scholarly journals Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics

2021 ◽  
pp. 367-400
Author(s):  
Wei Yao ◽  
Jianwei Wu
Keyword(s):  
Performance Analysis ◽  
Urban Areas ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Training Data ◽  
Detection Accuracy ◽  
Traffic Dynamics ◽  
Positive Rate ◽  
Adaboost Classifier

AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.

scholarly journals AN IMPROVED AUTOMATIC POINTWISE SEMANTIC SEGMENTATION OF A 3D URBAN SCENE FROM MOBILE TERRESTRIAL AND AIRBORNE LIDAR POINT CLOUDS: A MACHINE LEARNING APPROACH

2019 ◽  
Vol IV-4/W8 ◽  
pp. 139-146
Author(s):  
X.-F. Xing ◽  
M. A. Mostafavi ◽  
G. Edwards ◽  
N. Sabo
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Urban Scenes ◽  
Machine Learning Approach ◽  
Urban Scene ◽  
Point Level

<p><strong>Abstract.</strong> Automatic semantic segmentation of point clouds observed in a 3D complex urban scene is a challenging issue. Semantic segmentation of urban scenes based on machine learning algorithm requires appropriate features to distinguish objects from mobile terrestrial and airborne LiDAR point clouds in point level. In this paper, we propose a pointwise semantic segmentation method based on our proposed features derived from Difference of Normal and the features “directional height above” that compare height difference between a given point and neighbors in eight directions in addition to the features based on normal estimation. Random forest classifier is chosen to classify points in mobile terrestrial and airborne LiDAR point clouds. The results obtained from our experiments show that the proposed features are effective for semantic segmentation of mobile terrestrial and airborne LiDAR point clouds, especially for vegetation, building and ground classes in an airborne LiDAR point clouds in urban areas.</p>

scholarly journals Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective

2018 ◽  
Vol 10 (9) ◽  
pp. 1403 ◽  
Author(s):  
Jianwei Wu ◽  
Wei Yao ◽  
Przemyslaw Polewski
Keyword(s):  
Tree Species ◽  
Urban Areas ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Scanning System ◽  
Urban Road ◽  
Road Corridors ◽  
Laser Scanning System

To meet a growing demand for accurate high-fidelity vegetation cover mapping in urban areas toward biodiversity conservation and assessing the impact of climate change, this paper proposes a complete approach to species and vitality classification at single tree level by synergistic use of multimodality 3D remote sensing data. So far, airborne laser scanning system(ALS or airborne LiDAR) has shown promising results in tree cover mapping for urban areas. This paper analyzes the potential of mobile laser scanning system/mobile mapping system (MLS/MMS)-based methods for recognition of urban plant species and characterization of growth conditions using ultra-dense LiDAR point clouds and provides an objective comparison with the ALS-based methods. Firstly, to solve the extremely intensive computational burden caused by the classification of ultra-dense MLS data, a new method for the semantic labeling of LiDAR data in the urban road environment is developed based on combining a conditional random field (CRF) for the context-based classification of 3D point clouds with shape priors. These priors encode geometric primitives found in the scene through sample consensus segmentation. Then, single trees are segmented from the labelled tree points using the 3D graph cuts algorithm. Multinomial logistic regression classifiers are used to determine the fine deciduous urban tree species of conversation concern and their growth vitality. Finally, the weight-of-evidence (WofE) based decision fusion method is applied to combine the probability outputs of classification results from the MLS and ALS data. The experiment results obtained in city road corridors demonstrated that point cloud data acquired from the airborne platform achieved even slightly better results in terms of tree detection rate, tree species and vitality classification accuracy, although the tree vitality distribution in the test site is less balanced compared to the species distribution. When combined with MLS data, overall accuracies of 78% and 74% for tree species and vitality classification can be achieved, which has improved by 5.7% and 4.64% respectively compared to the usage of airborne data only.

scholarly journals Performance Comparison of Filtering Algorithms for High-Density Airborne LiDAR Point Clouds over Complex LandScapes

2021 ◽  
Vol 13 (14) ◽  
pp. 2663
Author(s):  
Chuanfa Chen ◽  
Jiaojiao Guo ◽  
Huiming Wu ◽  
Yanyan Li ◽  
Bo Shi
Keyword(s):  
Urban Areas ◽  
Total Error ◽  
Point Clouds ◽  
Performance Comparison ◽  
High Density ◽  
Airborne Lidar ◽  
Type I ◽  
Filtering Algorithms ◽  
Study Sites ◽  
Complex Landscapes

Airborne light detection and ranging (LiDAR) technology has become the mainstream data source in geosciences and environmental sciences. Point cloud filtering is a prerequisite for almost all LiDAR-based applications. However, it is challenging to select a suitable filtering algorithm for handling high-density point clouds over complex landscapes. Therefore, to determine an appropriate filter on a specific environment, this paper comparatively assessed the performance of five representative filtering algorithms on six study sites with different terrain characteristics, where three plots are located in urban areas and three in forest areas. The representative filtering methods include simple morphological filter (SMRF), multiresolution hierarchical filter (MHF), slope-based filter (SBF), progressive TIN densification (PTD) and segmentation-based filter (SegBF). Results demonstrate that SMRF performs the best in urban areas, and compared to MHF, SBF, PTD and SegBF, the total error of SMRF is reduced by 1.38%, 48.21%, 48.25% and 31.03%, respectively. MHF outperforms the others in forest areas, and compared to SMRF, SBF, PTD and SegBF, the total error of MHF is reduced by 1.98%, 35.87%, 45.11% and 9.42%, respectively. Moreover, both SMRF and MHF keep a good balance between type I and II errors, which makes the produced DEMs much similar to the references. Overall, SMRF and MHF are recommended for urban and forest areas, respectively, and MHF averagely performs slightly better than SMRF on all areas with respect to kappa coefficient.

scholarly journals HIERARCHICAL HIGHER ORDER CRF FOR THE CLASSIFICATION OF AIRBORNE LIDAR POINT CLOUDS IN URBAN AREAS

2016 ◽  
Vol XLI-B3 ◽  
pp. 655-662 ◽  
Author(s):  
J. Niemeyer ◽  
F. Rottensteiner ◽  
U. Soergel ◽  
C. Heipke
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Conditional Random Field ◽  
Point Clouds ◽  
Higher Order ◽  
Airborne Lidar ◽  
Classification Result ◽  
3D Lidar ◽  
Point Level

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the &lt;i&gt;distance&lt;/i&gt; and the &lt;i&gt;orientation of a segment with respect to the closest road&lt;/i&gt;. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

scholarly journals SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas

2013 ◽  
Vol 5 (8) ◽  
pp. 3749-3775 ◽  
Author(s):  
Jixian Zhang ◽  
Xiangguo Lin ◽  
Xiaogang Ning
Keyword(s):  
Urban Areas ◽  
Point Clouds ◽  
Airborne Lidar

scholarly journals AIRBORNE LIDAR POINT CLOUD CLASSIFICATION BASED ON MULTILEVEL POINT CLUSTER FEATURES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1231-1237
Author(s):  
Y. Gao ◽  
M. C. Li
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Location Based Services ◽  
Training Data ◽  
Cloud Classification ◽  
Point Set ◽  
Point Cloud Classification

Abstract. Airborne Light Detection And Ranging (LiDAR) has become an important means for efficient and high-precision acquisition of 3D spatial data of large scenes. It has important application value in digital cities and location-based services. The classification and identification of point cloud is the basis of its application, and it is also a hot and difficult problem in the field of geographic information science.The difficulty of LiDAR point cloud classification in large-scale urban scenes is: On the one hand, the urban scene LiDAR point cloud contains rich and complex features, many types of features, different shapes, complex structures, and mutual occlusion, resulting in large data loss; On the other hand, the LiDAR scanner is far away from the urban features, and is like a car, a pedestrian, etc., which is in motion during the scanning process, which causes a certain degree of data noise of the point cloud and uneven density of the point cloud.Aiming at the characteristics of LiDAR point cloud in urban scene.The main work of this paper implements a method based on the saliency dictionary and Latent Dirichlet Allocation (LDA) model for LiDAR point cloud classification. The method uses the tag information of the training data and the tag source of each dictionary item to construct a significant dictionary learning model in sparse coding to expresses the feature of the point set more accurately.And it also uses the multi-path AdaBoost classifier to perform the features of the multi-level point set. The classification of point clouds is realized based on the supervised method. The experimental results show that the feature set extracted by the method combined with the multi-path classifier can significantly improve the cloud classification accuracy of complex city market attractions.

Change detection of trees in urban areas using multi-temporal airborne lidar point clouds

2012 ◽  
Author(s):  
Wen Xiao ◽  
Sudan Xu ◽  
Sander Oude Elberink ◽  
George Vosselman
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Multi Temporal

scholarly journals Detection and Geometrical Characterization of a Buried Landfill Site by Integrating Land Use Historical Analysis, Digital Photogrammetry and Airborne Lidar Data

Geosciences ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 348 ◽  
Author(s):  
Giuseppe Esposito ◽  
Fabio Matano ◽  
Marco Sacchi
Keyword(s):  
Urban Areas ◽  
Airborne Lidar ◽  
Landfill Site ◽  
Aerial Photographs ◽  
Topographic Maps ◽  
Campi Flegrei ◽  
Digital Photogrammetry ◽  
Geometrical Characterization ◽  
Starting Point

Abandoned quarries are frequently used as sites of illegal dumping of solid urban waste. These sites often occur nearby or within urban areas so that their detection may turn out to be quite difficult from the surface. This study focuses on the detection and geometrical characterization of a hidden landfill site located along the coastline of the Campi Flegrei, near Naples, Italy. Our approach is based on the analysis of historical topographic maps and aerial photographs, coupled with quantitative comparison of multitemporal digital elevation models obtained by digital photogrammetry and lidar techniques. The comparative analysis of topographic maps and aerial photos clearly shows modifications of the landscape associated with the urban development and quarrying activity, as well as the later filling of the quarry. The change detection analysis reveals that remarkable elevation changes occurred in the study area between 1956 and 2008. The average thickness of the landfill deposits is ca. 8 m, whereas the average volume is ca. 100,000 m3. The results of this work confirm the suitability of the used methodological approach that combines both qualitative and quantitative techniques for the detection of buried landfill sites. The geometric characterization of a landfill represents a fitting starting point for the further planning of geophysical site surveys and direct investigations aimed at the assessment of environmental hazards.

scholarly journals EFFICIENT LARGE-SCALE AIRBORNE LIDAR DATA CLASSIFICATION VIA FULLY CONVOLUTIONAL NETWORK

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 527-532
Author(s):  
E. Maset ◽  
B. Padova ◽  
A. Fusiello
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Ground Vegetation ◽  
Lidar Data ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Two Dimensional Image

Abstract. Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700 km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (ground, vegetation, roof, overground and power line), with an overall accuracy of 92.9%.

scholarly journals CONTEXTUAL CLASSIFICATION OF POINT CLOUDS USING A TWO-STAGE CRF

2015 ◽  
Vol XL-3/W2 ◽  
pp. 141-148 ◽  
Author(s):  
J. Niemeyer ◽  
F. Rottensteiner ◽  
U. Soergel ◽  
C. Heipke
Keyword(s):  
Urban Areas ◽  
Clustering Algorithm ◽  
Conditional Random Field ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Two Stage ◽  
Urban Classification ◽  
Long Range Interactions ◽  
Set Up

In this investigation, we address the task of airborne LiDAR point cloud labelling for urban areas by presenting a contextual classification methodology based on a Conditional Random Field (CRF). A two-stage CRF is set up: in a first step, a point-based CRF is applied. The resulting labellings are then used to generate a segmentation of the classified points using a Conditional Euclidean Clustering algorithm. This algorithm combines neighbouring points with the same object label into one segment. The second step comprises the classification of these segments, again with a CRF. As the number of the segments is much smaller than the number of points, it is computationally feasible to integrate long range interactions into this framework. Additionally, two different types of interactions are introduced: one for the local neighbourhood and another one operating on a coarser scale. <br><br> This paper presents the entire processing chain. We show preliminary results achieved using the Vaihingen LiDAR dataset from the ISPRS Benchmark on Urban Classification and 3D Reconstruction, which consists of three test areas characterised by different and challenging conditions. The utilised classification features are described, and the advantages and remaining problems of our approach are discussed. We also compare our results to those generated by a point-based classification and show that a slight improvement is obtained with this first implementation.

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