An Ecological Irrigation Canal Extraction Algorithm Based on Airborne Lidar Point Cloud Data

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (three dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of the external uncertainty model were demonstrated using various lidar point cloud data from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane modeling.

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Water–land classification using three-dimensional point cloud data of airborne LiDAR bathymetry based on elevation threshold intervals

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.13.034511 ◽

2019 ◽

Vol 13 (03) ◽

pp. 1

Author(s):

Xinglei Zhao ◽

Xiaoyang Wang ◽

Jianhu Zhao ◽

Fengnian Zhou

Keyword(s):

Point Cloud ◽

Three Dimensional ◽

Airborne Lidar ◽

Point Cloud Data ◽

Land Classification ◽

Cloud Data ◽

Airborne Lidar Bathymetry

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AUTOMATIC EXTRACTION AND TOPOLOGY RECONSTRUCTION OF URBAN VIADUCTS FROM LIDAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w3-131-2015 ◽

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.

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A Workflow to Extract the Geometry and Type of Vegetated Landscape Elements from Airborne LiDAR Point Clouds

Remote Sensing ◽

10.3390/rs13204031 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4031

Author(s):

Ine Rosier ◽

Jan Diels ◽

Ben Somers ◽

Jos Van Orshoven

Keyword(s):

River Discharge ◽

Point Cloud ◽

Point Clouds ◽

Flood Frequency ◽

Airborne Lidar ◽

Main Function ◽

Point Cloud Data ◽

Landscape Elements ◽

Cloud Data ◽

Landscape Element

Rural European landscapes are characterized by a variety of vegetated landscape elements. Although it is often not their main function, they have the potential to affect river discharge and the frequency, extent, depth and duration of floods downstream by creating both hydrological discontinuities and connections across the landscape. Information about the extent to which individual landscape elements and their spatial location affect peak river discharge and flood frequency and severity in agricultural catchments under specific meteorological conditions is limited. This knowledge gap can partly be explained by the lack of exhaustive inventories of the presence, geometry, and hydrological traits of vegetated landscape elements (vLEs), which in turn is due to the lack of appropriate techniques and source data to produce such inventories and keep them up to date. In this paper, a multi-step methodology is proposed to delineate and classify vLEs based on LiDAR point cloud data in three study areas in Flanders, Belgium. We classified the LiDAR point cloud data into the classes ‘vegetated landscape element point’ and ‘other’ using a Random Forest model with an accuracy classification score ranging between 0.92 and 0.97. The landscape element objects were further classified into the classes ‘tree object’ and ‘shrub object’ using a Logistic Regression model with an area-based accuracy ranging between 0.34 and 0.95.

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