The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 589–594, 2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 589–594, 2015

  20 Aug 2015

20 Aug 2015


R. C. Lindenbergh1, D. Berthold2, B. Sirmacek1, M. Herrero-Huerta1, J. Wang1, and D. Ebersbach2 R. C. Lindenbergh et al.
  • 1Dept. of Geoscience & Remote Sensing, Delft University of Technology, Delft, the Netherlands
  • 2LEHMANN + PARTNER GmbH, Schwerborner Straße 1, D-99086 Erfurt, Germany

Keywords: big data, laser mobile mapping, tree parameters, urban, voxels

Abstract. In urbanized Western Europe trees are considered an important component of the built-up environment. This also means that there is an increasing demand for tree inventories. Laser mobile mapping systems provide an efficient and accurate way to sample the 3D road surrounding including notable roadside trees. Indeed, at, say, 50 km/h such systems collect point clouds consisting of half a million points per 100m. Method exists that extract tree parameters from relatively small patches of such data, but a remaining challenge is to operationally extract roadside tree parameters at regional level. For this purpose a workflow is presented as follows: The input point clouds are consecutively downsampled, retiled, classified, segmented into individual trees and upsampled to enable automated extraction of tree location, tree height, canopy diameter and trunk diameter at breast height (DBH). The workflow is implemented to work on a laser mobile mapping data set sampling 100 km of road in Sachsen, Germany and is tested on a stretch of road of 7km long. Along this road, the method detected 315 trees that were considered well detected and 56 clusters of tree points were no individual trees could be identified. Using voxels, the data volume could be reduced by about 97 % in a default scenario. Processing the results of this scenario took ~2500 seconds, corresponding to about 10 km/h, which is getting close to but is still below the acquisition rate which is estimated at 50 km/h.