Download GeoDMA – discovering patterns from spatio

GeoDMA – discovering patterns
from spatio-temporal data
Thales Sehn Korting
[email protected]
Short Bio
 Computer Engineer (FURG)
 MsC in Applied Computing (INPE)
 PhD candidate in Remote Sensing (INPE)
Advisors
 Leila Fonseca
 Gilberto Câmara
GeoDMA – Geographical Data Mining Analyst
GeoDMA – Geographical Data Mining Analyst
Pinho et al, 2008
Terra do Meio 1997 - 2004
Silva et al, 2008
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Ready
TBD
Spatio-Temporal Data Mining
• Amount of satellites allow the acquisition of
images with very short temporal resolution
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SITS – Satellite Image Time Series
Identify patterns of change in SITS
Goal: Determine where, when and
why land changes occur
Spatio-temporal analysis at INPE
• Semantics of type-based
evolution creates a
detailed history of
change
• Type conversions and
object’s history answers
questions about cases of
change
Bittencourt et al, 2007
• Rule-Based evolution
describe changes as a
set of rules
• Case-Based Reasoning
is used to extract rules of
object evolution
Mota et al, 2009
NDVI
Defining Spectral Trajectories
Proposals
Find trajectories in
 Classified images
 Segmented Objects
 Grid of pixels
 Single pixel
Event intervals
Seasonal
growing cicle
should not
itself be called
change
[Boriah et al, 2008]
Heas et al, 2005
GeoDMA – discovering patterns
from spatio-temporal data
http://www.dpi.inpe.br/geodma/
References
[Bittencourt et al, 2007] Rule-based Evolution of Typed Spatio-temporal Objects. GeoINFO, 2007.
[Boriah et al, 2008] Land Cover Change Detection: A Case Study. SIGKDD, 2008.
[Mota et al, 2009] Case-Based Reasoning for Eliciting the Evolution of Geospatial Objects. COSIT, 2009.
[Pinho et al, 2008] Urban Land Cover Classification from High-Resolution Images Using the C4.5 Algorithm. ISPRS, 2008.
[Silva et al, 2008] Remote Sensing Image Mining: detecting agents of Land use change in tropical forest areas. IJRS, 2008.
[Heas et al, 2005] Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning. TGRS, 2005.