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 –256 2 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. Current Applications Land Change in Brazilian Amazon Urban classification

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/