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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 78 days
Avg accept to publ: 48 days
APC: 300 EUR


PUBLISHER

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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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
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2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  3/2013 - 2

 HIGH-IMPACT PAPER 

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: 810 | Views: 4,503

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), building(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

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 12
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Cited-By CrossRef

[1] PointNAC: Copula-Based Point Cloud Semantic Segmentation Network, Deng, Chunyuan, Chen, Ruixing, Tang, Wuyang, Chu, Hexuan, Xu, Gang, Cui, Yue, Peng, Zhenyun, Symmetry, ISSN 2073-8994, Issue 11, Volume 15, 2023.
Digital Object Identifier: 10.3390/sym15112021
[CrossRef]

[2] A Novel Keep Zero as Zero Polar Correlation Technique for Mobile Robot Localization using LIDAR, SIDHARTHAN, R. K., KANNAN, R., SRINIVASAN, S., BALAS, M. M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 16, 2016.
Digital Object Identifier: 10.4316/AECE.2016.04003
[CrossRef] [Full text]

[3] A multi‐scale plane‐detection method based on the Hough transform and region growing, Leng, Xiaoxu, Xiao, Jun, Wang, Ying, The Photogrammetric Record, ISSN 0031-868X, Issue 154, Volume 31, 2016.
Digital Object Identifier: 10.1111/phor.12145
[CrossRef]

[4] A Window Detection Algorithm for Remote Laser Gas Leakage Detection System, Dong, Xiaozhou, Li, Jiaying, Wu, Junxian, Liu, Juan, Procedia Computer Science, ISSN 1877-0509, Issue , 2020.
Digital Object Identifier: 10.1016/j.procs.2020.06.148
[CrossRef]

[5] Slice-Based Window Detection from Scene Point Clouds, Hao, Wen, Wang, Yinghui, Liang, Wei, Ning, Xiaojuan, Li, Ye, 2018 International Conference on Virtual Reality and Visualization (ICVRV), ISBN 978-1-5386-8497-9, 2018.
Digital Object Identifier: 10.1109/ICVRV.2018.00014
[CrossRef]

[6] Extraction of lattice openings of buildings from terrestrial LiDAR data, Susaki, Junichi, 2014 8th IAPR Workshop on Pattern Reconition in Remote Sensing, ISBN 978-1-4799-7276-0, 2014.
Digital Object Identifier: 10.1109/PRRS.2014.6914285
[CrossRef]

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


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