Abstract
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.
Highlights
Recent years saw a rapid development of sensor systems that efficiently sample our 3D environment at high detail, (Vosselman and Maas, 2010)
Methods from e.g. computer vision and computational geometry became available over the last years that are able to extract useful information from such 3D point clouds by estimating locations and sizes of the different objects sampled by the point clouds, (Puttonen et al, 2011, Rutzinger et al, 2010)
The number of publications that addresses the difficulties of processing large urban point clouds is limited, (Weinmann et al, 2015)
Summary
Recent years saw a rapid development of sensor systems that efficiently sample our 3D environment at high detail, (Vosselman and Maas, 2010). Mobile mapping systems implemented in helicopters and cars obtain point clouds consisting of millions to billions of points at a daily basis, (Haala et al, 2008, Puente et al, 2013). Methods from e.g. computer vision and computational geometry became available over the last years that are able to extract useful information from such 3D point clouds by estimating locations and sizes of the different objects sampled by the point clouds, (Puttonen et al, 2011, Rutzinger et al, 2010) Such methods are typically demonstrated in the scientific community at case study scale . The number of publications that addresses the difficulties of processing large urban point clouds is limited, (Weinmann et al, 2015)
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