Wu Chen, Zhu Qing, Zhang Yeting, Xu Weipin, Xie Xiao, Zhou Yan. An Adaptive Organization Method for Complex 3D City Models Considering User Experience[J]. Geomatics and Information Science of Wuhan University, 2014, 39(11): 1293-1297.
Citation: Wu Chen, Zhu Qing, Zhang Yeting, Xu Weipin, Xie Xiao, Zhou Yan. An Adaptive Organization Method for Complex 3D City Models Considering User Experience[J]. Geomatics and Information Science of Wuhan University, 2014, 39(11): 1293-1297.

An Adaptive Organization Method for Complex 3D City Models Considering User Experience

More Information
  • Received Date: July 07, 2013
  • Published Date: November 04, 2014
  • The traditional method for visualization of 3D city models based on a“Layer-Object" doesnot consider the granularity differences in different data type and in different levels of detail,which recults in the low transmission efficiency in the network environment and does not satisfy the need forsmooth visualization with multi-user concurrent accesses. This paper analyzes the characteristics of anuser experience including roaming over a wide range and focusing on small range,and proposes amethod of using an index metadata to coordinate the organization and scheduling of model objects,which delivers instant responses to user requests,and at the same time,by decomposing model obsects,decreases invalid data transmission. This paper describes the design of unified object ID strumsture to implicitly store the relationships of LoD models and decomposed objects to support distributedmanagement. Finally,the validity and feasibility of this method are demonstrated through experi-menu on the platform of a distributed database MongoDB.
  • Related Articles

    [1]XU Ren, SAIMAITI A-li-mu, LI Er-zhu, WANG Wei. Task-oriented Alignment for Unsupervised Domain Adaptation of Remote sensing scene image classification[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230084
    [2]WU Yiquan, TAO Feixiang, CAO Zhaoqing. Remote Sensing Image Classification Based on Log-Gabor Wavelet and Krawtchouk Moments[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 861-867. DOI: 10.13203/j.whugis20140234
    [3]XU Kai, QIN Kun, LIU Xiuguo, LI Dengchao. Pan-concept-level Generation Method and Its Application in Remote Sensing Image Classification[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1078-1082.
    [4]ZHENG Wenwu, ZENG Yongnian. Remote Sensing Imagery Classification Based on Multiple Classifiers Combination Algorithm[J]. Geomatics and Information Science of Wuhan University, 2011, 36(11): 1290-1293.
    [5]XU Kai, QIN Kun, DU Yi. Classification for Remote Sensing Data with Decision Level Fusion[J]. Geomatics and Information Science of Wuhan University, 2009, 34(7): 826-829.
    [6]ZHONG Yanfei, ZHANG Liangpei, LI Pingxiang. Fuzzy Cluster Validation for Remote Sensing Image Classification[J]. Geomatics and Information Science of Wuhan University, 2009, 34(4): 391-394.
    [7]YIN Shuling, SHU Ning, LIU Xinhua. Classification of Remote Sensing Image Based on Adaptive GA and Improved BP Algorithm[J]. Geomatics and Information Science of Wuhan University, 2007, 32(3): 201-204.
    [8]LUO Hongxia, GONG Jianya. Effect Analysis of UFCLS Linear Spectral Mixture Analysis Method on Classification of Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2004, 29(7): 615-618.
    [9]Zhang Jingxiong. A Fully Fuzzy Supervised Classification of Remotely Sensed Imagery[J]. Geomatics and Information Science of Wuhan University, 1998, 23(3): 211-214.
    [10]J ia Yonghong, Li Deren. Multisource Classification of Remotely Sensed Data Based on Bayesian Data Fusion Method[J]. Geomatics and Information Science of Wuhan University, 1997, 22(3): 246-251.

Catalog

    Article views (1151) PDF downloads (599) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return