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  本文選題：地理信息系統(tǒng) + 地理信息鏈接��； 參考：《華北電力大學(北京)》2017年碩士論文

【摘要】：GIS是最近二十多年來新興的一門集地理信息科學、計算機科學、測繪科學和統(tǒng)計科學等于一體的一門綜合性學科,是用于輸入、存儲、查詢、分析和展示地理數(shù)據(jù)的信息系統(tǒng),以便及時解決地理信息處理中復雜的規(guī)劃和管理問題。目前,GIS技術(shù)已經(jīng)廣泛的應用在各種的領(lǐng)域當中,GIS和應用模型的集成以及GIS智能化是拓寬GIS應用領(lǐng)域的關(guān)鍵。在地理信息系統(tǒng)中,不同地理信息源的內(nèi)容之間的多樣性、異構(gòu)性等對地理信息實體描述的準確性、完整性等有很大的差異,這對實現(xiàn)地理信息共享以及促進地理信息技術(shù)的發(fā)展都產(chǎn)生了阻礙。不同地理信息源的地理信息實體之間鏈接的精確性對解決地理信息異構(gòu)性,促進地理信息檢索服務的準確性和地理信息集成等問題具有十分重要的意義。目前,大多數(shù)的研究工作基于語義關(guān)系、信息內(nèi)容、上下文信息等來計算地理信息實體的相似性,忽略了地理空間關(guān)系和空間拓撲結(jié)構(gòu)的作用。本文提出了一種基于空間關(guān)系、實體名稱和實體類別等多特征的方式,同時結(jié)合語義和機器學習的方法實現(xiàn)的地理信息鏈接的半自動化方式。首先,分別從三個地理信息源:OpenStreetMap、Wikimapia、Google places抽取地理信息,抽取的地理信息主要針對美國洛杉磯和英國倫敦兩個地區(qū)的城區(qū)建筑。其次,分析抽取地理信息數(shù)據(jù)的特點構(gòu)建地理信息本體,通過地理信息源數(shù)據(jù)與地理信息本體映射,實現(xiàn)地理數(shù)據(jù)的一體化。最后,分別討論融合分類算法支持向量機、K近鄰方法的鏈接方法,同時與Samal等人提出的鏈接方法進行對比,多角度綜合驗證本文提出方法的準確性,為地理信息集成奠定了良好的基礎。
[Abstract]:GIs is an integrated subject that integrates geographic information science, computer science, mapping science and statistical science. It is an information system for input, storage, query, analysis and display of geographic data. In order to solve the complex planning and management problems in geographic information processing in time. At present, GIS technology has been widely used in various fields of GIS and application model integration and GIS intelligence is the key to broaden the application of GIS. In GIS, the diversity and heterogeneity of the contents of different geographic information sources have great differences in the accuracy and completeness of the description of geographic information entities. This hinders the realization of geographic information sharing and the development of geographic information technology. The accuracy of the links between geographic information entities from different geographic information sources is of great significance in solving the problems of geographic information heterogeneity, promoting the accuracy of geographic information retrieval services and geographic information integration. At present, most of the research work based on semantic relations, information content, context information to calculate the similarity of geographic information entities, ignoring the role of geospatial relationships and spatial topology. In this paper, a semi-automatic method of geographic information link based on spatial relationship, entity name and entity category is proposed, which combines semantic and machine learning methods. First of all, three geographic information sources: OpenStreetMap WikimapiaGoogle places are extracted, which are mainly aimed at the urban buildings in Los Angeles and London. Secondly, the characteristics of extracting geographic information data are analyzed to construct geographical information ontology, and the integration of geographical data is realized by mapping geographical information source data with geographic information ontology. Finally, the link method of the fusion classification algorithm support vector machine / K-nearest neighbor method is discussed, and compared with the link method proposed by Samal et al., the accuracy of the proposed method is verified by multi-angle synthesis. It lays a good foundation for geographic information integration.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P208;TP181

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本文編號：2096194