Standard

Automatic identification of watercourses in flat and engineered landscapes by computing the skeleton of a LiDAR point cloud. / Broersen, Tom; Peters, Ravi; Ledoux, Hugo.

In: Computers & Geosciences: an international journal, Vol. 106, 2017, p. 171-180.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

APA

Vancouver

Author

Broersen, Tom ; Peters, Ravi ; Ledoux, Hugo. / Automatic identification of watercourses in flat and engineered landscapes by computing the skeleton of a LiDAR point cloud. In: Computers & Geosciences: an international journal. 2017 ; Vol. 106. pp. 171-180.

BibTeX

@article{0cc4912d9aea43e9b84ce697f2935b3d,
title = "Automatic identification of watercourses in flat and engineered landscapes by computing the skeleton of a LiDAR point cloud",
abstract = "Drainage networks play a crucial role in protecting land against floods. It is therefore important to have an accurate map of the watercourses that form the drainage network. Previous work on the automatic identification of watercourses was typically based on grids, focused on natural landscapes, and used mostly the slope and curvature of the terrain. We focus in this paper on areas that are characterised by low-lying, flat, and engineered landscapes; these are characteristic to the Netherlands for instance. We propose a new methodology to identify watercourses automatically from elevation data, it uses solely a raw classified LiDAR point cloud as input. We show that by computing twice a skeleton of the point cloud—once in 2D and once in 3D—and that by using the properties of the skeletons we can identify most of the watercourses. We have implemented our methodology and tested it for three different soil types around Utrecht, the Netherlands. We were able to detect 98% of the watercourses for one soil type, and around 75% for the worst case, when we compared to a reference dataset that was obtained semi-automatically.",
keywords = "Medial Axis Transform, LiDAR, Skeleton, Watercourse",
author = "Tom Broersen and Ravi Peters and Hugo Ledoux",
year = "2017",
doi = "10.1016/j.cageo.2017.06.003",
language = "English",
volume = "106",
pages = "171--180",
journal = "Computers & Geosciences: an international journal",
issn = "0098-3004",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Automatic identification of watercourses in flat and engineered landscapes by computing the skeleton of a LiDAR point cloud

AU - Broersen, Tom

AU - Peters, Ravi

AU - Ledoux, Hugo

PY - 2017

Y1 - 2017

N2 - Drainage networks play a crucial role in protecting land against floods. It is therefore important to have an accurate map of the watercourses that form the drainage network. Previous work on the automatic identification of watercourses was typically based on grids, focused on natural landscapes, and used mostly the slope and curvature of the terrain. We focus in this paper on areas that are characterised by low-lying, flat, and engineered landscapes; these are characteristic to the Netherlands for instance. We propose a new methodology to identify watercourses automatically from elevation data, it uses solely a raw classified LiDAR point cloud as input. We show that by computing twice a skeleton of the point cloud—once in 2D and once in 3D—and that by using the properties of the skeletons we can identify most of the watercourses. We have implemented our methodology and tested it for three different soil types around Utrecht, the Netherlands. We were able to detect 98% of the watercourses for one soil type, and around 75% for the worst case, when we compared to a reference dataset that was obtained semi-automatically.

AB - Drainage networks play a crucial role in protecting land against floods. It is therefore important to have an accurate map of the watercourses that form the drainage network. Previous work on the automatic identification of watercourses was typically based on grids, focused on natural landscapes, and used mostly the slope and curvature of the terrain. We focus in this paper on areas that are characterised by low-lying, flat, and engineered landscapes; these are characteristic to the Netherlands for instance. We propose a new methodology to identify watercourses automatically from elevation data, it uses solely a raw classified LiDAR point cloud as input. We show that by computing twice a skeleton of the point cloud—once in 2D and once in 3D—and that by using the properties of the skeletons we can identify most of the watercourses. We have implemented our methodology and tested it for three different soil types around Utrecht, the Netherlands. We were able to detect 98% of the watercourses for one soil type, and around 75% for the worst case, when we compared to a reference dataset that was obtained semi-automatically.

KW - Medial Axis Transform

KW - LiDAR

KW - Skeleton

KW - Watercourse

U2 - 10.1016/j.cageo.2017.06.003

DO - 10.1016/j.cageo.2017.06.003

M3 - Article

VL - 106

SP - 171

EP - 180

JO - Computers & Geosciences: an international journal

JF - Computers & Geosciences: an international journal

SN - 0098-3004

ER -

ID: 51440529