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Full-Text Articles in Geographic Information Sciences
The Image From The Road: Towards Mapping The Phenomenological, Rachel Anna Smith Loerts
The Image From The Road: Towards Mapping The Phenomenological, Rachel Anna Smith Loerts
Graduate Theses and Dissertations
An area of focus, used in early and contemporary forms of cognitive geography research, is the ‘cognitive map’, a concept that suggests “that people hold a map-like database in their minds to which they can add and use to tackle geographical tasks”. Kevin Lynch, an urban planner in the 1960s, was an early adopter of the cognitive map approach to reveal spatial cognition, what or how people see their environment, specifically cognition of the urban environment. Lynch’s research aimed to develop empirical methods, to identify how people make spatial relationships. Contemporary tools like machine learning are now considered relevant for …
Mapsnap System To Perform Vector-To-Raster Fusion, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Jerry Tagestad, Sergei Kovalerchuk, Brian Graff
Mapsnap System To Perform Vector-To-Raster Fusion, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Jerry Tagestad, Sergei Kovalerchuk, Brian Graff
All Faculty Scholarship for the College of the Sciences
As the availability of geospatial data increases, there is a growing need to match these datasets together. However, since these datasets often vary in their origins and spatial accuracy, they frequently do not correspond well to each other, which create multiple problems. To accurately align with imagery, analysts currently either: 1) manually move the vectors, 2) perform a labor-intensive spatial registration of vectors to imagery, 3) move imagery to vectors, or 4) redigitize the vectors from scratch and transfer the attributes. All of these are time consuming and labor-intensive operations. Automated matching and fusing vector datasets has been a subject …
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Computer Science Faculty Scholarship
The variability of panchromatic and multispectral images, vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of vector (feature) and raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, vectorized, and …