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Geographical Data Mining

Explore land use change, object detection, and data reduction in dynamic areas using GeoDMA tools. Uncover hidden patterns through segmentation, attribute extraction, and visualization.

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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. Dynamic areas New Frontiers INPE 2003/2004: Intense Pressure Deforestation Forest Future expansion Non-forest Clouds/no data

  3. Research Questions • What are the different land use agents? • When did a certain land use agent emerge? • What are the dominant land use agents for each region? • How do agents emerge and change in time?

  4. More Research Questions • What objects are in the image? • How many houses? • Where are the streets? • What is hidden by the shadow?

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

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

  7. Geo Data Mining 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

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

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

  10. GeoDMA Dataflow Adapted from [Silva, 2005]

  11. GeoDMA Dataflow Adapted from [Silva, 2005]

  12. GeoDMA Dataflow Adapted from [Silva, 2005]

  13. GeoDMA Dataflow Adapted from [Silva, 2005]

  14. GeoDMA Dataflow Adapted from [Silva, 2005]

  15. GeoDMA Dataflow Adapted from [Silva, 2005]

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

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

  18. Application – Terra do Meio 1997 - 2004 Silva et al, 2008

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

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

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