):  
Point Cloud ◽  
Original Method ◽  
Airborne Lidar ◽  
Least Square ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Topological Graph ◽  
Cloud Data ◽  
City Model ◽  
Region Growth

Urban viaducts are important infrastructures for the transportation system of a city. In this paper, an original method is proposed to automatically extract urban viaducts and reconstruct topology of the viaduct network just with airborne LiDAR point cloud data. It will greatly simplify the effort-taking procedure of viaducts extraction and reconstruction. In our method, the point cloud first is filtered to divide all the points into ground points and none-ground points. Region growth algorithm is adopted to find the viaduct points from the none-ground points by the features generated from its general prescriptive designation rules. Then, the viaduct points are projected into 2D images to extract the centerline of every viaduct and generate cubic functions to represent passages of viaducts by least square fitting, with which the topology of the viaduct network can be rebuilt by combining the height information. Finally, a topological graph of the viaducts network is produced. The full-automatic method can potentially benefit the application of urban navigation and city model reconstruction.

RESEARCH ON AUTOMATIC EXTRACTION OF BLOCK WALL USING POINT CLOUD DATA

2021 ◽  
Vol 77 (2) ◽  
pp. I_161-I_173
Author(s):  
Yoshimasa UMEHARA ◽  
Yoshinori TSUKADA ◽  
Shigenori TANAKA ◽  
Yasunori KOZUKI ◽  
Tsuneaki SHIMONARU ◽  
...  
Keyword(s):  
Point Cloud ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Block Wall

scholarly journals AN AUTOMATIC EXTRACTION METHOD FOR THE PARAMETERS OF MULTI-LOD BIM MODELS FOR TYPICAL COMPONENTS OF WOODEN ARCHITECTURAL HERITAGE

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 679-685
Author(s):  
H. Liu ◽  
M. Hou ◽  
A. Li ◽  
L. Xie
Keyword(s):  
Song Dynasty ◽  
Extraction Method ◽  
Point Cloud ◽  
Point Clouds ◽  
Model Parameters ◽  
Automatic Extraction ◽  
High Fidelity ◽  
Point Cloud Data ◽  
Architectural Heritage ◽  
Cloud Data

<p><strong>Abstract.</strong> A demand-oriented Building Information Model (BIM) model built using high-fidelity point cloud data can better protect architectural heritage. The multi-level detail (mutli-LoD) parametric model emphasizes the different protection requirements of typical components and the automatic extraction of corresponding parameters of high-fidelity point clouds, which are two related key issues. Taking the typical Chinese wooden architectural heritage as an example, according to different requirements, the multi-LoD principle of typical components is proposed. On this basis, the automatic extraction method of the above parameters is developed, and the key parameters of the method are recommended. In order to solve the above problems, taking the three typical Dou-Gong used in Liao Dynasty and Song Dynasty, including Zhutou Puzuo, Bujian Puzuo and Zhuanjiao Puzuo, as an example, briefly introduced the standardization characteristics of the typical components of the "Yingzao Fashi". Subsequently, the corresponding multiple LoD principles are recommended according to different requirements. Based on this and high-fidelity point cloud data, an automatic extraction method for multi-LoD BIM model parameters for typical components of wooden architectural heritage is proposed.</p>

scholarly journals Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests

2020 ◽  
Vol 12 (20) ◽  
pp. 3327 ◽  
Author(s):  
Eric Hyyppä ◽  
Xiaowei Yu ◽  
Harri Kaartinen ◽  
Teemu Hakala ◽  
Antero Kukko ◽  
...  
Keyword(s):  
Data Collection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Tree Height ◽  
Tree Level ◽  
Stem Volume ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Individual Trees

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

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