Contour Extraction in Buildings in Airborne LiDAR Point Clouds Using Multiscale Local Geometric Descriptors and Visual Analytics

Sreevalsan-Nair, Jaya; Jindal, Akshay; Kumari, Beena

doi:10.1109/jstars.2018.2833801

Cited by 12 publications

(10 citation statements)

References 47 publications

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“…It cannot connect the large gaps and completely delete the nonroad regions [40]. Therefore, this paper uses tensor voting [41][42][43] algorithm to solve the above problems.…”

Section: Methodsmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

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“…It cannot connect the large gaps and completely delete the nonroad regions [40]. Therefore, this paper uses tensor voting [41][42][43] algorithm to solve the above problems.…”

Section: Methodsmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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“…Metrics such as Matthew's Correlation coefficient (MCC) and F1-score (F1), defined by Eqs. 4 and 5, respectively, below based on the point coverage of extracted boundaries, are used by some authors to evaluate the reconstructed 3D buildings [42,43]. Individual extracted roof parts are evaluated with the corresponding reference data in this case.…”

Section: R C = Tp Tp + Fnmentioning

confidence: 99%

A Robust Performance Evaluation Metric for Extracted Building Boundaries From Remote Sensing Data

Dey

Awrangjeb

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…(Ameri, 2000) used the Voronoï diagram of the roof planes. Jaya et al (2018), Xiong et al (2015 and Perera et al (2012) used the roof topology graph based on the closed cycle graphs summarising the topological relationships between the roof segments.…”

Section: Related Workmentioning

confidence: 99%

Automatic 2d Modelling of Inner Roof Planes Boundaries Starting From Lidar Data

Kurdi

Awrangjeb

Munir

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Despite the large quantity of researches and publications achieved during the last three decades about 3D building modelling by using Lidar data, the question of inner roof plane boundaries modelling needs to be more extracted in detail. This paper focuses on detection and 2D modelling of building inner roof plane boundaries. This operation presents an imperative junction between roof planes detection and 3D building model generation. Therefore, it presents key procedure in data driven approaches. For achieving this purpose, roof boundaries are classified in four categories: outer building boundaries, inner roof plane boundaries, roof details (chimneys and windows) boundaries and boundaries related to non-detectable roof details. This paper concentrates on detection and modelling of inner roof plane boundaries and roof details (chimneys and windows) boundaries. Moreover, it details the modelling procedures step by step that is envisaged rarely in the literature. The proposed approach starts by analysing the adjacency relationship between roof planes. Then, the inner roof plane boundaries are detected. Finally, the junction relationships between boundaries are analysed before detecting the roof vertices. Once the 2D roof model is calculated, the visual deformations in addition to modelling accuracy are discussed.</p>

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Contour Extraction in Buildings in Airborne LiDAR Point Clouds Using Multiscale Local Geometric Descriptors and Visual Analytics

Cited by 12 publications

References 47 publications

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

A Robust Performance Evaluation Metric for Extracted Building Boundaries From Remote Sensing Data

Automatic 2d Modelling of Inner Roof Planes Boundaries Starting From Lidar Data

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