Building Virtual Earth Observatories Using Scientific Database, Semantic Web and Linked Geospatial Data Technologies

Transcription

1 Building Virtual Earth Observatories Using Scientific Database, Semantic Web and Linked Geospatial Data Technologies Presenter: George Garbis Dept. of Informatics and Telecommunications National and Kapodistrian University of Athens 1

2 Outline Motivation for project TELEIOS State of the art in Earth Observation data centers Developing Virtual Earth Observatories using ontologies and linked geospatial data Some technical highlights 2

3 Motivation 3

4 Motivation (cont d) ~20 PB 4

5 State of the Art in EO Data Centers Users EOWEB EO data center Catalogue Raw Data Processing Chains Archive 5

6 Example Can I pose the following query using EOWEB? Find images taken by the SEVIRI satellite on August 25, 2007 which contain fire hotspots in areas which have been classified as forests according to CORINE Land Cover, and are located within 2km from an archaeological site in the Peloponnese. 6

7 Example (cont d) 7

8 Example (cont d) Well, only partially. Find images taken by the SEVIRI satellite on August 25, 2007 which contain fire hotspots in areas which have been classified as forests according to CORINE Land Cover, and are located within 2km from an archaeological site in the Peloponnese. 8

9 Example (cont d) But why? All this information is available in the satellite images and other auxiliary data sources of EO data centers or on the Web. However, EO data centers today do not allow: the mining of satellite image content and its integration with other relevant data sources so the previous query can be answered. 9

10 The TELEIOS Earth Observatory: Concept View GIS Data Derived Products Metadata Raw Data Ingestion Processing Cataloguing Archiving

11 The TELEIOS Earth Observatory: Concept View Knowledge Discovery and Data Mining KNOWL- EDGE GIS Data Derived Products Metadata Features Raw Data Ingestion Processing Cataloguing Archiving Content Extraction DATA

12 The TELEIOS Earth Observatory: Concept View Linked Geospatial Data Semantic Annotations Ontologies Knowledge Discovery and Data Mining KNOWL- EDGE GIS Data Derived Products Metadata Features Raw Data Ingestion Processing Cataloguing Archiving Content Extraction DATA

13 The TELEIOS Earth Observatory: Concept View Web Portals Rapid Mapping Linked Geospatial Data Semantic Annotations Ontologies Knowledge Discovery and Data Mining KNOWL- EDGE GIS Data Derived Products Metadata Features Raw Data Ingestion Processing Cataloguing Archiving Content Extraction DATA

14 Scientific Database and Semantic Web Technologies The TELEIOS Earth Observatory: Concept View Web Portals Rapid Mapping Linked Geospatial Data Semantic Annotations Ontologies Knowledge Discovery and Data Mining KNOWL- EDGE GIS Data Derived Products Metadata Features Raw Data Ingestion Processing Cataloguing Archiving Content Extraction DATA

15 Use Case I: A Virtual Observatory for TerraSAR-X data (DLR) Metadata (xml annotation file) 15

16 Use Case I: A Virtual Observatory for TerraSAR-X data (DLR) Metadata (xml annotation file) 16

17 Use Case II: Real-time Fire Monitoring (NOA) 17

18 High Level Data Modeling Need for representing Standard product metadata Standard product semantic annotations Geospatial information Temporal information Need to link to other data sources GIS data Other information on the Web 18

19 Semantics-Based Representation and Querying of EO Data The data model strdf and the query language stsparql The system Strabon 19

20 RDF: Resource Description Framework W3C recommendation RDF is a graph data model ( + XML syntax + semantics) For representing metadata For describing the semantics of information in a machine-readable way Resources are described in terms of properties and property values using RDF statements Statements are represented as triples, consisting of a subject, predicate and object. ex:burntarea1 noa:hasarea " "^^xsd:double 20

21 The Data Model strdf strdf stands for spatial/temporal RDF. It is an extension of the W3C standard RDF for the representation of geospatial data that may change over time. strdf extends RDF with: Spatial literals encoded in OGC standards Well-Known Text or GML New datatypes for spatial literals (strdf:wkt, strdf:gml and strdf:geometry) Temporal literals can be either periods or instants New datatype for temporal literals (strdf:period) Placed as the fourth component of a triple to denote valid time 21

22 strdf: An example (1/2) 22

23 strdf: An example (1/2) ex:burntarea1 23

24 strdf: An example (1/2) noa:burntarea rdf:type ex:burntarea1 24

25 strdf: An example (1/2) noa:burntarea noa:hasid 1 ^^xsd:int rdf:type ex:burntarea1 25

26 strdf: An example (1/2) noa:burntarea noa:hasid 1 ^^xsd:int rdf:type ex:burntarea1 noa:hasarea " "^^xsd:double 26

27 strdf: An example (1/2) noa:burntarea noa:hasid 1 ^^xsd:int rdf:type ex:burntarea1 noa:hasarea " "^^xsd:double geo:geometry "POLYGON(( , , , , )); < ^^strdf:wkt 27

28 strdf: An example (1/2) noa:burntarea noa:hasid 1 ^^xsd:int rdf:type ex:burntarea1 noa:hasarea " "^^xsd:double geo:geometry "POLYGON(( , , , , )); < ^^strdf:wkt Spatial Data Type Well-Known Text 28

29 strdf: An example (1/2) noa:burntarea noa:hasid 1 ^^xsd:int rdf:type Spatial Literal (OpenGIS Simple Features) ex:burntarea1 noa:hasarea geo:geometry " "^^xsd:double "POLYGON(( , , , , )); < ^^strdf:wkt Spatial Data Type Well-Known Text 29

30 stsparql: An example (1/2) Find all burned forests within 10kms of a city SELECT?BA?BAGEO WHERE {?R rdf:type noa:region.?r geo:hasgeometry?rgeo.?r noa:hascorinelandcoveruse?f..?f rdfs:subclassof clc:forests.?city rdf:type dbpedia:city.?city geo:hasgeometry?cgeo.?ba rdf:type noa:burntarea.?ba geo:hasgeometry?bageo. FILTER( strdf:intersect(?rgeo,?bageo) && strdf:distance(?rgeo,?cgeo,uom:km)<10)} 30

31 stsparql: An example (1/2) Find all burned forests within 10kms of a city SELECT?BA?BAGEO WHERE {?R rdf:type noa:region.?r geo:hasgeometry?rgeo.?r noa:hascorinelandcoveruse?f..?f rdfs:subclassof clc:forests.?city rdf:type dbpedia:city.?city geo:hasgeometry?cgeo.?ba rdf:type noa:burntarea.?ba geo:hasgeometry?bageo. FILTER( strdf:intersect(?rgeo,?bageo) && strdf:distance(?rgeo,?cgeo,uom:km)<10)} 31

32 stsparql: An example (1/2) Find all burned forests within 10kms of a city SELECT?BA?BAGEO WHERE {?R rdf:type noa:region.?r geo:hasgeometry?rgeo.?r noa:hascorinelandcoveruse?f..?f rdfs:subclassof clc:forests.?city rdf:type dbpedia:city.?city geo:hasgeometry?cgeo.?ba rdf:type noa:burntarea.?ba geo:hasgeometry?bageo. FILTER( strdf:intersect(?rgeo,?bageo) && strdf:distance(?rgeo,?cgeo,uom:km)<10)} 32

33 stsparql: An example (1/2) Find all burned forests within 10kms of a city SELECT?BA?BAGEO WHERE {?R rdf:type noa:region.?r geo:hasgeometry?rgeo.?r noa:hascorinelandcoveruse?f..?f rdfs:subclassof clc:forests.?city rdf:type dbpedia:city.?city geo:hasgeometry?cgeo.?ba rdf:type noa:burntarea.?ba geo:hasgeometry?bageo. FILTER( strdf:intersect(?rgeo,?bageo) && strdf:distance(?rgeo,?cgeo,uom:km)<10)} 33

34 stsparql: An example (1/2) Find all burned forests within 10kms of a city SELECT?BA?BAGEO WHERE {?R rdf:type noa:region.?r geo:hasgeometry?rgeo.?r noa:hascorinelandcoveruse?f..?f rdfs:subclassof clc:forests.?city rdf:type dbpedia:city.?city geo:hasgeometry?cgeo.?ba rdf:type noa:burntarea.?ba geo:hasgeometry?bageo. FILTER( strdf:intersect(?rgeo,?bageo) && strdf:distance(?rgeo,?cgeo,uom:km)<10)} 34

35 stsparql: An example (1/2) Find all burned forests within 10kms of a city SELECT?BA?BAGEO WHERE {?R rdf:type noa:region.?r geo:hasgeometry?rgeo.?r noa:hascorinelandcoveruse?f..?f rdfs:subclassof clc:forests.?city rdf:type dbpedia:city.?city geo:hasgeometry?cgeo.?ba rdf:type noa:burntarea.?ba geo:hasgeometry?bageo. Spatial Functions (OGC Simple Feature Access) FILTER( strdf:intersect(?rgeo,?bageo) && strdf:distance(?rgeo,?cgeo,uom:km)<10)} 35

36 stsparql: More details We start from SPARQL 1.1 (W3C Recommendtaion). We add a SPARQL extension function for each function defined in the OGC standard OpenGIS Simple Feature Access Part 2: SQL option (ISO 19125) for adding geospatial data to relational DBMSs and SQL. We add a set of temporal functions (superset of Allen s functions) as SPARQL extension functions We add appropriate geospatial and temporal extensions to SPARQL 1.1 Update language 36

37 stsparql vs. GeoSPARQL GeoSPARQL is a recent OGC standard to develop an extension of SPARQL for querying geospatial data expressed in RDF. stsparql and GeoSPARQL have been developed independently. stsparql geospatial query functionality is very close to a subset of the recent OGC standard GeoSPARQL: Core Geometry extension Geometry topology extension GeoSPARQL goes beyond stsparql: binary topological relations as RDF properties (spatial reasoners) Additional stsparql features: Geospatial aggregation functions Temporal evolution of geometries 37

38 Strabon: A Scalable Geospatial RDF Store WKT GML Sesame Strabon Period strdf graphs Query Engine Parser Optimizer Evaluator Transaction Manager Storage Manager Repository SAIL RDBMS stsparql/ GeoSPARQL queries GeneralDB PostGIS 38

39 Linked Geospatial Data Datasets that we published as linked data: CORINE Land Use / Land Cover Coastline of Greece Greek Administrative Geography Portal: Datasets from Linked Open Data Cloud LinkedGeoData GeoNames 39

40 Discover EO data Get all hotspots detected in the Peloponnese on 24/08/2007. SELECT?h?hGeo?hAcqTime?hConfidence?hConfirmation?hProvider?hSensor?hSatellite WHERE {?h rdf:type noa:hotspot ; noa:hasgeometry?hgeo ; noa:hasacquisitiontime?hacqtime ; noa:hasconfidence?hconfidence ; noa:isproducedby?hprovider ; noa:hasconfirmation?hconfirmation ; noa:isderivedfromsensor?hsensor ; noa:isderivedfromsatellite?hsatellite. FILTER(" T00:00:00"^^xsd:dateTime <=?hacqtime &&?hacqtime <= " T23:59:59"^^xsd:dateTime). FILTER(strdf:contains("POLYGON(( , , , , ))" ^^strdf:wkt,?hgeo) ). } 40

41 Discover EO data Get all hotspots detected in the Peloponnese on 24/08/2007. SELECT?h?hGeo?hAcqTime?hConfidence?hConfirmation?hProvider?hSensor?hSatellite WHERE {?h rdf:type noa:hotspot ; noa:hasgeometry?hgeo ; noa:hasacquisitiontime?hacqtime ; noa:hasconfidence?hconfidence ; noa:isproducedby?hprovider ; noa:hasconfirmation?hconfirmation ; noa:isderivedfromsensor?hsensor ; noa:isderivedfromsatellite?hsatellite. FILTER(" T00:00:00"^^xsd:dateTime <=?hacqtime &&?hacqtime <= " T23:59:59"^^xsd:dateTime). FILTER(strdf:contains("POLYGON(( , , , , ))" ^^strdf:wkt,?hgeo) ). } 41

42 Improve product accuracy Correlate fire products with auxiliary data to increase their thematic accuracy e.g., delete the parts of the polygons that fall into the sea. DELETE {?h noa:hasgeometry?hgeo} INSERT {?h noa:hasgeometry?dif} WHERE { SELECT DISTINCT?h?hGeo (strdf:intersection(?hgeo, strdf:union(?cgeo)) AS?dif) WHERE {?h rdf:type noa:hotspot.?h strdf:hasgeometry?hgeo.?c rdf:type coast:coastline.?c strdf:hasgeometry?cgeo. FILTER( strdf:anyinteract(?hgeo,?cgeo)} GROUP BY?h?hGeo HAVING strdf:overlap(?hgeo, strdf:union(?cgeo))} 42

43 Improve product accuracy Correlate fire products with auxiliary data to increase their thematic accuracy e.g., delete the parts of the polygons that fall into the sea. DELETE {?h noa:hasgeometry?hgeo} INSERT {?h noa:hasgeometry?dif} WHERE { SELECT DISTINCT?h?hGeo (strdf:intersection(?hgeo, strdf:union(?cgeo)) AS?dif) WHERE {?h rdf:type noa:hotspot.?h strdf:hasgeometry?hgeo.?c rdf:type coast:coastline.?c strdf:hasgeometry?cgeo. FILTER( strdf:anyinteract(?hgeo,?cgeo)} GROUP BY?h?hGeo HAVING strdf:overlap(?hgeo, strdf:union(?cgeo))} 43

44 Thank you for your attention! More information about TELEIOS: The NOA Fire Monitoring Service is operational: User guide at Fraunhofer booth 44