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

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

Broersen, Tom
Peters, R.Y. (TU Delft Urban Data Science)
Ledoux, H. (TU Delft Urban Data Science)

2017

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.

Medial Axis Transform
LiDAR
Skeleton
Watercourse

http://resolver.tudelft.nl/uuid:0cc4912d-9aea-43e9-b84c-e697f2935b3d

https://doi.org/10.1016/j.cageo.2017.06.003

2019-06-16

0098-3004

Computers & Geosciences: an international journal, 106, 171-180

Institutional Repository

journal article

© 2017 Tom Broersen, R.Y. Peters, H. Ledoux