Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2014, Vol. 26 Issue (1) : 173-178     DOI: 10.6046/gtzyyg.2014.01.29
|
General geo-spatial database construction method based on data dictionary
ZHANG Long1, WANG Xinqing1,2
1. Institute of Mathematical Geology and Remote Sensing Geology, China University of Geosciences(Wuhan), Wuhan 430074, China;
2. State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences(Wuhan), Wuhan 430074, China
Download: PDF(1186 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Spatial database is a spatial information infrastructure, and the efficiency and quality of its construction determine the success or failure of the geo-information project. Currently, the spatial database is mostly associated with a specific GIS platform. The database construction process is complex, inefficient and lack of versatility. In order to adapt the database construction to complex variability, the authors, through an analysis of the structural expression of the spatial database data model, studied and put forward the method for storing data model by using data dictionary and the technology for automatic construction of a spatial database. Practice of quite a few projects has proved that the method can significantly reduce the complexity of building a database and improve efficiency, together with certain extent of versatility.

Keywords remote sensing      soil classification      TM image      terrain data      GeoEye-1     
:  P283.7  
Issue Date: 08 January 2014
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LIU Juan
CAI Yanjun
WANG Jin
Cite this article:   
LIU Juan,CAI Yanjun,WANG Jin. General geo-spatial database construction method based on data dictionary[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(1): 173-178.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.01.29     OR     https://www.gtzyyg.com/EN/Y2014/V26/I1/173

[1] 左泽均, 周顺平.基于MapGIS数据中心构建基础地理信息地图集模型[J].地球科学:中国地质大学学报, 2010, 35(3):391-396. Zuo Z J, Zhou S P.Building basic geographic information mapset model based on MapGIS data center[J].Earth Science:Journal of China University of Geosciences, 2010, 35(3):391-396.

[2] 李定平, 胡光道, 程路.MapGIS下空间数据库的建立及其典型问题研究[J].武汉大学学报:信息科学版, 2005, 30(11):92-95. Li D P, Hu G D, Cheng L.The building of spatial database based on MapGIS and the discussion of its typical questions[J].Geomatics and Information Science of Wuhan University, 2005, 30(11):92-95.

[3] 李连胜.Geodatabase在空间数据库建库中的应用[J].现代计算机:专业版, 2009(12):131-134. Li L S.Application of Geodatabase in construction of spatial database[J].Modern Computer, 2009(12):131-134.

[4] 何勇, 辜寄蓉, 江浏光艳.基于CASE工具的地理空间数据库建模方法研究[J].成都理工大学学报:自然科学版, 2007, 34(2):185-189. He Y, Gu J R, Jiang L G Y.Design of the geodatabase based on CASE tools[J].Journal of Chengdu University of Technology:Science and Technology Edition, 2007, 34(2):185-189.

[5] 陈勇, 宋关福, 钟耳顺.基于可视化建模思想的空间数据库自动化建库技术研究[J].测绘通报, 2006(12):54-56. Chen Y, Song G F, Zhong E S.A study of spatial database automated construction based on visual modeling[J].Bulletin of Surveying and Mapping, 2006(12):54-56.

[6] 张泽烈, 余静, 袁超.基于模板控制的地理空间数据库自动化建库方法[J].测绘通报, 2010(5):20-22. Zhang Z L, Yu J, Yuan C.A method of geo-spatial database automated construction based on template control[J].Bulletin of Surveying and Mapping, 2010(5):20-22.

[7] 马小刚, 汪新庆, 毋丽红, 等.应用数据字典实现多源地质空间数据的通用管理[J].矿业研究与开发, 2007, 27(1):37-40. Ma X G, Wang X Q, Wu L H, et al.Implement of universal information models for multi-source geology spatial database management based on data dictionary[J].Mining Research and Development, 2007, 27(1):37-40.

[8] 徐翠玲.基于Geodatabase建立数字地质图数据库的方法与实践[J].测绘科学, 2008, 33(3):176-177, 186. Xu C L.Methods and practice of building database for digital geological map based on Geodatabase[J].Science of Surveying and Mapping, 2008, 33(3):176-177, 186.

[9] 邵亚.基于Geodatabase的土地利用空间数据库建库研究[D].武汉:华中农业大学, 2007. Shao Y.Research on construction of land use spatial database based on Geodatabase[D].Wuhan:Huazhong Agricultural University, 2007.

[10] 薛涛, 刁明光, 李建存, 等.资源环境遥感海量空间数据存储、检索和访问方法[J].国土资源遥感, 2013, 25(2):168-173. Xue T, Diao M G, Li J C, et al.Approach to storing, retrieving and accessing mass spatial data in resources and environments remote sensing[J].Remote Sensing for Land and Resources, 2013, 25(2):168-173.

[11] 李禹生, 何健, 彭飞.VFP数据字典工具及其应用系统维护[J].武汉工业学院学报, 2003, 22(2):32-34. Li Y S, He J, Peng F.The tools of VFP data dictionary and the maintenance of VFP application system[J].Journal of Wuhan Polytechnic University, 2003, 22(2):32-34.

[12] 李阳东, 汪新庆, 刘妙龙.基于国标的地学数据库智能建模方法[J].同济大学学报:自然科学版, 2007, 35(5):690-694. Li Y D, Wang X Q, Liu M L.An intelligent modeling method for geosciences database based on national standard terms and codes[J].Journal of Tongji University:Natural Science, 2007, 35(5):690-694.

[13] 刘夏, 汪新庆, 常思思, 等.矿产资源潜力评价数据模型应用支持研究[J].电子科技, 2010, 23(5):23-25. Liu X, Wang X Q, Chang S S, et al.Supporting research on application of the mineral resource potential evaluation data model[J].Electronic Science and Technology, 2010, 23(5):23-25.

[14] 常思思, 汪新庆, 过剑, 等.矿产资源潜力评价中定性数据标准化检查[J].物探化探计算技术, 2010, 32(3):320-324. Chang S S, Wang X Q, Guo J, et al.Standard check of qualitative data on the mineral resources potential assessment[J].Computing Techniques for Geophysical and Geochemical Exploration, 2010, 32(3):320-324.

[15] 全国重要矿产资源潜力评价项目组.矿产资源潜力评价数据模型丛书[M].北京:地质出版社, 2011. Project Group of the Country's Major Mineral Resource Potential Assessment.The series of mineral resource potential assessment data model[M].Beijing:Geology Press, 2011.

[16] 邬晓芳, 邓毅, 王常薇, 等.如何运用GeoMAG软件使图件结构规范化——以贵州省分幅实际材料图为例[J].贵州地质, 2012, 29(2):156-159. Wu X F, Deng Y, Wang C W, et al.How to make the graph structure standardization by GeoMAG software:The division factual datum map is taken as the example[J].Guizhou Geology, 2012, 29(2):156-159.

[1] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[3] LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[4] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[5] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[6] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[7] LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[8] DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[9] GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[10] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[11] HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[12] AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[13] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[14] LIU Bailu, GUAN Lei. An improved method for thermal stress detection of coral bleaching in the South China Sea[J]. Remote Sensing for Natural Resources, 2021, 33(4): 136-142.
[15] WU Fang, JIN Dingjian, ZHANG Zonggui, JI Xinyang, LI Tianqi, GAO Yu. A preliminary study on land-sea integrated topographic surveying based on CZMIL bathymetric technique[J]. Remote Sensing for Natural Resources, 2021, 33(4): 173-180.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech