*Miner: A spatial and spatiotemporal data mining system

Ranga Raju Vatsavai, Shashi Shekhar, Thomas E. Burk, Budhendra Bhaduri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Intelligent image information mining for thematic pattern extraction is a complex task. Ever increasing spatial, spectral, and temporal resolution poses several challenges to the geographic knowledge discovery community. Although the improvements in sensor technology and data collection methods may lead to improved geoinformation generation, it also places several constraints on data mining techniques. Moreover thematic classes are spectrally overlapping, that is, many thematic classes can not be separated by spectral features alone. In recent years we have developed several innovative machine learning approaches to address these problems. The resulting software system, called *Miner, was tested on several real world multisource spatiotemporal datasets. Experimental evaluation showed improved accuracy over conventional data mining approaches. In addition, we integrated *Miner with another popular open source machine learning system called Weka. In this demo we show the utility of *Miner for thematic information extraction from multisource spatiotemporal data (remote sensing images and ancillary geospatial databases).

Original languageEnglish (US)
Title of host publicationProceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008
Pages531-532
Number of pages2
DOIs
StatePublished - Dec 1 2008
Event16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008 - Irvine, CA, United States
Duration: Nov 5 2008Nov 7 2008

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Other

Other16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM GIS 2008
CountryUnited States
CityIrvine, CA
Period11/5/0811/7/08

Keywords

  • EM
  • GMM
  • Multisource data
  • Semi-supervised learning

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