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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/