XU Li, YANG Chuncheng, LIU Peng, LIU Mingxuan, CHEN Jiudong, DAI Jilong. Research on 3D Geospatial PDF Map Data Organization Model[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240227
Citation: XU Li, YANG Chuncheng, LIU Peng, LIU Mingxuan, CHEN Jiudong, DAI Jilong. Research on 3D Geospatial PDF Map Data Organization Model[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240227

Research on 3D Geospatial PDF Map Data Organization Model

More Information
  • Received Date: September 24, 2024
  • Objectives: The three-dimensional (3D) electronic map has the advantage of representing the objective world in a vivid and intuitive way, which is of great significance for the abstract simulation and cognition of the three-dimensional geographical environment. Traditional 3D electronic maps have a large amount of data, diverse data formats, and rely on professional software tools, which increases the difficulty of using maps. In the era of informatization, ordinary users are familiar with the portable document format (PDF), and the PDF format has better universality. In order to adapt to the trend of popular map application and reduce the difficulty of ordinary users to use maps, this paper explores the design of a new type of 3D map data organization model -- 3D Geospatial PDF Map Data Organization Model, which formally expresses 3D maps based on PDF format. Methods: Firstly, we elaborate the concept of 3D geospatial PDF map, designed the data organization structure and propose the organizational structure of 3D geospatial PDF map. We also designed the data storage structure based on the PDF format and the Universal 3D (U3D) model. Secondly, based on our 3D geospatial PDF map data organization model, experiments were conducted on the production of 3D geospatial PDF maps using terrain data, oblique photography 3D data, and urban 3D data. Finally, we discuss and analyze the typical features of 3D geospatial PDF map data organization model. Results: (1) Three 3D geospatial PDF map were produced based on our 3D geospatial PDF map data organization model, with clear and beautiful visual effects; (2) 3D geospatial PDF map has both basic map features and PDF lightweight features, namely, map scene element visualization, geospatial reference, three-dimensional geographic entity structure, storage lightweight, form lightweight and application lightweight. Conclusions: This research provides map users with a 3D map solution based on the PDF format, which is helpful to expand the user group and application scenarios of 3D electronic map.
  • Related Articles

    [1]ZHANG Lefei, HE Fazhi. Hyper-spectral Image Rank-Reducing and Compression Based on Tensor Decomposition[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2): 193-197. DOI: 10.13203/j.whugis20140688
    [2]LIAO Lu, LI Pingxiang, YANG Jie, CHANG Hong. An Improved Method to SAR Polarimetric Calibration Based on Reciprocity Judgement Using Distributed Target[J]. Geomatics and Information Science of Wuhan University, 2015, 40(8): 1042-1047. DOI: 10.13203/j.whugis20140096
    [3]FU Haiqiang, WANG Changcheng, ZHU Jianjun, XIE Qinghua, ZHAO Rong. A Polarimetric Classification Method Based on Neumann Decomposition[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 607-611. DOI: 10.13203/j.whugis20130372
    [4]ZHANG Jianqing, DUAN Yan. A Supervised Classification Method of Polarimetric Sythetic ApertureRadar Data Using Watershed Segmentation and Decision Tree C5.0[J]. Geomatics and Information Science of Wuhan University, 2014, 39(8): 891-896. DOI: 10.13203/j.whugis20120112
    [5]chen qihao,  liu xiuguo,  huang xiaodong,  jiang ping. an inte grated four-component model-based decomposition  of polarimetric sar with covariance matrix[J]. Geomatics and Information Science of Wuhan University, 2014, 39(7): 873-877.
    [6]ZHANG Bin, MA Guorui, LIU Guoying, QIN Qianqing. MRF-Based Segmentation Algorithm Combined with Freeman Decomposition and Scattering Entropy for Polarimetric SAR Images[J]. Geomatics and Information Science of Wuhan University, 2011, 36(9): 1064-1067.
    [7]ZHANG Bin, YANG Ran, XIE Xing, QIN Qianqing. Classification of Fully Polarimetric SAR Image Based on Polarimetric Target Decomposition and Wishart Markov Random Field[J]. Geomatics and Information Science of Wuhan University, 2011, 36(3): 297-300.
    [8]YANG Jie, LANG Fengkai, LI Deren. An Unsupervised Wishart Classification for Fully Polarimetric SAR Image Based on Cloude-Pottier Decomposition and Polarimetric Whitening Filter[J]. Geomatics and Information Science of Wuhan University, 2011, 36(1): 104-107.
    [9]ZHANG Haijian, YANG Wen, ZOU Tongyuan, SUN Hong. Classification of Polarimetric SAR Image Based on Four-component Scattering Model[J]. Geomatics and Information Science of Wuhan University, 2009, 34(1): 122-125.
    [10]WANG Wenbo, FEI Pusheng, YI Xuming, ZHANG Jianguo. Denoising of SAR Images Based on Lifting SchemeWavelet Packet Transform[J]. Geomatics and Information Science of Wuhan University, 2007, 32(7): 585-588.

Catalog

    Article views PDF downloads Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return