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Geographical Data Mining Thales Sehn Korting [email protected] http://www.dpi.inpe.br/~tkorting/ Motivation • Large datasets – Few data manipulation techniques – Few information extraction tools • [Silva 2005] prototype system for mining patterns applied to Brazilian Amazon deforestation Amount of data • Simple crop – 2562 x 3 = – 196608 values! 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? How to reduce input data? • Segmentation Regions Data Information Area Perimeter Rectangularity … Pixels’ Mean Pixels’ STD Texture … 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 In Practice • More than 5 different softwares! – Processing time – File-conversion time – etc. • GeoDMA – Geographical Data Mining Analyst – All tools on the same system GeoDMA • Input – Raster – Polygons • Processing – Attributes Extraction – Normalization – Supervised training • Output – Thematic classification GeoDMA Dataflow GeoDMA and TerraLib • Image processing functions – Segmentation • Region Growing – Attributes Extraction • Data Mining algorithms – C4.5 Decision Tree – Self-Organizing Maps – ... Current Applications • Land Change in Brazilian Amazon • Urban classification Future Works • Allow multi-temporal data mining – Snapshots – Try to explain changes • More classification algorithms • More precise segmentation Geographical Data Mining Try GeoDMA! http://www.dpi.inpe.br/geodma/
