Automated vector-vector and vector-imagery geospatial conflation
No Thumbnail Available
Authors
Meeting name
Sponsors
Date
Journal Title
Format
Thesis
Abstract
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.
Table of Contents
DOI
PubMed ID
Degree
Ph. D.
Thesis Department
Rights
OpenAccess.
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.