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Morse-Smale Decompositions for Modeling Terrain Knowledge

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Spatial Information Theory (COSIT 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3693))

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Abstract

In this paper, we describe, analyze and compare techniques for extracting spatial knowledge from a terrain model. Specifically, we investigate techniques for extracting a morphological representation from a terrain model based on an approximation of a Morse-Smale complex. A Morse-Smale complex defines a decomposition of a topographic surface into regions with vertices at the critical points and bounded by integral lines which connect passes to pits and peaks. This provides a terrain representation which encompasses the knowledge on the salient characteristics of the terrain. We classify the various techniques for computing a Morse-Smale complexe based on the underlying terrain model, a Regular Square Grid (RSG) or a Triangulated Irregular Network (TIN), and based on the algorithmic approach they apply. Finally, we discuss hierarchical terrain representations based on a Morse-Smale decomposition.

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Author information

Authors and Affiliations

  1. Department of Information and Computer Sciences (DISI), University of Genova,  

    Lidija Čomić, Leila De Floriani & Laura Papaleo

Authors
  1. Lidija Čomić
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  2. Leila De Floriani
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  3. Laura Papaleo
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Editor information

Editors and Affiliations

  1. School of Computing, University of Leeds, LS2 9JT, Leeds, United Kingdom

    Anthony G. Cohn

  2. Department of Geography, State University of New York at Buffalo, 105 Wilkeson Quad, 14261, Buffalo, NY, USA

    David M. Mark

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© 2005 Springer-Verlag Berlin Heidelberg

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Čomić, L., De Floriani, L., Papaleo, L. (2005). Morse-Smale Decompositions for Modeling Terrain Knowledge. In: Cohn, A.G., Mark, D.M. (eds) Spatial Information Theory. COSIT 2005. Lecture Notes in Computer Science, vol 3693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11556114_27

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  • DOI: https://doi.org/10.1007/11556114_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28964-7

  • Online ISBN: 978-3-540-32020-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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