In our most recent 3DGeo publication, colleagues from KU Leuven together with us developed a new method to extract drainage ditches from LiDAR point clouds in a fully automatic process.
Ditches are often absent in hydrographic geodatasets and their mapping would benefit from a cost and labor effective alternative to field surveys. We propose and evaluate an alternative that makes use of a high resolution LiDAR point cloud dataset. First the LiDAR points are classified as ditch and non-ditch points by means of a random forest classifier which considers subsets of the topographic and radiometric features provided by or derived from the LiDAR product. The LiDAR product includes for each georeferenced point, the elevation, the returned intensity value, and RGB values from simultaneously acquired aerial images. Next so-called ditch dropout points are reconstructed for the blind zones in the dataset using a new geometric approach. Finally, LiDAR ditch points and dropouts are assembled into ditch objects (2D-polygons and their derived centre lines). The procedure was evaluated for a grassland and a peri-urban agricultural area in Flanders, Belgium. A good point classification was obtained (Kappa = 0.77 for grassland and 0.73 for peri-urban area) by using all the features derived from the LiDAR product, whereby the geometric features had the greatest influence. However, even better results were obtained when the radiometric component of the LiDAR product was also taken into account. For the tested models for the extraction of ditch centre lines, the best resulted in an error of omission of 0.03 and an error of commission of 0.08 for the grassland study area and an error of omission of 0.14 and an error of commission of 0.07 for the peri-urban study area.
Roelens, J., Höfle, B., Dondeyne, S., Orshoven, J.V. & Diels, J. (2018): Drainage ditch extraction from airborne LiDAR point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 146, pp. 409-420.
Free access to the paper is available for 50 days via https://authors.elsevier.com/c/1XzwI3I9x1V2FA