S.J. Oude Elberink

Generation and Dissemination of a National Virtual 3D City and Landscape Model for the Netherlands

This paper describes the generation and dissemination of a national three-dimensional (3D) dataset representing the virtual and landscape model. The 3D model is produced automatically by fusing a two-dimensional (2D) national objectoriented database describing the physical landscape and the national high-resolution height model of the Netherlands. Semantic constraints are introduced to correctly model 3D objects. Three areas from different regions in the Netherlands have been processed in order to develop, improve, and test the automatic generation of a national 3D city and landscape model. Specific attention has been paid to exceptional cases that may occur in a nationwide dataset. Based on the test results, the Kadaster, the national agency in the Netherlands responsible for the production of nation wide geo-information, decided that it is feasible to produce a national 3D city and landscape model that fulfills the specifications that were defined as part of this study. Future research is identified to make the results further ready for practice.

Three-dimensional modeling with national coverage: case of The Netherlands

Three-dimensional technologies have matured over the years. At the same time, 3D information is becoming increasingly important in many applications. Still it is not straightforward to apply the solutions that work on prototypes, small areas or for specific projects to 3D modeling of a whole nation. In the Netherlands, two initiatives are ongoing to address the issues of nation-wide 3D modeling. First, the initiative that aims at establishing and implementing a national 3D standard for large-scale topography with support of all stakeholders. Collecting and maintaining the large-scale data are the responsibility of local governments (mainly municipalities). The second initiative is led by the Kadaster (the organization responsible for topographic mapping in the Netherlands) and aims at automatically generating a 3D version of the 1:10 k object-oriented data-set based on a smart combination of the two-dimensional data with high-resolution laser data. Both initiatives are presented in this paper including results, open issues, and future plans.

Individual tree crown modeling and change detection from airborne lidar data

Light detection and ranging (lidar) provides a promising way of detecting changes of trees in three-dimensional (3-D) because laser beams can penetrate through the foliage and therefore provide full coverage of trees. The aim is to detect changes in trees in urban areas using multitemporal airborne lidar point clouds. Three datasets covering a part of Rotterdam, The Netherlands, have been classified into several classes including trees. A connected components algorithm is applied first to cluster the tree points. However, closely located and intersected trees are clustered together as multi-tree components. A tree-shaped model-based continuously adaptive mean shift (CamShift) algorithm is implemented to further segment these components into individual trees. Then, the tree parameters are derived in two independent methods: a point-based method using the convex hull and a model-based method which fits a tree-shaped model to the lidar points. At last, changes are detected by comparing the parameters of corresponding tree models which are matched by a tree-to-tree matching algorithm using overlapping bounding boxes and point-to-point distances. The results are visualized and statistically analyzed. The CamShift using a tree model kernel yields high segmentation accuracies. The model-based change detection is consistent with the point-based method according to the small differences between the parameters of single trees. The highlight is that it is more robust to data noise and to the segmentation of multi-tree components compared to the point-based method. The detected changes show the potential of the method to monitor the growth of urban trees.

Map based segmentation of airborne laser scanner data

The task of segmenting point clouds is to group points that belong to the same (part of an) object. In this project we make use of an existing topographic map as a kind of background layer to segment point clouds acquired by airborne laser scanning systems. This map is an object based representation into polygons which are labelled into topographic classes like buildings, roads, terrain, water and vegetation. We implemented a point-in-polygon operation which is succeeded by a relabeling step at locations of roof overhangs. Next, the topographic class of the object is used to correctly process the point cloud into meaningful segments. The type of segmentation, e.g. planar or smooth segmentation or connect component analysis, depends on the corresponding topographic class of the object. The segmentation step is extended with a classification step where points are labelled as actually belonging to the corresponding map class or not. This is helpful when dealing with point clouds of cars on roads, or powerlines above the terrain. We segment for example the points on the individual cars into one segment each, but we label those points as not belonging to the class ‘road’. This is useful information to filter points from the point cloud when used to generate a 3D landscape model. The result of the map based segmentation algorithm is a point cloud enriched with information on the corresponding topographic class, corresponding map object, a segment number, and an indication whether this point actually belongs the map class or not. Results on two national datasets show that the use of map information is beneficial compared to a standard segmentation approach. Improvements are shown at situations where in one class a smooth segmentation is more suitable, whereas in the other class a planar segmentation is better, which can only be achieved if the class is known before the segmentation