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Geographical Data Mining

GeoDMA is a comprehensive system for geospatial data mining and analysis in the Brazilian Amazon. It offers efficient data manipulation, information extraction, and pattern mining techniques to analyze large datasets. With GeoDMA, users can reduce input data, segment images, extract attributes, normalize data, classify regions, and visualize results seamlessly.

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Geographical Data Mining

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  1. Geographical Data Mining Thales Sehn Korting tkorting@dpi.inpe.br http://www.dpi.inpe.br/~tkorting/

  2. Motivation • Large datasets • Few data manipulation techniques • Few information extraction tools • [Silva 2005] prototype system for mining patterns applied to Brazilian Amazon deforestation

  3. Amount of data • Simple crop • 2562 x 3 = • 196608 values!

  4. Amount of data • 196608 input values to answer questions like: • What kind of image? • What objects are in the image? • How many houses? • Where are the streets?

  5. How to reduce input data? • Segmentation  Regions Data Information Area Perimeter Rectangularity … Pixels’ Mean Pixels’ STD Texture …

  6. In Practice • Segment image = software A • Visualize segmentation = software B • Extract attributes = software C • Normalize attributes = software D • Visualize attributes’ space = software D • Select Samples = software E • Classify regions = software F • Visualize results = software B

  7. In Practice • More than 5 different softwares! • Processing time • File-conversion time • etc. • GeoDMA – Geographical Data Mining Analyst • All tools on the same system

  8. GeoDMA • Input • Raster • Polygons • Processing • Attributes Extraction • Normalization • Supervised training • Output • Thematic classification

  9. GeoDMA Dataflow

  10. GeoDMA and TerraLib • Image processing functions • Segmentation • Region Growing • Attributes Extraction • Data Mining algorithms • C4.5 Decision Tree • Self-Organizing Maps • ...

  11. Land Change in Brazilian Amazon Urban classification Current Applications

  12. Future Works • Allow multi-temporal data mining • Snapshots • Try to explain changes • More classification algorithms • More precise segmentation

  13. Geographical Data Mining Try GeoDMA! http://www.dpi.inpe.br/geodma/

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