Urban land cover classification using airborne LiDAR data: A review

Yan, Wai Yeung; Shaker, Ahmed; El-Ashmawy, Nagwa

doi:10.1016/j.rse.2014.11.001

Cited by 460 publications

(294 citation statements)

References 208 publications

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“…Over the past two decades, airborne LiDAR data have been used in urban applications (Yan et al, 2015). Numerous studies have focused on extracting one object type in urban scenes, such as separation of ground from non-ground points in order to generate digital terrain models (DEMs) (Bartels et al 2006), building extraction (Huang et al, 2013), road extraction (Samadzadegan et al, 2009) and curbstones mapping (Zhou and Vosselman, 2012).…”

Section: Introductionmentioning

confidence: 99%

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Morsy

Shaker

El-Rabbany

2017

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

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ABSTRACT:With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532 m), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation -Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1% using different NDVIs.

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Section: Introductionmentioning

confidence: 99%

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Morsy

Shaker

El-Rabbany

2017

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

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“…Over the past decade, acquisition capabilities of many satellite remote sensing systems have increased rapidly to better than 2.5 m, and comprehensive classification and high-performance computing technology for big data have been successfully developed [2][3][4][5]. Accordingly, fine-scale land cover information has become crucial for mapping and managing complex Earth surface environments, such as urban and surface mine environments in MAs, at local and regional scales.…”

Section: Importance Of Fine-scale Land Cover Classification In Open-pmentioning

confidence: 99%

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Chen

et al. 2017

Remote Sensing

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Abstract:Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.

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“…During the last decades, many filtering algorithms have been explored and developed for classifying top-view LiDAR point cloud in order to extract some key components of urban features, e.g. land covers (Yan et al, 2015), trees (Alonzo et al, 2014;Han et al, 2014;Chen et al, 2015), buildings (Kabolizade et al, 2010;Awrangjeb et al, 2013;Mongus et al, 2014;Song et al, 2015;Ferraz et al, 2016), roads (Li et al, 2015;Ferraz et al, 2016), or even vehicles (Yao et al, 2010). When a set of criteria has been characterised, essential information embedded in point cloud can be extracted and classified into particular segments.…”

Section: Top-view Lidar Point Cloud Extractionmentioning

confidence: 99%

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

Meesuk¹

2017

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Although all care is taken to ensure the integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers, the author nor UNESCO-IHE for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. A pdf version of this work will be made available as Open Access via http://repository.tudelft.nl/ihe This version is licensed under the Creative Commons Attribution-Non Commercial 4.0 International License, http://creativecommons.org/licenses/by-nc/4.0/Published by: CRC Press/Balkema PO Box 11320, 2301 EH Leiden, the Netherlands e-mail: Pub.NL@taylorandfrancis.com www.crcpress.com -www.taylorandfrancis.com to my dear family SummaryModelling urban flood dynamics requires detailed knowledge of a number of complex processes. Numerical simulation of flood propagation requires proper understanding of all processes involved. Also, special care has to be taken to adequately represent the urban topography, taking into account typical urban features like underpasses and hidden alleyways. Incorrect input data will affect the numerical model results, which may lead to inadequate flood-protection measures or even catastrophic flooding situations. This thesis explores how to include particular urban topographic features in urban flood modelling.Aerial Light Detection and Ranging (LiDAR) systems offer opportunities for achieving good quality topographic data, with less fieldwork on the ground. Even though aerial LiDAR data have long been used in many applications, conventional top-view LiDAR data can not quite capture some hidden urban features. However, recent improvements in Structure from Motion (SfM) techniques provide opportunities to achieve improved quality topographic data by using multiple viewpoints, including side-view data.This dissertation explores insights into the capabilities of assimilating SfM point cloud data by using multi-source views as input for enhancing 2D urban flood modelling. Side-view SfM point cloud data are collected and merged with conventional top-view LiDAR point cloud data to create novel Multi-Source Views (MSV) topographic data. The main objectives of this research are (i) to provide insight into the capabilities of using computer-based environments; (ii) to explore the benefits of using the new MSV data; (iii) to enhance 2D model schematizations; (iv) to compare simulated results using new MSV data and conventional top-view LiDAR data as input for urban flood models; and (v) to help developing flood-protection measures. Findings showed that simulation results using conventional top-view LiDAR-DSM as input show the least flood inundation areas and results contained many dry areas. This is because conventional LiDAR-DSM does not capture hidden urban features like underpasses, high trees, overarching structures, which behave as obstacles in 2D model schematics. When applying extended top-view LiDAR-DBM+ and the newly developed MSV-DEM as input, simulation results showed m...

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Urban land cover classification using airborne LiDAR data: A review

Cited by 460 publications

References 208 publications

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

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