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Automated vector-vector and vector-i...

Song, Wenbo.

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Automated vector-vector and vector-imagery geospatial conflation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automated vector-vector and vector-imagery geospatial conflation./
作者:	Song, Wenbo.
面頁冊數:	285 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.
Contained By:	Dissertation Abstracts International74-04B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3533872
ISBN:	9781267793713

Automated vector-vector and vector-imagery geospatial conflation.
Song, Wenbo.

Automated vector-vector and vector-imagery geospatial conflation. - 285 p.

Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.

Thesis (Ph.D.)--University of Missouri - Columbia, 2011.

This item is not available from ProQuest Dissertations & Theses.

With the rapid advance of geospatial technologies, the availability of geospatial data from multiple sources has increased dramatically. Integration of multi-source geospatial data can provide insights and capabilities not possible with individual datasets. However, multi-source datasets over the same geographical area are often disparate and do not match well with each other. Accurately integrating geospatial data from different sources is a challenging task. In this dissertation research, we proposed a set of innovative geospatial conflation algorithms to attack the multi-source geospatial integration/conflation problem. We developed a novel snake-based approach to conflate two vector road datasets which has several benefits over traditional conflation methods. Since feature matching is one of the most crucial subtasks of conflation, we proposed a new relaxation labeling-based point matching algorithm to provide an elegant and well-motivated solution to the conflation problem. For the vector-to-imagery conflation, we presented a comprehensive approach by integrating several vector-based and image-based algorithms including spatial contextual signature extraction, road intersections and terminations extraction, relaxation labeling-based point matching, piecewise rubber-sheeting transformation, and snake-based refinement. Finally we extended our road conflation approach to digital parcel map to make it consistent with high-resolution imagery. The experiments on real world geospatial datasets showed excellent results.

ISBN: 9781267793713Subjects--Topical Terms:

626642
Computer Science.

Automated vector-vector and vector-imagery geospatial conflation.
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245 1 0 $a Automated vector-vector and vector-imagery geospatial conflation.
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500 $a Source: Dissertation Abstracts International, Volume: 74-04(E), Section: B.
500 $a Adviser: James M. Keller.
502 $a Thesis (Ph.D.)--University of Missouri - Columbia, 2011.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a With the rapid advance of geospatial technologies, the availability of geospatial data from multiple sources has increased dramatically. Integration of multi-source geospatial data can provide insights and capabilities not possible with individual datasets. However, multi-source datasets over the same geographical area are often disparate and do not match well with each other. Accurately integrating geospatial data from different sources is a challenging task. In this dissertation research, we proposed a set of innovative geospatial conflation algorithms to attack the multi-source geospatial integration/conflation problem. We developed a novel snake-based approach to conflate two vector road datasets which has several benefits over traditional conflation methods. Since feature matching is one of the most crucial subtasks of conflation, we proposed a new relaxation labeling-based point matching algorithm to provide an elegant and well-motivated solution to the conflation problem. For the vector-to-imagery conflation, we presented a comprehensive approach by integrating several vector-based and image-based algorithms including spatial contextual signature extraction, road intersections and terminations extraction, relaxation labeling-based point matching, piecewise rubber-sheeting transformation, and snake-based refinement. Finally we extended our road conflation approach to digital parcel map to make it consistent with high-resolution imagery. The experiments on real world geospatial datasets showed excellent results.
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