Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review,        editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Name
E-mail
Phone
Title
Content
Verification Code
Turn off MathJax
Article Contents

LI Anping, ZHAI Renjian, YIN Jichong, ZHU Li, QI Linjun. Automatic Aggregation of Building Considering the Spatial Structure[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210731
Citation: LI Anping, ZHAI Renjian, YIN Jichong, ZHU Li, QI Linjun. Automatic Aggregation of Building Considering the Spatial Structure[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210731

Automatic Aggregation of Building Considering the Spatial Structure

doi: 10.13203/j.whugis20210731
Funds:

Henan Province Outstanding Youth Fund Project (212300410014)

  • Received Date: 2021-12-27
  • Building aggregation is one of the main contents in map generalization at the micro scale, which is of great significance to map compilation and multi-scale representation of spatial data. In order to maintain the consistency of spatial characteristics, a building aggregation method considering the spatial structure is proposed. Firstly, based on distance-adaptive Delaunay triangles, the relationships of spatial structure between adjacent buildings are divided into six types. Meanwhile, these spatial structures are summarized as positive bridging type and oblique bridging type according to the bridging surfaces. Then, the bridging area is constructed according to bridging triangles, which are identified by projective overlap lines between adjacent buildings. The rectangular method is applied to the bridging area to maintain the spatial structure relationship. At the same time, the calculation of bridging distance between adjacent buildings is put forward for building clustering, which can meet the mapping requirements and cognitive habits. Finally, the effectiveness and universality of our method are verified by several experiments. Moreover, the comparative experiment shows that our method has advantage in maintaining the spatial structure characteristics and geometrical characteristics, including area consistency and rectangularity.
  • [1] Ai T H, Zhang X.The Aggregation of Urban Building Clusters Based on the Skeleton Partitioning of Gap Space[M]//Lecture Notes in Geoinformation and Cartography.Berlin, Heidelberg:Springer Berlin Heidelberg, 2007:153-170
    [2] He X J, Zhang X C, Yang J.Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups[J].ISPRS International Journal of Geo-Information, 2018, 7(3):116
    [3] Su B, Li Z L, Lodwick G, et al.Algebraic Models for the Aggregation of Area Features Based Upon Morphological Operators[J].International Journal of Geographical Information Science, 1997, 11(3):233-246
    [4] Shen Y L, Ai T H, Li W D, et al.A Polygon Aggregation Method with Global Feature Preservation Using Superpixel Segmentation[J].Computers, Environment and Urban Systems, 2019, 75:117-131
    [5] Peng D L, Touya G.Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing[C]//Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics.Redondo Beach, CA, USA.2017:1-8
    [6] Li Z, Yan H, Ai T, et al.Automated Building Generalization Based on Urban Morphology and Gestalt Theory[J].International Journal of Geographical Information Science, 2004, 18(5):513-534
    [7] Yan H W, Weibel R, Yang B S.A Multi-Parameter Approach to Automated Building Grouping and Generalization[J].GeoInformatica, 2008, 12(1):73-89
    [8] Regnauld N, Revell P.Automatic Amalgamation of Buildings for Producing Ordnance Survey® 1:50000 Scale Maps[J].The Cartographic Journal, 2007, 44(3):239-250
    [9] Ai T H, Yin H M, Shen Y L, et al.A Formal Model of Neighborhood Representation and Applications in Urban Building Aggregation Supported by Delaunay Triangulation[J].PLoS One, 2019, 14(7):e0218877
    [10] REGNAULD N.Algorithms for the amalgamation of topographic data[C]//Proceedings of the 21st international cartographic conference.Durban, South Africa, 2003:222-234
    [11] Regnauld N.Contextual Building Typification in Automated Map Generalization[J].Algorithmica, 2001, 30(2):312-333
    [12] Papadias D, Theodoridis Y.Spatial Relations, Minimum Bounding Rectangles, and Spatial Data Structures[J].International Journal of Geographical Information Science, 1997, 11(2):111-138
    [13] Du S H, Shu M, Feng C C.Representation and Discovery of Building Patterns:A Three-Level Relational Approach[J].International Journal of Geographical Information Science, 2016, 30(6):1161-1186
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(88) PDF downloads(10) Cited by()

Related
Proportional views

Automatic Aggregation of Building Considering the Spatial Structure

doi: 10.13203/j.whugis20210731
Funds:

Henan Province Outstanding Youth Fund Project (212300410014)

Abstract: Building aggregation is one of the main contents in map generalization at the micro scale, which is of great significance to map compilation and multi-scale representation of spatial data. In order to maintain the consistency of spatial characteristics, a building aggregation method considering the spatial structure is proposed. Firstly, based on distance-adaptive Delaunay triangles, the relationships of spatial structure between adjacent buildings are divided into six types. Meanwhile, these spatial structures are summarized as positive bridging type and oblique bridging type according to the bridging surfaces. Then, the bridging area is constructed according to bridging triangles, which are identified by projective overlap lines between adjacent buildings. The rectangular method is applied to the bridging area to maintain the spatial structure relationship. At the same time, the calculation of bridging distance between adjacent buildings is put forward for building clustering, which can meet the mapping requirements and cognitive habits. Finally, the effectiveness and universality of our method are verified by several experiments. Moreover, the comparative experiment shows that our method has advantage in maintaining the spatial structure characteristics and geometrical characteristics, including area consistency and rectangularity.

LI Anping, ZHAI Renjian, YIN Jichong, ZHU Li, QI Linjun. Automatic Aggregation of Building Considering the Spatial Structure[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210731
Citation: LI Anping, ZHAI Renjian, YIN Jichong, ZHU Li, QI Linjun. Automatic Aggregation of Building Considering the Spatial Structure[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210731
Reference (13)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return