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JCR Impact Factor: 0.459
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  3/2013 - 2

Automatic Building Extraction from Terrestrial Laser Scanning Data

HAO, W. See more information about HAO, W. on SCOPUS See more information about HAO, W. on IEEExplore See more information about HAO, W. on Web of Science, WANG, Y. See more information about  WANG, Y. on SCOPUS See more information about  WANG, Y. on SCOPUS See more information about WANG, Y. on Web of Science, NING, X. See more information about  NING, X. on SCOPUS See more information about  NING, X. on SCOPUS See more information about NING, X. on Web of Science, ZHAO, M. See more information about  ZHAO, M. on SCOPUS See more information about  ZHAO, M. on SCOPUS See more information about ZHAO, M. on Web of Science, ZHANG, J. See more information about  ZHANG, J. on SCOPUS See more information about  ZHANG, J. on SCOPUS See more information about ZHANG, J. on Web of Science, SHI, Z. See more information about  SHI, Z. on SCOPUS See more information about  SHI, Z. on SCOPUS See more information about SHI, Z. on Web of Science, ZHANG, X. See more information about ZHANG, X. on SCOPUS See more information about ZHANG, X. on SCOPUS See more information about ZHANG, X. on Web of Science
 
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Download PDF pdficon (1,092 KB) | Citation | Downloads: 406 | Views: 2,489

Author keywords
building extraction, point cloud segmentation, plane recognition, terrestrial laser scanning

References keywords
data(13), sensing(9), remote(9), photogrammetry(8), segmentation(6), laser(6), extraction(6), ransac(5), point(5), isprs(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-08-31
Volume 13, Issue 3, Year 2013, On page(s): 11 - 16
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.03002
Web of Science Accession Number: 000326321600002
SCOPUS ID: 84884962911

Abstract
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The extraction of building from the huge amount of point clouds with different local densities, especially in the presence of random noisy points, is still a formidable challenge. In this paper, we present a complete strategy for building extraction from terrestrial laser scanning data. First, a novel segmentation method is proposed to facilitate the task of building extraction. The points are grouped based on the normals and the adjacency relationships. Second, the planar surfaces are recognized from the segmentation results based on the properties of the Gaussian image. Finally, the buildings are extracted from the urban point clouds based on a collection of characteristics of point cloud segments like shape, normal direction and topological relationship. Experimental results demonstrate that the proposed method can be used as a robust way to extract buildings from terrestrial laser scanning data. At the same time, the buildings are decomposed into several patches which lay a good foundation for building reconstruction.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 6,358 TCR
SCOPUS® Citations for all references: 9,503 TCR

Web of Science® Average Citations per reference: 227 ACR
SCOPUS® Average Citations per reference: 339 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-04-26 03:21 in 91 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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