Automatic extraction of building roofs using LIDAR data and multispectral imagery

Awrangjeb, Mohammad; Zhang, Chunsun; Fraser, Clive S.

doi:10.1016/j.isprsjprs.2013.05.006

Cited by 154 publications

(164 citation statements)

References 24 publications

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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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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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“…Rau [45] Images 0.8 0.6 Bulatov et al [8] Images 1.1 0.4 Oude Elbrink and Vosselman [46] LiDAR 0.8 0.1 Xiong et al [47] LiDAR 0.7 0.1 Perera et al [48] LiDAR 0.7 0.1 Dorninger and Pfeifer [16] LiDAR 0.8 0.1 Sohn et al [49] LiDAR 0.6 0.2 Xiao et al [50] LiDAR 0.8 0.1 Awrangjeb et al [14] LiDAR 0.8 0.1 Zarea et al [43] LiDAR 0.9 0.4 Our approach LiDAR + Image 0.6 0.1…”

Section: Researchers and Referencesmentioning

confidence: 99%

“…In the data driven case, roof planes are first extracted from LiDAR data. Then, several plane segmentation algorithms are adopted to separate different building rooftops; some examples of these are RANSAC (RANdom SAmple Consensus) [11], the 3D Hough Transform [12], and the region growing algorithm [13][14][15]. Next, the topological relationships of roof features are established; the ridges can be obtained by plane-plane intersection, and the boundaries are determined by regularization with parallel and perpendicular constraints [16,17].…”

Section: Introductionmentioning

confidence: 99%

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

et al. 2014

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Abstract:In this paper, a primitive-based 3D building roof modeling method, by integrating LiDAR data and aerial imagery, is proposed. The novelty of the proposed modeling method is to represent building roofs by geometric primitives and to construct a cost function by using constraints from both LiDAR data and aerial imagery simultaneously, so that the accuracy potential of the different sensors can be tightly integrated for the building model generation by an integrated primitive's parameter optimization procedure. To verify the proposed modeling method, both simulated data and real data with simple buildings provided by ISPRS (International Society for Photogrammetry and Remote Sensing), were used in this study. The experimental results were evaluated by the ISPRS, which demonstrate the proposed modeling method can integrate LiDAR data and aerial imagery to generate 3D building models with high accuracy in both the horizontal and vertical directions. The experimental results also show that by adding a component, such as a dormer, to the primitive, a variant of the simple primitive is constructed, and the proposed method can generate a building model with some details.

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“…Image information is used to help the vegetation filtering. In Awrangjeb et al (2013) information from LIDAR data and multispectral imagery are used for detecting and extracting straight lines. LiDAR points are further classified into ground and non-ground points and, those from the non-ground points' set are checked for proximity to long straight lines.…”

Section: Introductionmentioning

confidence: 99%

BUILDING ROOF BOUNDARY EXTRACTION FROM LiDAR AND IMAGE DATA BASED ON MARKOV RANDOM FIELD

Póz

Fernandes

2017

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

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ABSTRACT:In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects' polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.

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Automatic extraction of building roofs using LIDAR data and multispectral imagery

Cited by 154 publications

References 24 publications

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

Point Cloud Data Fusion for Enhancing 2D Urban Flood Modelling

3D Building Roof Modeling by Optimizing Primitive’s Parameters Using Constraints from LiDAR Data and Aerial Imagery

BUILDING ROOF BOUNDARY EXTRACTION FROM LiDAR AND IMAGE DATA BASED ON MARKOV RANDOM FIELD

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