semantic search applied to earth observation products
DESCRIPTION
Seoul, Korea - 2012.10.10 82th OGC Technical Comittee How to search for Earth Observation imagery that contains coastal cultivated areas ? Semantic content extraction from image is a complex and time consuming task. A simpler approach is to use the metadata footprint against exogenous data to perform image characterization. SLACkER (SimpLe Automated Characterization of EaRth observation products) uses Global Land Cover 2000 classification to perform automatically such characterizationTRANSCRIPT
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Semantic search applied to Earth Observation products
Jerome Gasperi @ CNES | 82th OGC Technical Commitee | Seoul, Korea - October 10th, 2012
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?How to search for Earth Observation imagerythat contains coastal cultivated areas ?
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Orthorectified image Characterized image
This is urban
This is water
This is forest
What we got What we need
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How to do this ?
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Semantic content extraction from image is a complex and time consuming task
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A different and simpler approach is to use the metadata footprint against exogenous
data to perform image characterization
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SLACkERSimpLe Automated Characterization of EaRth observation products
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SLACkER uses Global Land Cover 2000 classification to perform automatic
characterization of Earth Observation products
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World land coverTIFF file ~ 650 Mo1 km resolution1 color = 1 thematic class22 classes
Global Land Cover 2000
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Metadata
IdentifierDescriptionCopyrightAcquisition dateAcquisition anglesThumbnail...+ Footprint
SLACkER
1. Get footprint bounding box 2. Get corresponding GLC2000 area
3. Process characterization 4. Store results within database
Inge
stio
nSearch
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Tag table images within database glc2000 >> Add classification columns >> Processing 10 Crop GLC2000 raster : 24137 6169 156 119 Polygonize extracted raster Process footprint against Global Land Cover Store classification => 40.89% of Deserts (10 + 14 + 19) => 31.89% of Herbaceous (9 + 11 + 12 + 13) => 14.71% of Water (20) => 9.27% of Cultivated (15 + 16 + 17 + 18) => 3.2% of Forests (1 + 2 + 3 + 4 + 5 + 6)
( 28.65 % of 14 : Sparse Herbaceous or sparse Shrub Cover ) ( 6.1 % of 18 : Mosaic: Cropland / Shrub or Grass Cover ) ( 12.24 % of 19 : Bare Areas ) ( 31.88 % of 12 : Shrub Cover, closed-open, deciduous ) ( 2.5 % of 3 : Tree Cover, broadleaved, deciduous, open ) ( 14.71 % of 20 : Water Bodies (natural & artificial) ) ( 0.13 % of 15 : Regularly flooded Shrub and/or Herbaceous Cover ) ( 2.81 % of 16 : Cultivated and managed areas ) ( 0.69 % of 4 : Tree Cover, needle-leaved, evergreen ) ( 0.21 % of 17 : Mosaic: Cropland / Tree Cover / Other natural vegetation )
Processing result example
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What's next
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md
SLACkER classification
Provide classification service through WPS
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Segmentation+classification processing with OTB*
Additionnaly, provide a "true" WPS classification service based on the OTB suite service
*OTB is the Orfeo ToolBox - http://www.orfeo-toolbox.org/otb/
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