A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas

Abstract

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).

Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?

“…Based on the segments, features are extracted and then used as input for classification. In contrast, an initial point-wise classification may serve as input for a subsequent segmentation in order to detect specific objects in the scene [4,58,59] or to improve the labeling [60]. The latter has also been addressed with a two-layer CRF [52,61].…”

GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

“…The data were also used in [8], employing deep learning with a convolutional neural network (CNN) and semantic analysis to guide an adaptive image segmentation, finally also demonstrating transfer learning between different benchmark locations. Semantic classification was also performed in [9], here applied to three-dimensional (3D) point cloud data that are increasingly subjected to OBIA processing (e.g., [10][11][12][13]). Transferability of OBIA methods is also furthered though semantic classification with ontologies, as shown in [14], where basic machine learning was also used.…”

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

“…[13][14][15][16][17]; evaluation of different sets of characteristics calculated from a point and its neighborhood [18][19][20]; or multiclass classification techniques based on supervised machine learning algorithms [21][22][23]. In this way, it is possible to locate different elements regardless the complexity of the scenario going from simple geometries such as roofs [24] or columns [25] to complex geometries such as trees [26,27], buildings [24,28,29] or vehicles [30].…”