Strabon Semantic support for EO Data Access in TELEIOSinspire.ec.europa.eu/events/conferences/inspire_2013/pdfs... · 2013. 7. 1. · Exploiting the Integration Potential of Semantic

Exploiting the Integration Potential of Semantic Web and Linked Data Technologies for Geospatial Applications, Workshop in INSPIRE Conference 2013

June 23 Florence, Italy

Strabon

Semantic support for EO Data Access in TELEIOS

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

Presenter: George Garbis

2 Exploiting the Integration Potential of Semantic Web and Linked Data Technologies for Geospatial Applications, Workshop in INSPIRE Conference 2013

Outline

Motivation

Representing and Querying Geospatial and Temporal Information in RDF

Applications

The Fire Monitoring Service of the National Observatory of Athens

The TerraSAR-X Virtual Observatory of the German Aerospace Center

Thoughts for discussion

Catalogue

Processing Chains

EO data center

Archive

State of the Art in EO Data Centers

Raw 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:

mining of satellite image content and

its integration with other relevant data sources so the previous query can be answered.

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

Outline

Motivation

Applications

Conclusions

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 Data Type Well-Known Text

“1” ^^xsd:int

noa:hasID

noa:BurntArea

geo:geometry

Spatial Literal (OpenGIS Simple

Features)

SELECT ?BA ?BAGEO

WHERE { ?R rdf:type noa:Region .

?R geo:hasGeometry ?RGEO .

?R noa:hasCorineLandCoverUse . .

?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

Spatial Functions (OGC Simple Feature Access)

clc:region1 clc:hasLandCover clc:Forest clc:region1 clc:hasLandCover clc:Forest .

clc:region1 clc:hasLandCover clc:Forest

"[2006-08- 25T11:00:00+02,2007-08-25T11:00:00+02)"^^strdf:period . clc:region1 clc:hasLandCover clc:Forest .

"[2006-08- 25T11:00:00+02,2007-08-25T11:00:00+02)"^^strdf:period .

noa:ba1 rdf:type noa:BurntArea

"[2007-08-25T11:00:00+02,2009-08-25T11:00:00+02)"^^strdf:period .

clc:region1 clc:hasLandCover clc:Forest .

"[2006-08- 25T11:00:00+02,2007-08-25T11:00:00+02)"^^strdf:period .

noa:ba1 rdf:type noa:BurntArea

"[2007-08-25T11:00:00+02,2009-08-25T11:00:00+02)"^^strdf:period .

clc:region1 clc:hasLandCover clc:AgriculturalArea

"[2009-08-25T11:00:00+02, "UC")"^^strdf:period .

clc:region1 clc:hasLandCover clc:Forest .

SELECT ?BA ?BAGEO

?R geo:geometry ?RGEO ;

?R noa:hasCorineLandCoverUse ?F ?t1. .

?CITY geo:geometry ?CGEO .

?BA rdf:type noa:BurntArea ?t2.

?BA geo:geometry ?BAGEO .

strdf:distance(?RGEO,?CGEO,uom:km)<10)

FILTER( strdf:during(?t1, “[2006-01-01:00:00:01,

2006-01-01:23:59:59]”^^strdf:period)) &&

strdf:before(?t1, ?t2) }

Find all areas that were forests in 2006 and got burned later within 10kms of a city

SELECT ?BA ?BAGEO

2006-01-01:23:59:59]”^^strdf:period)) &&

SELECT ?BA ?BAGEO

2006-01-01:23:59:59]”^^strdf:period)) &&

Temporal extension functions

stSPARQL: More details

We start from SPARQL 1.1.

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 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 literals

Strabon: A Scalable Geospatial RDF Store

stRDF graphs

stSPARQL/ GeoSPARQL

queries

WKT GML

Query Engine

Parser

Optimizer

Evaluator

Transaction

Manager

Storage Manager

Repository

Strabon

GeneralDB

Sesame

PostgreSQL

PostgreSQL Temporal

PostGIS

http://bit.ly/Strabon

Period

Outline

Motivation

Applications

Conclusions

Processing Chain (SciQL based)

HotSpots

Back End: MonetDB / Strabon

Use Case I: Real-Time Fire Monitoring (NOA)

41 7/1/2013

Data Vault

Eumetsat @ 9.5°East

Raw Data

• CORINE Landcover • Admin Boundaries • POIs

External Sources

• Search & Display

• Search for raw & Processing

• Real-time Fire Monitoring

• Refinement (Post-Processing)

• Linked Data

Geospatial Ontology

Web access based on Semantics

Linked Geospatial Data

Semantic technologies

NOA Ontology

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

OpenStreetMap

GeoNames

Linked Open Data Cloud

Linked Open Data GeoNames

Linked Open Data OpenSteetMap

Published by LinkedGeoData

Linked Open Data Greek Administrative Geography

Linked Open Data CORINE Land Use / Land Cover

Linked Open Data Coastline

Improvements

http://bit.ly/StrabonDemo

Semantic Enrichment for hotspots

Increase Accuracy by correlating with linked geospatial data

Generating Rapid Mapping products

Validating the results of the automatic annotation process by correlating them with auxiliary linked geospatial data

Assisting the training process by providing the user with contextual information about the area of interest

Semantic Enrichment for Hotspots

Enrich hotspot products

1. Connect each hotspot with a municipality that it is located

Improve accuracy with respect to underlying area

2. Eliminate false alarms in sea

3. Keep land part of the polygon

4. Eliminate false alarms in inconsistent land cover areas

Improve accuracy with respect to underlying area

2. Eliminate false alarms in sea

3. Keep land part of the polygon

4. Eliminate false alarms in inconsistent land cover areas

Improve accuracy with respect to temporal persistence of each hotspots

5. Remove “Christmas tree” effects

”Christmas tree effect”: some hotspots appear in a timestamp, in the next timestamp they disappear, then they re-appear again, and so on.

Improve the accuracy of EO data

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 the accuracy of EO data

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))}

Generating Rapid Mapping Products ZKI (Center for Satellite Based Crisis Information)

Improvements

http://bit.ly/DemoSextant

Generating Rapid Mapping Products: Sextant

Outline

Motivation

Applications

Conclusions

Metadata (xml annotation file)

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

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

Metadata (xml annotation file)

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

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

Improvements

http://bit.ly/DLRDemo

Discovery Queries

Select all patches corresponding to the class Water

SELECT ?g (GROUP_CONCAT(?annotation; separator=", ") AS

?labels)

WHERE {

?p rdf:type dlr:Patch .

?p dlr:hasGeometry ?g .

?p dlr:hasLabel ?l .

?l rdf:type dlr:Label .

?l dlr:correspondsTo dlr:Water .

FILTER (strdf:anyInteract(?g, "POLYGON ((12.301451

45.40493,12.398127 45.416817,12.386066

45.46502,12.289288 45.45313,12.301451

45.40493))"^^strdf:WKT)) .

GROUP BY ?g

Discovery Queries

Select all patches corresponding to the class Water

?labels)

WHERE {

?l dlr:correspondsTo dlr:Water .

45.40493,12.398127 45.416817,12.386066

45.46502,12.289288 45.45313,12.301451

45.40493))"^^strdf:WKT)) .

GROUP BY ?g

Discovery Queries

Select all patches corresponding to the class Water and its subclasses

?labels)

WHERE {

?l dlr:correspondsTo ?annotation .

?annotation rdfs:subClassOf dlr:Water .

45.40493,12.398127 45.416817,12.386066 45.46502,12.289288

45.45313,12.301451 45.40493))"^^strdf:WKT)) .

GROUP BY ?g

Discovery Queries

Select all patches corresponding to the class Water and its subclasses

?labels)

WHERE {

?annotation rdfs:subClassOf dlr:Water .

45.40493,12.398127 45.416817,12.386066 45.46502,12.289288

45.45313,12.301451 45.40493))"^^strdf:WKT)) .

GROUP BY ?g

Discover Correlation Between Datasets (1/2)

List the labels of all patches that are inside each CORINE Land Use/Land Cover class

SELECT DISTINCT ?clcLandUse ?annotation ?g

WHERE {

FILTER(strdf:anyInteract(?g, "POLYGON ((…))"^^strdf:WKT)).

?clc rdf:type ?clcType .

?clc teleios:hasCode ?clcCode .

?clcteleios:hasID ?clcID .

?clc teleios:hasGeometry ?clcG .

?clc teleios:hasLandUse teleios:continuousUrbanFabri.

FILTER(strdf:anyInteract(?clcG,"POLYGON((…))"^^strdf:WKT)).

FILTER (geof:sf-contains(?clcG, ?g)) . }

WHERE {

?clcLandUse ?annotation

clc:coastalLagoons

dlr:Bouy, dlr:Water, dlr:Boat, dlr:RiverDeposit, dlr:Bridge, dlr:UrbanBuildUp, dlr:Agriculture, dlr:Vegetation, dlr:Cemetery

clc:seaAndOcean dlr:BreakingWave, dlr:Water

clc:continuousUrbanFabric dlr:UrbanBuildUp, dlr:Vegetation, dlr:Building

clc:discontinuousUrbanFabric

dlr:UrbanBuildUp, dlr:Vegetation, dlr:Building

… …

WHERE {

?clcLandUse ?annotation

clc:coastalLagoons

dlr:Bouy, dlr:Water, dlr:Boat, dlr:RiverDeposit, dlr:Bridge, dlr:UrbanBuildUp, dlr:Agriculture, dlr:Vegetation, dlr:Cemetery

clc:seaAndOcean dlr:BreakingWave, dlr:Water

clc:continuousUrbanFabric dlr:UrbanBuildUp, dlr:Vegetation, dlr:Building

clc:discontinuousUrbanFabric

dlr:UrbanBuildUp, dlr:Vegetation, dlr:Building

… …

List the labels of all OpenStreetMap classes that are inside a patch SELECT ?annotation (GROUP_CONCAT(DISTINCT ?lgdType) AS ?lgdTypes)

WHERE {

FILTER (strdf:anyInteract(?g, "POLYGON ((...))"^^strdf:WKT)) .

?lgd lgdont:directType ?lgdDirectType .

OPTIONAL { ?lgd a ?lgdType .

FILTER((?lgdType!=lgdont:Node)&&(?lgdType !=?lgdDirectType)) .}

?lgd lgdgeo:geometry ?lgdGeo .

FILTER(strdf:anyInteract(?lgdGeo, "POLYGON ((...))"^^strdf:WKT)) .

FILTER (geof:sf-contains(?g, ?lgdGeo)) .

GROUP BY ?annotation

ORDER BY ?annotation

WHERE {

Conclusions

We developed the data model stRDF and the query language stSPARQL for representing and querying geospatial data that may change over time

Discussion

Use higher-level languages, stop worrying about how to store and manage metadata, just focus on the actual processing

Express common earth observation operations easily using the stSPARQL/GeoSPARQL queries instead of using a lengthy C program

Rapid prototyping without the need to recompile everything

Integration with publically available linked open geospatial data

Thank you for your attention!

Questions?

Strabon http://strabon.di.uoa.gr Manolis Koubarakis, Kostis Kyzirakos, Manos Karpathiotakis, Charalampos Nikolaou, Giorgos Garbis, Konstantina Bereta, Kallirroi Dogani, Stella Giannakopoulou and Panayiotis Smeros.

Mercurial repository: http://hg.strabon.di.uoa.gr Trac: http://bug.strabon.di.uoa.gr Mailing list: http://cgi.di.uoa.gr/~mailman/listinfo/strabon-users Continuous integration server http://test.strabon.di.uoa.gr/cis

Sextant: A web tool for browsing and mapping Linked Geospatial Data

http://test.strabon.di.uoa.gr/sextant/

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