Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/5A0ABF9C-7D65-4D2B-9024-1AF061EE124C | ||
| Year | 2017 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | M. Papadogiorgaki and P. Partsinevelos, "Adaptive DTM generalization methods for tangible GIS applications," Earth Sci. Inform., vol. 10, no.4, pp. 483-494, Dec. 2017. doi:10.1007/s12145-017-0311-9 https://doi.org/10.1007/s12145-017-0311-9 | ||
| Appears in Collections |
Several international studies attempt to construct tangible GIS systems, forming real 3D surfaces using a large number of mechanical parts along a matrix formation. Most of these attempts suffer in cost, accuracy, resolution and/or speed. In order to facilitate data handling in GIS tangible systems, the generalization process becomes crucial, accommodating compression, visualization and comprehension of spatial data under various scales. Under this perspective, the main objective of the proposed adaptive generalization approach is to provide optimized representation of 3D digital terrain models with minimum loss of information, serving specific applications. That is, to minimize the number of pixels in a raster dataset used to define a DTM, while reserving surface information and enhancing the important semantic features. To this aim, this paper presents specific computationally efficient surface generalization approaches, which can be incorporated in applications of tangible GIS systems, due to their low processing time, facilitating real-time results. The research methods developed and tested include adaptive variations of a) the Douglas-Peucker line simplification algorithm in 3D data and b) the spatial Laplace filter that estimates the significance of each node, based on its nearest neighbors. The proposed strategy also dynamically incorporates topology and semantic restraints, in order to preserve important information and depict it with variable detail level, according to the application at hand. The algorithms are evaluated on their accuracy for a specific wildfire application and for different reduction levels in two different types of terrain data. They are also compared to their original-basic implementations in terms of performance.
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