Building Virtual Earth Observatories Using Scientific ...€¦ · Scientific Database, Semantic Web...

1 INSPIRE Conference 2013: Building Virtual Earth Observatories Using Scientific Database, Semantic Web and Linked Geospatial Data

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

Dept. of Informatics and Telecommunications National and Kapodistrian University of Athens

Presenter: George Garbis ggarbis@di.uoa.gr

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

Motivation

Motivation (cont’d)

~20 PB

Catalogue

Processing Chains

EO data center

Archive

State of the Art in EO Data Centers

Raw Data

INSPIRE Conference 2013: Building Virtual Earth Observatories Using Scientific Database, Semantic Web and Linked Geospatial Data

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.

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.

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.

Raw Data Ingestion

Archiving

Cataloguing

Processing

GIS Data Derived

Products Metadata

The TELEIOS Earth Observatory: Concept View

Content Extraction

Knowledge Discovery and Data Mining

Features

Raw Data Ingestion

Archiving

Cataloguing

Processing

GIS Data Derived

Products Metadata

KNOWL-

Content Extraction

Linked Geospatial

Semantic Annotations

Ontologies

Features

Raw Data Ingestion

Archiving

Cataloguing

Processing

GIS Data Derived

Products Metadata

KNOWL-

Content Extraction

Linked Geospatial

Ontologies

Features

Raw Data Ingestion

Archiving

Cataloguing

Processing

GIS Data Derived

Products Metadata

Web Portals

Rapid Mapping

KNOWL-

Content Extraction

Linked Geospatial

Ontologies

Features

Raw Data Ingestion

Archiving

Cataloguing

Processing

GIS Data Derived

Products Metadata

Web Portals

Rapid Mapping

base a

KNOWL-

Metadata (xml annotation file)

Use Case I: A Virtual Observatory for TerraSAR-X data (DLR)

Metadata (xml annotation file)

Use Case I: A Virtual Observatory for TerraSAR-X data (DLR)

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

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

Semantics-Based Representation and Querying of EO Data

The data model stRDF and the query language stSPARQL

The system Strabon

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.

"23.7636"^^xsd:double noa:hasArea

ex:BurntArea1

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

stRDF: An example (1/2)

ex:BurntArea1

rdf:type ex:BurntArea1

noa:BurntArea

“1” ^^xsd:int noa:hasID

noa:BurntArea

“1” ^^xsd:int

noa:hasID

noa:BurntArea

"POLYGON(( 38.16 23.7, 38.18 23.7, 38.18

23.8, ... 38.16 23.8, 38.16 3.7));

<http://spatialreference.org/ref/epsg/4121/>"

^^strdf:WKT

“1” ^^xsd:int

noa:hasID

noa:BurntArea

geo:geometry

"POLYGON(( 38.16 23.7, 38.18 23.7, 38.18

23.8, ... 38.16 23.8, 38.16 3.7));

^^strdf:WKT

Spatial Data Type Well-Known Text

“1” ^^xsd:int

noa:hasID

noa:BurntArea

geo:geometry

"POLYGON(( 38.16 23.7, 38.18 23.7, 38.18

23.8, ... 38.16 23.8, 38.16 3.7));

^^strdf:WKT

Spatial Literal (OpenGIS Simple

Features)

Spatial Data Type Well-Known Text

“1” ^^xsd:int

noa:hasID

noa:BurntArea

geo:geometry

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)}

• Find all burned forests within 10kms of a city

stSPARQL: An example (1/2)

SELECT ?BA ?BAGEO

?R noa:hasCorineLandCoverUse ?F . .

SELECT ?BA ?BAGEO

?R noa:hasCorineLandCoverUse ?F . .

SELECT ?BA ?BAGEO

Spatial Functions (OGC Simple Feature Access)

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

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:

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

Strabon: A Scalable Geospatial RDF Store

stRDF graphs

stSPARQL/ GeoSPARQL

queries

WKT GML

Query Engine

Parser

Optimizer

Evaluator

Transaction

Manager

Storage Manager

Repository

Strabon

PostGIS

GeneralDB

Sesame

http://bit.ly/Strabon

Period

Linked Geospatial Data

Datasets that we published as linked data:

CORINE Land Use / Land Cover

Coastline of Greece

Greek Administrative Geography

Portal: http://www.linkedopendata.gr/

Datasets from Linked Open Data Cloud

LinkedGeoData

GeoNames

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("2007-08-24T00:00:00"^^xsd:dateTime <= ?hAcqTime &&

?hAcqTime <= "2007-08-24T23:59:59"^^xsd:dateTime).

FILTER(strdf:contains("POLYGON((21.027 38.36, 23.77

38.36, 23.77 36.05, 21.027 36.05, 21.027 38.36))"

^^strdf:WKT, ?hGeo) ) . }

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("2007-08-24T00:00:00"^^xsd:dateTime <= ?hAcqTime &&

?hAcqTime <= "2007-08-24T23:59:59"^^xsd:dateTime).

FILTER(strdf:contains("POLYGON((21.027 38.36, 23.77

38.36, 23.77 36.05, 21.027 36.05, 21.027 38.36))"

^^strdf:WKT, ?hGeo) ) . }

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))}

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))}

Thank you for your attention!

More information about TELEIOS:

http:/www.earthobservatory.eu/

The NOA Fire Monitoring Service is operational: http://papos.space.noa.gr/fend_static/

User guide at Fraunhofer booth

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