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author:

Wu, B. (Wu, B..) [1] | Wu, S. (Wu, S..) [2] | Li, Y. (Li, Y..) [3] | Wu, J. (Wu, J..) [4] | Huang, Y. (Huang, Y..) [5] | Chen, Z. (Chen, Z..) [6] | Yu, B. (Yu, B..) [7]

Indexed by:

Scopus

Abstract:

Automatic building rooftop extraction is of great importance to many applications including building reconstruction, solar energy supply, and disaster management. This study proposes a building rooftop extraction method using DSM data generated from aerial stereo images and vegetation cover vector data. The method consists of five steps: noise filtering, dilation reconstruction, vegetation and terrain region removal, region growing and merging, and post-processing. We applied the proposed method to the centre of Shanghai, China, a typical urban area. Experimental results show that the proposed method can successfully extract building rooftops, with an approximately 82.6% quality percentage and 96.2% matched overlay. © 2020, © 2020 Mapping Science Institute, Australia and Surveying and Spatial Science Institute.

Keyword:

Building rooftop; digital surface model; dilation reconstruction; large urban area; region growing and merging

Community:

  • [ 1 ] [Wu, B.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China
  • [ 2 ] [Wu, B.]School of Geographic Sciences, East China Normal University, Shanghai, China
  • [ 3 ] [Wu, S.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China
  • [ 4 ] [Wu, S.]School of Geographic Sciences, East China Normal University, Shanghai, China
  • [ 5 ] [Li, Y.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China
  • [ 6 ] [Li, Y.]School of Geographic Sciences, East China Normal University, Shanghai, China
  • [ 7 ] [Wu, J.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China
  • [ 8 ] [Wu, J.]School of Geographic Sciences, East China Normal University, Shanghai, China
  • [ 9 ] [Huang, Y.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China
  • [ 10 ] [Huang, Y.]School of Geographic Sciences, East China Normal University, Shanghai, China
  • [ 11 ] [Chen, Z.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, China
  • [ 12 ] [Chen, Z.]The Academy of Digital China, Fuzhou University, Fuzhou, China
  • [ 13 ] [Yu, B.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China
  • [ 14 ] [Yu, B.]School of Geographic Sciences, East China Normal University, Shanghai, China

Reprint 's Address:

  • [Yu, B.]Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal UniversityChina

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Source :

Journal of Spatial Science

ISSN: 1449-8596

Year: 2020

1 . 9 8 1

JCR@2020

1 . 0 0 0

JCR@2023

ESI HC Threshold:115

JCR Journal Grade:4

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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