Semantic Segmentation on LiDAR Point Cloud in Urban Area using Deep Learning

2019 ◽  
Author(s):  
Satria Bagus Wicaksono ◽  
Ari Wibisono ◽  
Wisnu Jatmiko ◽  
Ahmad Gamal ◽  
Hanif Arief Wisesa
Keyword(s):  
Deep Learning ◽  
Urban Area ◽  
Point Cloud ◽  
Semantic Segmentation

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

Review of Semantic Segmentation of Point Cloud Based on Deep Learning

2020 ◽  
Vol 57 (4) ◽  
pp. 040002
Author(s):  
张佳颖 Zhang Jiaying ◽  
赵晓丽 Zhao Xiaoli ◽  
陈正 Chen Zheng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation

scholarly journals A Novel Point Cloud Encoding Method Based on Local Information for 3D Classification and Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2501 ◽  
Author(s):  
Yanan Song ◽  
Liang Gao ◽  
Xinyu Li ◽  
Weiming Shen
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Local Information ◽  
Local Region ◽  
Feature Representation ◽  
Learning Network ◽  
Feature Representations ◽  
Encoding Method ◽  
Deep Learning Network

Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.

scholarly journals Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods

2019 ◽  
Vol 8 (5) ◽  
pp. 213 ◽  
Author(s):  
Florent Poux ◽  
Roland Billen
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Representation ◽  
Structural Connectivity ◽  
Three Dimensional ◽  
Strong Support ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Learning Methods ◽  
Cloud Data

Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that better characterize point clusters and provide strong support to supervised or unsupervised classification. We provide different feature generalization levels to permit interoperable frameworks. First, we recommend a shape-based feature set (SF1) that only leverages the raw X, Y, Z attributes of any point cloud. Afterwards, we derive relationship and topology between voxel entities to obtain a three-dimensional (3D) structural connectivity feature set (SF2). Finally, we provide a knowledge-based decision tree to permit infrastructure-related classification. We study SF1/SF2 synergy on a new semantic segmentation framework for the constitution of a higher semantic representation of point clouds in relevant clusters. Finally, we benchmark the approach against novel and best-performing deep-learning methods while using the full S3DIS dataset. We highlight good performances, easy-integration, and high F1-score (> 85%) for planar-dominant classes that are comparable to state-of-the-art deep learning.

scholarly journals TAILORED FEATURES FOR SEMANTIC SEGMENTATION WITH A DGCNN USING FREE TRAINING SAMPLES OF A COLORED AIRBORNE POINT CLOUD

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 339-346
Author(s):  
E. Widyaningrum ◽  
M. K. Fajari ◽  
R. C. Lindenbergh ◽  
M. Hahn
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Blue Color ◽  
Point Cloud Data ◽  
Dynamic Graph ◽  
Cloud Data ◽  
Training Samples ◽  
Map Generation ◽  
Fast Development

Abstract. Automation of 3D LiDAR point cloud processing is expected to increase the production rate of many applications including automatic map generation. Fast development on high-end hardware has boosted the expansion of deep learning research for 3D classification and segmentation. However, deep learning requires large amount of high quality training samples. The generation of training samples for accurate classification results, especially for airborne point cloud data, is still problematic. Moreover, which customized features should be used best for segmenting airborne point cloud data is still unclear. This paper proposes semi-automatic point cloud labelling and examines the potential of combining different tailor-made features for pointwise semantic segmentation of an airborne point cloud. We implement a Dynamic Graph CNN (DGCNN) approach to classify airborne point cloud data into four land cover classes: bare-land, trees, buildings and roads. The DGCNN architecture is chosen as this network relates two approaches, PointNet and graph CNNs, to exploit the geometric relationships between points. For experiments, we train an airborne point cloud and co-aligned orthophoto of the Surabaya city area of Indonesia to DGCNN using three different tailor-made feature combinations: points with RGB (Red, Green, Blue) color, points with original LiDAR features (Intensity, Return number, Number of returns) so-called IRN, and points with two spectral colors and Intensity (Red, Green, Intensity) so-called RGI. The overall accuracy of the testing area indicates that using RGB information gives the best segmentation results of 81.05% while IRN and RGI gives accuracy values of 76.13%, and 79.81%, respectively.

scholarly journals FUSION OF FEATURE BASED AND DEEP LEARNING METHODS FOR CLASSIFICATION OF MMS POINT CLOUDS

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 235-242 ◽  
Author(s):  
D. Tosic ◽  
S. Tuttas ◽  
L. Hoegner ◽  
U. Stilla
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Local Geometry ◽  
Learning Approach ◽  
Semantic Classification ◽  
Feature Based ◽  
Urban Scene

<p><strong>Abstract.</strong> This work proposes an approach for semantic classification of an outdoor-scene point cloud acquired with a high precision Mobile Mapping System (MMS), with major goal to contribute to the automatic creation of High Definition (HD) Maps. The automatic point labeling is achieved by utilizing the combination of a feature-based approach for semantic classification of point clouds and a deep learning approach for semantic segmentation of images. Both, point cloud data, as well as the data from a multi-camera system are used for gaining spatial information in an urban scene. Two types of classification applied for this task are: 1) Feature-based approach, in which the point cloud is organized into a supervoxel structure for capturing geometric characteristics of points. Several geometric features are then extracted for appropriate representation of the local geometry, followed by removing the effect of local tendency for each supervoxel to enhance the distinction between similar structures. And lastly, the Random Forests (RF) algorithm is applied in the classification phase, for assigning labels to supervoxels and therefore to points within them. 2) The deep learning approach is employed for semantic segmentation of MMS images of the same scene. To achieve this, an implementation of Pyramid Scene Parsing Network is used. Resulting segmented images with each pixel containing a class label are then projected onto the point cloud, enabling label assignment for each point. At the end, experiment results are presented from a complex urban scene and the performance of this method is evaluated on a manually labeled dataset, for the deep learning and feature-based classification individually, as well as for the result of the labels fusion. The achieved overall accuracy with fusioned output is 0.87 on the final test set, which significantly outperforms the results of individual methods on the same point cloud. The labeled data is published on the TUM-PF Semantic-Labeling-Benchmark.</p>

scholarly journals SEMANTIC SEGMENTATION FOR BUILDING FAÇADE 3D POINT CLOUD FROM 2D ORTHOPHOTO IMAGES USING TRANSFER LEARNING

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 201-206
Author(s):  
A. Murtiyoso ◽  
C. Lhenry ◽  
T. Landes ◽  
P. Grussenmeyer ◽  
E. Alby
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
3D Point Cloud ◽  
Semantic Classification ◽  
3D Data ◽  
The Neural Network ◽  
Building Facades ◽  
Open Question

Abstract. The task of semantic segmentation is an important one in the context of 3D building modelling. Indeed, developments in 3D generation techniques have rendered the point cloud ubiquitous. However pure data acquisition only captures geometric information and semantic classification remains to be performed, often manually, in order to give a tangible sense to the 3D data. Recently progress in computing power also opened the way for massive application of deep learning methods, including for semantic segmentation purposes. Although well established in the processing of 2D images, deep learning solutions remain an open question for 3D data. In this study, we aim to benefit from the vastly more developed 2D semantic segmentation by performing transfer learning on a photogrammetric orthoimage. The neural network was trained using labelled and rectified images of building façades. Another programme was then written to permit the passage between 2D orthoimage and 3D point cloud. Results show that the approach worked well and presents an alternative to help the automation process for point cloud semantic segmentation, at least in the case of photogrammetric data.

scholarly journals Bottleneck Identification to Semantic Segmentation of Industrial 3D Point Cloud Scene via Deep Learning

2021 ◽  
Author(s):  
Romain Cazorla ◽  
Line Poinel ◽  
Panagiotis Papadakis ◽  
Cédric Buche
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Oil And Gas ◽  
Synthetic Data ◽  
Semantic Segmentation ◽  
Object Size ◽  
3D Point Cloud ◽  
Industrial Plants ◽  
Bottleneck Identification ◽  
Data Adaptive

Point cloud acquisition techniques are an essential tool for the digitization of industrial plants, yet the bulk of a designer's work remains manual. A first step to automatize drawing generation is to extract the semantics of the point cloud. Towards this goal, we investigate the use of deep learning to semantically segment oil and gas industrial scenes. We focus on domain characteristics such as high variation of object size, increased concavity and lack of annotated data, which hampers the use of conventional approaches. To address these issues, we advocate the use of synthetic data, adaptive downsampling and context sharing.

scholarly journals Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

2020 ◽  
Vol 12 (6) ◽  
pp. 1005 ◽  
Author(s):  
Roberto Pierdicca ◽  
Marina Paolanti ◽  
Francesca Matrone ◽  
Massimo Martini ◽  
Christian Morbidoni ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cultural Heritage ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Information Modeling ◽  
Dynamic Graph ◽  
Historical Building ◽  
Architectural Elements ◽  
3D Point Clouds

In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.

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