An experimental approach for selection/elimination in stream network generalization using support vector machines


Creative Commons License

Sen A. , Gokgoz T.

GEOCARTO INTERNATIONAL, cilt.30, ss.311-329, 2015 (SCI İndekslerine Giren Dergi)

  • Cilt numarası: 30
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1080/10106049.2014.937466
  • Dergi Adı: GEOCARTO INTERNATIONAL
  • Sayfa Sayısı: ss.311-329

Özet

Multi-representation databases (MRDB) are used in several Geographical Information System applications for different purposes. MRDB are mainly obtained through model and cartographic generalizations. The model generalization is essentially achieved with the selection/elimination process in which a decision must be made to include or exclude the object at the target level. In this study, support vector machines (SVM) was, for the first time, used for the selection/elimination process in stream network generalization. Within this context, the attributes to be used as input data in the SVM method were determined and weighted according to the associations determined in a chi-squared independence test. 1:100,000-scale (medium resolution) stream networks were derived from two 1:24,000-scale (high resolution) stream networks with different patterns in the United States Geological Survey National Hydrography Data-sets. The derived stream networks were quite similar to the 1:100,000-scale original stream networks in both qualitative and visual aspects.

Multi-representation databases (MRDB) are used in several Geographical Information System applications for different purposes. MRDB are mainly obtained through model and cartographic generalizations. The model generalization is essentially achieved with the selection/elimination process in which a decision must be made to include or exclude the object at the target level. In this study, support vector machines (SVM) was, for the first  time, used for the selection/elimination process in stream network generalization. Within this context, the attributes to be used as input data in the  SVM method were determined and weighted according to the associations determined in a chi-squared independence test. 1:100,000-scale (medium  resolution) stream networks were derived from two 1:24,000-scale (high resolution) stream networks with different patterns in the United States  Geological Survey National Hydrography Data-sets. The derived stream networks were quite similar to the 1:100,000-scale original stream networks  in both qualitative and visual aspects.