A STUDY ON AUTOMATIC EXTRACTION OF 3D POINT CLOUDS BY MACHINE LEARNING FOR ROAD LINES

2019 ◽  
Vol 75 (2) ◽  
pp. I_114-I_122
Author(s):  
Hisashi EMORI ◽  
Motoki SAITO ◽  
Tatsunori SADA ◽  
Masaaki OKAJIMA
Keyword(s):  
Machine Learning ◽  
Point Clouds ◽  
Automatic Extraction ◽  
3D Point Clouds

scholarly journals GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 541-548 ◽  
Author(s):  
E. Grilli ◽  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Heritage ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Documentation ◽  
The Impact

<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

scholarly journals Machine Learning-Based Supervised Classification of Point Clouds Using Multiscale Geometric Features

2021 ◽  
Vol 10 (3) ◽  
pp. 187
Author(s):  
Muhammed Enes Atik ◽  
Zaide Duran ◽  
Dursun Zafer Seker
Keyword(s):  
Machine Learning ◽  
Point Cloud ◽  
Supervised Classification ◽  
Point Clouds ◽  
Support Vector ◽  
Geometric Features ◽  
Mathematical Tool ◽  
City Area ◽  
3D Point Clouds

3D scene classification has become an important research field in photogrammetry, remote sensing, computer vision and robotics with the widespread usage of 3D point clouds. Point cloud classification, called semantic labeling, semantic segmentation, or semantic classification of point clouds is a challenging topic. Machine learning, on the other hand, is a powerful mathematical tool used to classify 3D point clouds whose content can be significantly complex. In this study, the classification performance of different machine learning algorithms in multiple scales was evaluated. The feature spaces of the points in the point cloud were created using the geometric features generated based on the eigenvalues of the covariance matrix. Eight supervised classification algorithms were tested in four different areas from three datasets (the Dublin City dataset, Vaihingen dataset and Oakland3D dataset). The algorithms were evaluated in terms of overall accuracy, precision, recall, F1 score and process time. The best overall results were obtained for four test areas with different algorithms. Dublin City Area 1 was obtained with Random Forest as 93.12%, Dublin City Area 2 was obtained with a Multilayer Perceptron algorithm as 92.78%, Vaihingen was obtained as 79.71% with Support Vector Machines and Oakland3D with Linear Discriminant Analysis as 97.30%.

scholarly journals Automatic Extraction and Labelling of Memorial Objects From 3D Point Clouds

2021 ◽  
Vol 4 (1) ◽  
pp. 79-93
Author(s):  
Nicholas Arnold ◽  
Plamen Angelov ◽  
Tim Viney ◽  
Peter Atkinson
Keyword(s):  
Point Clouds ◽  
Automatic Extraction ◽  
3D Point Clouds

scholarly journals Machine Learning in LiDAR 3D Point Clouds

2021 ◽  
pp. 113-133
Author(s):  
F. Patricia Medina ◽  
Randy Paffenroth
Keyword(s):  
Machine Learning ◽  
Point Clouds ◽  
3D Point Clouds

scholarly journals AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 843-849
Author(s):  
E. Özdemir ◽  
F. Remondino ◽  
A. Golkar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Point Clouds ◽  
Machine Learning Algorithms ◽  
Geometric Features ◽  
Semantic Classes ◽  
3D Point Clouds ◽  
City Models

Abstract. With recent advances in technology, 3D point clouds are getting more and more frequently requested and used, not only for visualization needs but also e.g. by public administrations for urban planning and management. 3D point clouds are also a very frequent source for generating 3D city models which became recently more available for many applications, such as urban development plans, energy evaluation, navigation, visibility analysis and numerous other GIS studies. While the main data sources remained the same (namely aerial photogrammetry and LiDAR), the way these city models are generated have been evolving towards automation with different approaches. As most of these approaches are based on point clouds with proper semantic classes, our aim is to classify aerial point clouds into meaningful semantic classes, e.g. ground level objects (GLO, including roads and pavements), vegetation, buildings’ facades and buildings’ roofs. In this study we tested and evaluated various machine learning algorithms for classification, including three deep learning algorithms and one machine learning algorithm. In the experiments, several hand-crafted geometric features depending on the dataset are used and, unconventionally, these geometric features are used also for deep learning.

scholarly journals SEMANTIC ENRICHMENT OF POINT CLOUD BY AUTOMATIC EXTRACTION AND ENHANCEMENT OF 360&#176; PANORAMAS

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 355-362 ◽  
Author(s):  
A. Tabkha ◽  
R. Hajji ◽  
R. Billen ◽  
F. Poux
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Point Clouds ◽  
Automatic Process ◽  
Automatic Extraction ◽  
Learning Approaches ◽  
Semantic Enrichment ◽  
3D Point Clouds ◽  
Important Challenge

Abstract. The raw nature of point clouds is an important challenge for their direct exploitation in architecture, engineering and construction applications. Particularly, their lack of semantics hinders their utility for automatic workflows (Poux, 2019). In addition, the volume and the irregularity of the structure of point clouds makes it difficult to directly and automatically classify datasets efficiently, especially when compared to the state-of-the art 2D raster classification. Recently, with the advances in deep learning models such as convolutional neural networks (CNNs) , the performance of image-based classification of remote sensing scenes has improved considerably (Chen et al., 2018; Cheng et al., 2017). In this research, we examine a simple and innovative approach that represent large 3D point clouds through multiple 2D projections to leverage learning approaches based on 2D images. In other words, the approach in this study proposes an automatic process for extracting 360° panoramas, enhancing these to be able to leverage raster data to obtain domain-base semantic enrichment possibilities. Indeed, it is very important to obtain a rigorous characterization for use in the classification of a point cloud. Especially because there is a very large variety of 3D point cloud domain applications. In order to test the adequacy of the method and its potential for generalization, several tests were performed on different datasets. The developed semantic augmentation algorithm uses only the attributes X, Y, Z and camera positions as inputs.

scholarly journals MACHINE LEARNING FOR MOBILE LIDAR DATA CLASSIFICATION OF 3D ROAD ENVIRONMENT

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 113-117
Author(s):  
M. Mohamed ◽  
S. Morsy ◽  
A. El-Shazly
Keyword(s):  
Machine Learning ◽  
Laser Scanning ◽  
Smart Cities ◽  
Data Classification ◽  
Point Clouds ◽  
Lidar Data ◽  
Road Maintenance ◽  
3D Point Clouds ◽  
Geometric Point

Abstract. 3D road mapping is essential for intelligent transportation system in smart cities. Road features can be utilized for road maintenance, autonomous driving vehicles, and providing regulations to drivers. Currently, 3D road environment receives its data from Mobile Laser Scanning (MLS) systems. MLS systems are capable of rapidly acquiring dense and accurate 3D point clouds, which allow for effective surveying of long road corridors. They produce huge amount of point clouds, which requires automatic features classification algorithms with acceptable processing time. Road features have variant geometric regular or irregular shapes. Therefore, most researches focus on classification of one road feature such as road surface, curbs, building facades, etc. Machine learning (ML) algorithms are widely used for predicting the future or classifying information to help policymakers in making necessary decisions. This prediction comes from a pre-trained model on a given data consisting of inputs and their corresponding outputs of the same characteristics. This research uses ML algorithms for mobile LiDAR data classification. First, cylindrical neighbourhood selection method was used to define point’s surroundings. Second, geometric point features including geometric, moment and height features were derived. Finally, three ML algorithms, Random Forest (RF), Gaussian Naïve Bayes (GNB), and Quadratic Discriminant Analysis (QDA) were applied. The ML algorithms were used to classify a part of Paris-Lille-3D benchmark of about 1.5 km long road in Lille with more than 98 million points into nine classes. The results demonstrated an overall accuracy of 92.39%, 78.5%, and 78.1% for RF, GNB, and QDA, respectively.

scholarly journals SUPERVISED CLASSIFICATION AND ITS REPEATABILITY FOR POINT CLOUDS FROM DENSE VHR TRI-STEREO SATELLITE IMAGE MATCHING USING MACHINE LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 525-532
Author(s):  
A-M. Loghin ◽  
N. Pfeifer ◽  
J. Otepka-Schremmer
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Image Matching ◽  
Laser Scanning ◽  
Satellite Image ◽  
Point Clouds ◽  
Supervised Machine Learning ◽  
Ground Sample ◽  
Machine Learning Classification ◽  
3D Point Clouds

Abstract. Image matching of aerial or satellite images and Airborne Laser Scanning (ALS) are the two main techniques for the acquisition of geospatial information (3D point clouds), used for mapping and 3D modelling of large surface areas. While ALS point cloud classification is a widely investigated topic, there are fewer studies related to the image-derived point clouds, even less for point clouds derived from stereo satellite imagery. Therefore, the main focus of this contribution is a comparative analysis and evaluation of a supervised machine learning classification method that exploits the full 3D content of point clouds generated by dense image matching of tri-stereo Very High Resolution (VHR) satellite imagery. The images were collected with two different sensors (Pléiades and WorldView-3) at different timestamps for a study area covering a surface of 24 km2, located in Waldviertel, Lower Austria. In particular, we evaluate the performance and precision of the classifier by analysing the variation of the results obtained after multiple scenarios using different training and test data sets. The temporal difference of the two Pléiades acquisitions (7 days) allowed us to calculate the repeatability of the adopted machine learning algorithm for the classification. Additionally, we investigate how the different acquisition geometries (ground sample distance, viewing and convergence angles) influence the performance of classifying the satellite image-derived point clouds into five object classes: ground, trees, roads, buildings, and vehicles. Our experimental results indicate that, in overall the classifier performs very similar in all situations, with values for the F1-score between 0.63 and 0.65 and overall accuracies beyond 93%. As a measure of repeatability, stable classes such as buildings and roads show a variation below 3% for the F1-score between the two Pléiades acquisitions, proving the stability of the model.

scholarly journals Automated mapping of building facades by machine learning

2014 ◽  
Vol XL-3 ◽  
pp. 127-132
Author(s):  
J. Höhle
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Point Cloud ◽  
Grid Point ◽  
Point Clouds ◽  
Machine Learning Techniques ◽  
Automated Classification ◽  
Additional Information ◽  
3D Point Clouds ◽  
Fast Processing

Facades of buildings contain various types of objects which have to be recorded for information systems. The article describes a solution for this task focussing on automated classification by means of machine learning techniques. Stereo pairs of oblique images are used to derive 3D point clouds of buildings. The planes of the buildings are automatically detected. The derived planes are supplemented with a regular grid of points for which the colour values are found in the images. For each grid point of the façade additional attributes are derived from image and object data. This "intelligent" point cloud is analysed by a decision tree, which is derived from a small training set. The derived decision tree is then used to classify the complete point cloud. To each point of the regular façade grid a class is assigned and a façade plan is mapped by a colour palette representing the different objects. Some image processing methods are applied to improve the appearance of the interpreted façade plot and to extract additional information. The proposed method is tested on facades of a church. Accuracy measures were derived from 140 independent checkpoints, which were randomly selected. When selecting four classes ("window", "stone work", "painted wall", and "vegetation") the overall accuracy is assessed with 80 % (95 % Confidence Interval: 71 %&ndash;88 %). The user accuracy of class “stonework” was assessed with 90 % (95 % CI: 80 %&ndash;97 %). The proposed methodology has a high potential for automation and fast processing.

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