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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
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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
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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.
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[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.

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