Techniques for Automated Classification and Segregation of Mobile Mapping 3D Point Clouds

Wolf, Johannes; Richter, Rico; Döllner, Jürgen

doi:10.5220/0007308802010208

Cited by 4 publications

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“…In Fig. 4, 3D point cloud interpretation has been used to extract the underground infrastructure entities in the street space from mobile mapping data (Wolf et al 2019). The visualization shows the extracted tubes and also has detected street elements such as manhole covers.…”

Section: Examplesmentioning

confidence: 99%

Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins

Döllner

2020

PFG

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Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term "AI" and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a "Naturalness Hypothesis" for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/ DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models.

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Section: Examplesmentioning

confidence: 99%

Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins

Döllner

2020

PFG

Self Cite

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“…Each of these segments can then be analyzed with regard to their size and orientation. Large, vertical surfaces can then, for example, be identified as building facades, whereas groups of points whose corresponding surface normals are pointing into many different directions are usually part of vegetation (Wolf et al, 2019). An alternative approach uses machine learning techniques to identify the semantic classes of objects by using previously trained neural networks (Zhou, Tuzel, 2018).…”

Section: Related Workmentioning

confidence: 99%

Applicability of Neural Networks for Image Classification on Object Detection in Mobile Mapping 3d Point Clouds

Wolf

Richter

Discher

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> In this work, we present an approach that uses an established image recognition convolutional neural network for the semantic classification of two-dimensional objects found in mobile mapping 3D point cloud scans of road environments, namely manhole covers and road markings. We show that the approach is capable of classifying these objects and that it can efficiently be applied on large datasets. Top-down view images from the point cloud are rendered and classified by a U-Net implementation. The results are integrated into the point cloud by setting an additional semantic attribute. Shape files can be computed from the classified points.</p>

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“…These point clouds are characterized by a high density of points and a large volume of unstructured data (Richter and Döllner (2014)), allowing for the precise measurement of small-scale features on real-world objects (e.g., millimeter range). LiDAR scanners can be mounted on unmanned aerial vehicles (UAVs) or mobile mapping vehicles (MMVs) for various applications, including the analysis of indoor objects and structures, infrastructure networks (e.g., roads, railways), and entire cities and countries (Wolf et al (2019)).…”

Section: Introductionmentioning

confidence: 99%

Detecting Road Damages in Mobile Mapping Point Clouds using Competitive Reconstruction Networks

Mattes,

Richter,

Döllner

2023

AGILE GIScience Ser.

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Abstract. LiDAR scanning technology is an established method for capturing landscapes, buildings, or roads in order to create a so-called spatial digital twin of the reality, stored as a large collection of 3D coordinates called 3D point cloud. This spatial data offers high density and precision at the cost of hard to extract shape or object information. One popular application of LiDAR 3D point clouds is road condition quality exams. This task is challenging due to a lack of dedicated algorithms to extract and evaluate road point cloud features and due to the large variety of road damages. Deep learning approaches are very promising, but require extensive training data. The data and damage characteristics make data labeling a very difficult and tedious task that often results in mislabeled data, even when performed by trained human operators.We propose a semi supervised generative adversarial network (GAN) based approach for labeling 2D images rendered from LiDAR point cloud data captured by mobile mapping vehicles, named Competitive Reconstruction Networks (CRN). Our solution trains multiple networks with the same architecture in an ”all vs all” fashion. Our method achieves reliable and robust results on two road image datasets as well as the MVTecAD dataset, and surpass comparable anomaly detection approaches in anomaly detection performance. We also implemented a data generation pipeline to render training images from 3D point cloud of roads and remap anomaly scores back to those 3D point clouds to use the full potential of the 3D data for further analysis.

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Techniques for Automated Classification and Segregation of Mobile Mapping 3D Point Clouds

Cited by 4 publications

References 0 publications

Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins

Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins

Applicability of Neural Networks for Image Classification on Object Detection in Mobile Mapping 3d Point Clouds

Detecting Road Damages in Mobile Mapping Point Clouds using Competitive Reconstruction Networks

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