scholarly journals CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS OF URBAN SCENES EXPLOITING CYLINDRICAL NEIGHBOURHOODS

2018 ◽  
Vol XLII-2 ◽  
pp. 1225-1228 ◽  
Author(s):  
M. Zheng ◽  
M. Lemmens ◽  
P. van Oosterom
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Bench Mark ◽  
Data Sets ◽  
Automated Classification ◽  
Height Difference ◽  
Urban Scenes ◽  
The Core ◽  
Selection Of

This paper presents our work on automated classification of Mobile Laser Scanning (MLS) point clouds of urban scenes with features derived from cylinders around points of consideration. The core of our method consists of spanning up a cylinder around points and deriving features, such as reflectance, height difference, from the points present within the cylindrical neighbourhood. Crucial in the approach is the selection of features from the points within the cylinder. An overall accuracy could be achieved, exploiting two bench mark data sets (Paris-rue-Madame and IQmulus & TerraMobilita) of 83 % and 87 % respectively.

scholarly journals ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-4 ◽  
pp. 343-349 ◽  
Author(s):  
M. Lemmens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Decision Rules ◽  
Point Clouds ◽  
Bench Mark ◽  
Automated Classification ◽  
Initial Experiment ◽  
Data Set ◽  
Knowledge Based

<p><strong>Abstract.</strong> A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79<span class="thinspace"></span>% for the Paris-rue-Madame point cloud bench mark data set.</p>

scholarly journals Automated Classification of Airborne Laser Scanning Point Clouds

2014 ◽  
pp. 269-292 ◽  
Author(s):  
Christoph Waldhauser ◽  
Ronald Hochreiter ◽  
Johannes Otepka ◽  
Norbert Pfeifer ◽  
Sajid Ghuffar ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Automated Classification ◽  
Airborne Laser

scholarly journals Pointwise classification of mobile laser scanning point clouds of urban scenes using raw data

2021 ◽  
Vol 15 (02) ◽  
Author(s):  
Qiujie Li ◽  
Pengcheng Yuan ◽  
Yusen Lin ◽  
Yuekai Tong ◽  
Xu Liu
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Raw Data ◽  
Urban Scenes

scholarly journals CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS FROM HEIGHT FEATURES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 321-325 ◽  
Author(s):  
M. Zheng ◽  
M. Lemmens ◽  
P. van Oosterom
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
3D Mapping ◽  
Automated Classification ◽  
Present Stage ◽  
Dense Point ◽  
Manual Processing ◽  
Automated Methods ◽  
Acquisition Method

The demand for 3D maps of cities and road networks is steadily growing and mobile laser scanning (MLS) systems are often the preferred geo-data acquisition method for capturing such scenes. Because MLS systems are mounted on cars or vans they can acquire billions of points of road scenes within a few hours of survey. Manual processing of point clouds is labour intensive and thus time consuming and expensive. Hence, the need for rapid and automated methods for 3D mapping of dense point clouds is growing exponentially. The last five years the research on automated 3D mapping of MLS data has tremendously intensified. In this paper, we present our work on automated classification of MLS point clouds. In the present stage of the research we exploited three features &amp;ndash; two height components and one reflectance value, and achieved an overall accuracy of 73&amp;thinsp;%, which is really encouraging for further refining our approach.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

A Survey on Intelligence Tools for Data Analytics

2021 ◽  
pp. 73-95
Author(s):  
Shatakshi Singh ◽  
Kanika Gautam ◽  
Prachi Singhal ◽  
Sunil Kumar Jangir ◽  
Manish Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Real Life ◽  
Learning Tools ◽  
The Core ◽  
Training Mode ◽  
Real Life Situation ◽  
Selection Of

The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.

scholarly journals An Adaptive End-to-End Classification Approach for Mobile Laser Scanning Point Clouds Based on Knowledge in Urban Scenes

2019 ◽  
Vol 11 (2) ◽  
pp. 186 ◽  
Author(s):  
Mingxue Zheng ◽  
Huayi Wu ◽  
Yong Li
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Basic Knowledge ◽  
Multiple Objects ◽  
Urban Scenes ◽  
Knowledge Based ◽  
Training Samples ◽  
End To End ◽  
Heavy Training

It is fundamental for 3D city maps to efficiently classify objects of point clouds in urban scenes. However, it is still a large challenge to obtain massive training samples for point clouds and to sustain the huge training burden. To overcome it, a knowledge-based approach is proposed. The knowledge-based approach can explore discriminating features of objects based on people’s understanding of the surrounding environment, which exactly replaces the role of training samples. To implement the approach, a two-step segmentation procedure is carried out in this paper. In particular, Fourier Fitting is applied for second adaptive segmentation to separate points of multiple objects lying within a single group of the first segmentation. Then height difference and three geometrical eigen-features are extracted. In comparison to common classification methods, which need massive training samples, only basic knowledge of objects in urban scenes is needed to build an end-to-end match between objects and extracted features in the proposed approach. In addition, the proposed approach has high computational efficiency because of no heavy training process. Qualitative and quantificational experimental results show the proposed approach has promising performance for object classification in various urban scenes.

scholarly journals An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

2020 ◽  
Vol 12 (10) ◽  
pp. 1680
Author(s):  
Chenguang Dai ◽  
Zhenchao Zhang ◽  
Dong Lin
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Recall Rate ◽  
Data Sets ◽  
Building Extraction ◽  
Dense Matching ◽  
Object Based ◽  
Alignment Errors ◽  
Precision Rate

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

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