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Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms

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Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017) (COSIT 2017)

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

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Abstract

Landform objects extracted from Geographic Object Based Image Analysis (GEOBIA) based terrain segmentation to locations are overlaid and compared to feature types of landforms mapped in the USGS maintained Geographic Names Information System (GNIS) topographic database. GEOBIA terrain objects were found to statistically related to GNIS feature classes. Comparison of GNIS feature classes and GEOBIA landform classes suggests that GEOBIA landform class semantics correspond well with naïve geographic conceptualizations reflected in GNIS feature types.

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Acknowledgements

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Authors and Affiliations

  1. Center of Excellence for Geographic Information Science (CEGIS), USGS, Rolla, MO, USA

    Samantha T. Arundel

  2. Department of Geography, Ohio University, Athens, OH, USA

    Gaurav Sinha

Authors
  1. Samantha T. Arundel
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  2. Gaurav Sinha
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Corresponding author

Correspondence to Samantha T. Arundel .

Editor information

Editors and Affiliations

  1. Department of Geodesy and Geoinformation, Vienna University of Technology(TU-Wien), Wien, Austria

    Paolo Fogliaroni

  2. Department of Geography, Birkbeck, University of London, London, Middlesex, United Kingdom

    Andrea Ballatore

  3. Department of Industrial and Information Engineering and Economics, University of L'Aquila , L'Aquila, Italy

    Eliseo Clementini

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Cite this paper

Arundel, S.T., Sinha, G. (2018). Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms. In: Fogliaroni, P., Ballatore, A., Clementini, E. (eds) Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017). COSIT 2017. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-63946-8_3

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