Automatic construction of 3D tree models in multiple levels of detail from airborne LiDAR data

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Automatically generated 3D city models are becoming less of a futuristic, demanding or even impossible to attain goal, and more of a necessary, or vastly sought after, means for a multitude of applications. The current prevalence of open geographic information, such as nationwide-covering LiDAR datasets in the Netherlands, opens up opportunities for different parties to experiment in a search for solutions based on LiDAR data. A current approach in answering this demand for 3D city models is 3dfier, which is an ongoing project to automatically generate, disseminate and maintain a 3D city model based on open source airborne LiDAR datasets as a main source. Trees are currently not included in the 3D city models generated by 3dfier, while trees are an integral part of any city landscape.

In this thesis, an implementation is developed that goes through multiple stages of the construction of 3D tree models. First, an initial classification method of the available LiDAR point cloud data is done. This results in a new intermediate point cloud that consists of mostly points belonging to trees. These classified tree points need to be segmented, such that each segment consists of a group of points that represent a single tree. A second classification is constructed after the segmentation, which is called data cleaning. This step ensures that every segment that consists of tree points, is checked for misclassifications and outliers and that these are removed. After cleaning every segment, tree models can be constructed in various LODs and additionally, the types of trees are classified based on identifying features of these trees.

The conclusions of this research are that it is possible to construct 3D tree models based on airborne LiDAR point cloud data and that these can be made to fit in an existing 3D city model. This is demonstrated by creating a 3D city tree model for an existing 3D city model and merging them into one dataset. While further work is required to achieve a seamless fit, the integrated results show that the datasets complement each other well.