Abstract
In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as “tree points” and “other points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6 % are labeled as “tree points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h).
Highlights
The automated analysis of data acquired in urban areas has become a topic of major interest in the fields of remote sensing, photogrammetry, computer vision and robotics
The first step of the framework is given by a semantic classification in terms of assigning semantic class labels to irregularly distributed 3D points (Section 3.1), whereas the second step is given by a semantic segmentation in terms of separating individual objects within the labeled 3D points (Section 3.2)
“other points” in dense 3D point cloud data acquired in urban areas
Summary
The automated analysis of data acquired in urban areas has become a topic of major interest in the fields of remote sensing, photogrammetry, computer vision and robotics. Once respective 3D data have been acquired, different tasks may be addressed, such as a Remote Sens. As a prerequisite for urban planning, numerous municipalities and governmental agencies focus on acquiring tree cadasters, which allow statements about the number of trees, the tree species and the physical and environmental effects of respective trees. Such tree cadasters can be derived from publicly available aerial and street view images from Google maps [18], but in many cases, they are derived from acquired MMS point cloud data. To foster research on the extraction of individual trees from MMS point cloud data as, e.g., shown in Figure 1, a special track within the recent IQmulus
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