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SEMANTIC SEARCH WITHIN EARTH OBSERVATION PRODUCTS DATABASES 
BASED ON AUTOMATIC TAGGING OF IMAGE CONTENT 
Jérôme Gasperi 
2014 Conference on Big Data from Space 
Frascati - Italy - November 12th, 2014
Big Data ? 
The data deluge 
The search paradigm 
iTag 
An EO tagging library 
resto 
An EO product search engine 
What’s next ? 
Conclusion and perspectives
The data deluge 
Brett Ryder - http://www.economist.com/node/15579717
Earth Observation products search paradigm is to use 
the acquisition parameters stored in the metadata
When Where How
What ? 
i.e. image content
Sven Sachsalber | http://www.palaisdetokyo.com/fr/events/sven-sachsalber
iTag 
Automatic tagging of Earth Observation products
Orthorectified image Characterized image 
This is urban 
This is water 
This is forest 
What we got What we need
iTag provides semantic enhancement of Earth 
Observation data
It uses metadata footprint to enrich metadata 
from exogenous data 
i.e. no image processing !
Out of the box tagging sources 
Continents, 
Countries, 
Regions, 
States, 
Cities, 
Land cover, 
Rivers, 
Population count
# Polygon around Moscow 
$moscow = ‘POLYGON((37.1351 55.9655,38.1006 55.9640,38.0525 
55.4969,37.0926 55.5171,37.1351 55.9655))’; 
# Initialize iTag 
$iTag = new iTag(); 
# Tag polygon for land cover 
$result = $iTag->tag($moscow, array( 
‘landcover’ => true 
));
Tag footprint around Moscow 
http://goo.gl/6AkU4y
github.com/jjrom/itag
resto 
Toward an Earth Observation products search engine
Search, visualize and download 
Earth Observation data
Architecture
Gazetteer Query Analyzer 
Administration 
REST Webservices 
Abstract Database Access 
Layer 
PostgreSQL 
Driver 
iTag 2.0 
resto 2.0 
Search 
Visualize 
Download 
Users 
POST 
DELETE 
Admin 
Data
Abstract Database Access Layer 
PostgreSQL Driver 
database 
resto 
schema 
_collection1 
schema 
_collection2 
…etc… 
schema 
resto 
schema 
usersmanagement 
PostGIS 
hstore 
Table inheritance
Rresto 
Search Ingest 
GET POST
Ingest
Gazetteer Query Analyzer 
Administration 
REST Webservices 
Abstract Database Access 
Layer 
PostgreSQL 
Driver 
iTag 2.0 
resto 2.0 
Search 
Visualize 
Download 
Users 
POST 
DELETE 
Data
During ingestion process, resources are automatically 
tagged thanks to iTag library
Why to tag image first ?
Search images over Russia 
Bounding box !!
Search
resto provides semantic search capabilities 
It uses a Query Analyzer to translate natural language query into 
a set of EO OpenSearch parameters
<with> "keyword" 
<without> "keyword" 
"quantity" <lesser> (than) "numeric" "unit" 
"quantity" <greater> (than) "numeric" "unit" 
"quantity" <equal> (to) "numeric" "unit" 
<lesser> (than) "numeric" "unit" (of) "quantity" 
<greater> (than) "numeric" "unit" (of) "quantity" 
<equal> (to) "numeric" "unit" (of) "quantity" 
"quantity" <between> "numeric" <and> "numeric" ("unit") 
<between> "numeric" <and> "numeric" "unit" (of) "quantity" 
<today> 
<yesterday> 
<before> "date" 
<after> "date" 
<between> "date" <and> "date" 
"numeric" "(year|day|month)" <ago> 
<last> "(year|day|month)" 
<last> "numeric" "(year|day|month)" 
"numeric" <last> "(year|day|month)" 
"(year|day|month)" <last> 
<since> "numeric" "(year|day|month)" 
<since> "month" "year" 
<since> "date" 
<since> "numeric" <last> "(year|day|month)" 
<since> <last> "numeric" "(year|day|month)" 
<since> <last> "(year|day|month)" 
<since> "(year|day|month)" <last> 
Query string analysis algorithm 
is based on simple recognition 
of words and patterns
Example 
« Images of urban area in Russia acquired in last year with less than 5 % of cloud cover »
Example 
« Images of urban area in Russia acquired in last year with less than 5 % of cloud cover » 
keyword location date acquisition parameter
2. Each search result has an « human readable url » that can 
be indexed by web crawler (i.e. google robots) 
1. Search parameters are derived from 
Natural Language query 
3. Keywords on resources are links to search requests : 
they can be indexed by web crawler…and so on
2. Each search result has an « human readable url » that can 
be indexed by web crawler (i.e. google robots) 
http://goo.gl/BCZ3z4 
1. Search parameters are derived from 
Natural Language query 
3. Keywords on resources are links to search requests : 
they can be indexed by web crawler…and so on
As of version 2.0, resto supports faceted search
http://dinosaurs.wikia.com/wiki/Coelurosauria 
Facets
Performances 
iTag / resto
1 000 000 
SPOT DATABASE 
New products retrieved every 3 hours from ADS catalog 
0.2s 
SEARCH 
0.5s 
Time period of 1 month within a 10x10 km2 box 
INGEST 
Per product for a ~5000 products ingestion 
Order of magnitude compute on a Dual Core 2.6 GHz | 4 Go RAM | HDD 500 To
What’s next ? 
Conclusion and perspectives
Need for « fresh » tagging reference databases 
(e.g. GLC2000 replacement)
Enhance metadata with twitter trends hashtags 
Add tags #mh370,#plane,#malaysianairline 
to resources acquired between 2014, march 8th and 2014, april 14th 
in the south of the Indian Ocean
« Linked data is the right way to do Semantic Web » 
Tim Berners-Lee
Update iTag JSON model to follow JSON-LD format 
{ 
"@context": "http://json-ld.org/contexts/person.jsonld", 
"@id": "http://dbpedia.org/resource/John_Lennon", 
"name": "John Lennon", 
"born": "1940-10-09", 
"spouse": "http://dbpedia.org/resource/Cynthia_Lennon" 
}
Semantic search within Earth Observation products databases based on automatic tagging of image content

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Semantic search within Earth Observation products databases based on automatic tagging of image content

  • 1. SEMANTIC SEARCH WITHIN EARTH OBSERVATION PRODUCTS DATABASES BASED ON AUTOMATIC TAGGING OF IMAGE CONTENT Jérôme Gasperi 2014 Conference on Big Data from Space Frascati - Italy - November 12th, 2014
  • 2. Big Data ? The data deluge The search paradigm iTag An EO tagging library resto An EO product search engine What’s next ? Conclusion and perspectives
  • 3. The data deluge Brett Ryder - http://www.economist.com/node/15579717
  • 4. Earth Observation products search paradigm is to use the acquisition parameters stored in the metadata
  • 6. What ? i.e. image content
  • 7. Sven Sachsalber | http://www.palaisdetokyo.com/fr/events/sven-sachsalber
  • 8. iTag Automatic tagging of Earth Observation products
  • 9. Orthorectified image Characterized image This is urban This is water This is forest What we got What we need
  • 10. iTag provides semantic enhancement of Earth Observation data
  • 11. It uses metadata footprint to enrich metadata from exogenous data i.e. no image processing !
  • 12. Out of the box tagging sources Continents, Countries, Regions, States, Cities, Land cover, Rivers, Population count
  • 13. # Polygon around Moscow $moscow = ‘POLYGON((37.1351 55.9655,38.1006 55.9640,38.0525 55.4969,37.0926 55.5171,37.1351 55.9655))’; # Initialize iTag $iTag = new iTag(); # Tag polygon for land cover $result = $iTag->tag($moscow, array( ‘landcover’ => true ));
  • 14. Tag footprint around Moscow http://goo.gl/6AkU4y
  • 16. resto Toward an Earth Observation products search engine
  • 17. Search, visualize and download Earth Observation data
  • 19. Gazetteer Query Analyzer Administration REST Webservices Abstract Database Access Layer PostgreSQL Driver iTag 2.0 resto 2.0 Search Visualize Download Users POST DELETE Admin Data
  • 20. Abstract Database Access Layer PostgreSQL Driver database resto schema _collection1 schema _collection2 …etc… schema resto schema usersmanagement PostGIS hstore Table inheritance
  • 23. Gazetteer Query Analyzer Administration REST Webservices Abstract Database Access Layer PostgreSQL Driver iTag 2.0 resto 2.0 Search Visualize Download Users POST DELETE Data
  • 24. During ingestion process, resources are automatically tagged thanks to iTag library
  • 25. Why to tag image first ?
  • 26. Search images over Russia Bounding box !!
  • 28. resto provides semantic search capabilities It uses a Query Analyzer to translate natural language query into a set of EO OpenSearch parameters
  • 29. <with> "keyword" <without> "keyword" "quantity" <lesser> (than) "numeric" "unit" "quantity" <greater> (than) "numeric" "unit" "quantity" <equal> (to) "numeric" "unit" <lesser> (than) "numeric" "unit" (of) "quantity" <greater> (than) "numeric" "unit" (of) "quantity" <equal> (to) "numeric" "unit" (of) "quantity" "quantity" <between> "numeric" <and> "numeric" ("unit") <between> "numeric" <and> "numeric" "unit" (of) "quantity" <today> <yesterday> <before> "date" <after> "date" <between> "date" <and> "date" "numeric" "(year|day|month)" <ago> <last> "(year|day|month)" <last> "numeric" "(year|day|month)" "numeric" <last> "(year|day|month)" "(year|day|month)" <last> <since> "numeric" "(year|day|month)" <since> "month" "year" <since> "date" <since> "numeric" <last> "(year|day|month)" <since> <last> "numeric" "(year|day|month)" <since> <last> "(year|day|month)" <since> "(year|day|month)" <last> Query string analysis algorithm is based on simple recognition of words and patterns
  • 30. Example « Images of urban area in Russia acquired in last year with less than 5 % of cloud cover »
  • 31. Example « Images of urban area in Russia acquired in last year with less than 5 % of cloud cover » keyword location date acquisition parameter
  • 32. 2. Each search result has an « human readable url » that can be indexed by web crawler (i.e. google robots) 1. Search parameters are derived from Natural Language query 3. Keywords on resources are links to search requests : they can be indexed by web crawler…and so on
  • 33. 2. Each search result has an « human readable url » that can be indexed by web crawler (i.e. google robots) http://goo.gl/BCZ3z4 1. Search parameters are derived from Natural Language query 3. Keywords on resources are links to search requests : they can be indexed by web crawler…and so on
  • 34. As of version 2.0, resto supports faceted search
  • 37. 1 000 000 SPOT DATABASE New products retrieved every 3 hours from ADS catalog 0.2s SEARCH 0.5s Time period of 1 month within a 10x10 km2 box INGEST Per product for a ~5000 products ingestion Order of magnitude compute on a Dual Core 2.6 GHz | 4 Go RAM | HDD 500 To
  • 38. What’s next ? Conclusion and perspectives
  • 39. Need for « fresh » tagging reference databases (e.g. GLC2000 replacement)
  • 40. Enhance metadata with twitter trends hashtags Add tags #mh370,#plane,#malaysianairline to resources acquired between 2014, march 8th and 2014, april 14th in the south of the Indian Ocean
  • 41. « Linked data is the right way to do Semantic Web » Tim Berners-Lee
  • 42.
  • 43. Update iTag JSON model to follow JSON-LD format { "@context": "http://json-ld.org/contexts/person.jsonld", "@id": "http://dbpedia.org/resource/John_Lennon", "name": "John Lennon", "born": "1940-10-09", "spouse": "http://dbpedia.org/resource/Cynthia_Lennon" }