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Semantic search applied to Earth
Observation products
Jerome Gasperi @ CNES | 82th OGC Technical Commitee | Seoul, Korea - October 10th, 2012
?

How to search for Earth Observation imagery
that contains coastal cultivated areas ?
This is water
This is urban

This is forest

Orthorectified image

What we got

Characterized image

What we need
How to do this ?
Semantic content extraction from image
is a complex and time consuming task
A different and simpler approach is to use the
metadata footprint against exogenous
data to perform image characterization
SLACkER

SimpLe Automated Characterization of EaRth observation products
SLACkER uses Global Land Cover 2000
classification to perform automatic
characterization of Earth Observation
products
Global Land Cover 2000
World land cover
TIFF file ~ 650 Mo
1 km resolution
1 color = 1 thematic class
22 classes
SLACkER
1. Get footprint bounding box

2. Get corresponding GLC2000 area

Identifier
Description
Copyright
Acquisition date
Acquisition angles
Thumbnail
...
+ Footprint

Ingestion

Metadata

3. Process characterization

4. Store results within database

Search
Processing result example
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 )
https://vimeo.com/51045597
What's next
Provide classification service through WPS
md

SLACkER classification
Additionnaly, provide a "true" WPS classification
service based on the OTB suite service

Segmentation+classification processing with OTB*

*OTB is the Orfeo ToolBox - http://www.orfeo-toolbox.org/otb/
Semantic search applied to Earth Observation products

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Semantic search applied to Earth Observation products

  • 1. Semantic search applied to Earth Observation products Jerome Gasperi @ CNES | 82th OGC Technical Commitee | Seoul, Korea - October 10th, 2012
  • 2.
  • 3. ? How to search for Earth Observation imagery that contains coastal cultivated areas ?
  • 4. This is water This is urban This is forest Orthorectified image What we got Characterized image What we need
  • 5. How to do this ?
  • 6. Semantic content extraction from image is a complex and time consuming task
  • 7. A different and simpler approach is to use the metadata footprint against exogenous data to perform image characterization
  • 8. SLACkER SimpLe Automated Characterization of EaRth observation products
  • 9. SLACkER uses Global Land Cover 2000 classification to perform automatic characterization of Earth Observation products
  • 10. Global Land Cover 2000 World land cover TIFF file ~ 650 Mo 1 km resolution 1 color = 1 thematic class 22 classes
  • 11. SLACkER 1. Get footprint bounding box 2. Get corresponding GLC2000 area Identifier Description Copyright Acquisition date Acquisition angles Thumbnail ... + Footprint Ingestion Metadata 3. Process characterization 4. Store results within database Search
  • 12. Processing result example 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 )
  • 15. Provide classification service through WPS md SLACkER classification
  • 16. Additionnaly, provide a "true" WPS classification service based on the OTB suite service Segmentation+classification processing with OTB* *OTB is the Orfeo ToolBox - http://www.orfeo-toolbox.org/otb/