Spatial contextual classification and prediction models for mining geospatial data

  title={Spatial contextual classification and prediction models for mining geospatial data},
  author={Shashi Shekhar and Paul R. Schrater and Ranga Raju Vatsavai and Weili Wu and Sanjay Chawla},
  journal={IEEE Trans. Multimedia},
Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for incorporating spatial context into image segmentation and land-use classification problems. The spatial autoregression (SAR) model, which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 31 references

An artificial neural network approach to spatial habitat modeling with interspecific interaction,

  • S. Oźesmi, U. Ozesmi
  • Ecological Modeling. Amsterdam, The Netherlands…
  • 1999
Highly Influential
4 Excerpts

A spatial habitat model for the marsh-breeding red-winged blackbird (agelaius phoeniceus 1.),

  • U. Ozesmi, W. Mitsch
  • Coastal Lake Erie Wetlands Ecological Modeling…
  • 1997
Highly Influential
4 Excerpts

and A

  • A. H. Solberg, T. Taxt
  • K. Jain, “A Markov random field model for…
  • 1996
Highly Influential
4 Excerpts

A Tour of Spatial Databases

  • S. Shekhar, S. Chawla
  • Englewood Cliffs, NJ: Prentice-Hall
  • 2002
1 Excerpt

Spatial dependence in data mining,” in Data Mining for Scientific and Engineering Applications

  • J. P. LeSage, R. K. Pace
  • 2001
2 Excerpts

Jan.) Turning a map into a cake layer of information. New York Times[Online

  • C. Greenman
  • 2000
1 Excerpt

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