From Earth Science Observations to GI Coverages: Towards an harmonization framework for coverages...

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From Earth Science Observations to GI Coverages:

Towards an harmonization framework for coverages

Stefano NativiItalian National Research Council

(Institute of Methodologies for Environmental Analysis) and

University of Florence

Scientific Data Type and Feature modeling-----

Rutherford Appleton Lab 7-9 Mar 07

[email protected]

Outline• Rationale• From events to datasets

– An harmonization framework for the coverage domain

• netCDF Coverage implementation– Abstract, content and encoding models– ncML-Gml

• O&M Coverage implementation• Scientific Feature Coverage implementation

• Future work• Conclusions

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Rationale Provide Information Society with an effective, NRT and easy-to-use

fruition of multidimensional Earth Sciences datasets (e.g. 4/5-D)

Geospatial datasetsAcquisition and

Encoding

Knowledge Extraction

and Harmonization

StandardModels andInterfaces

Explicit Semantic level /Interoperability level

SOCIETY

INFRASTRUCTURES,

PLATFORMS

and SYSTEMS

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Over-simplified Worldviews• To the Geographic Information community, the world

is:– A collection of featuresfeatures (e.g., roads, lakes, plots of land)

with geographic footprints on the Earth (surface).– The featuresfeatures are discrete objectsdiscrete objects described by a set of

characteristics such as a shape/geometryshape/geometry

• To the Earth Science community, the world is:– A set of event observationsobservations described by parametersparameters

(e.g., pressure, temperature, wind speed) which vary as continuous functionscontinuous functions in 3-dimensional space and time.

– The behavior of the parametersparameters in space and time is governed by a set of equations.equations.

[from Ben Domenico]

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ES and GI Data Models• Diverse semantic and portability levels• Diverse attention to domain multi-dimensionality• Diverse aggregation structures and Data types

[from J. Caron]

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Observ.s Vs. Features: Value-added Chaining

• (Event) Observation – estimate of value of a property for a single

specimen/station/location – data-capture, with metadata concerning procedure, operator, etc

• Feature – object having geometry & values of several different

properties 1. classified object

– snapshot for transport geological map elements 2. object created by human activity

– artefact of investigation borehole, mine, specimen

[from S.Cox Information Standards for EON]

• Coverage – compilation of values of a single property across the domain of

interest – data prepared for analysis/pattern detection

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An Harmonization Framework• A unified framework with: O&M model, GF Model and IGCD Model • Harmonization

– O&M classes implementations for the Coverage domain – Scientific feature implementations for the Coverage domain

• GI:coverage is a GI:feature type

<<O&M Model>>OGC model

<<General FeatureModel>>

ISO 19107,…

<<IGCD Model>>

ISO 19123

O&M Coverages

<<IGCD Model>>

CF- NetCDF Grid Model

CF-NetCDF Coverage

<<O&M Model>>

CF- NetCDF Observation Model … .

This allows to develop crosswalks towards well-accepted ES data models

- Example: CF-netCDF community

ES

GI

<<IGCD Model>>

ISO 19123

<<IGCD Model>>


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multidimensional Observation

dataset(e.g. 4/5D hypercube)

N-Dimension Coordinate Systems

ES Dataset content

<dimension>,<coordinateSystem><coordinateAxis>

<netcdf type>

explicit/semi-implicit/implicit Geometry

<dimension>,<variable>

0110110011111010101010010101…01101100111

11010101010010101…

Scalar measured quantities

<variable>

IGCD

CF-netCDF

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2D+elev+timedataset

2D Spatial Coordinate System + elev + time

Range set

GI coverage content

<_CoordinateSystem>,<coordinateSystem Axis>

<rangeSet> <_Coverage>

explicit/implicit Geometry

Spatial Reference System (SRS)

<gridDomain>,<rectifiedGrid Domain>,<multipointDomain>

<GeographicCRS>

IGCD

CF-netCDF

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2 Dimension Coordinate System

Implicit/explicit Geometry

Range set




Range set




Range set




Range set


The Mediation Process

ES hyperspacedataset(3/4/5D)

2D + elev + time Coverages

2D+elev+time dataset

2D SCS + elev + time


Range set


a Coverage

0110110011111010101010010101…01101100111

11010101010010101…

N-Dimension Coordinate Systems

explicit/semi-implicit/implicit Geometry

Scalar measured quantities s S

SSS

S

IGCD

CF-netCDF

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The Implementation

• ES data model– netCDF– Extra metadata: CF conventions

• GI Coverage model– ISO 19123: DiscreteGridPointCoverage

• Harmonization implementation-style– Declarative style

• Mediation Markup Language• Rule-based procedure

IGCD

CF-netCDF

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CF-netCDF Model

• NetCDF data model was extended adding a set of conventions– One of the most

popular convention is the Climate and Forecasting metadata convention (CF)

– Introduce more specific semantic elements (i.e. metadata) required by different communities to fully describe their datasets

Coordinate

{ shape->size = 1}

units as defined inUdunits package

TimeCoordinate

-calendar:String-month_lengths :String-leap_year: int-leap_month :int

Identified either via:- units (length, pressure, etc.)- positive attribute

Vertical Coordinate

-units:String-positive:up|down-formula_terms:String

LatLonCoordinate

CF-CoordinateVariable

-axis:X|Y|Z|T

DimensionalQuantity

-units:String

- same name as related dimension - numeric values- monotonic

Dimension

CoordinateVariable

local attributes override global ones

CF-Variable

-institution :String-source:String-references:String-comment:String-long_name :String-standard_name:String

Variable

-shape:Dimension[]

Dataset

-Conventions:String-history:String-title:String

CF-Dataset

-Conventions:String=CF-1.0-institution :String-source:String-reference:String-comment:String

CF-netCDFProfileModel

*

*

*

*

*

coordinates

netCDF Model

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DiscreteGridPointCoverage

<< DataType >>CV_GridCoordinates

+coordValues:Sequence<Integer>

<< DataType >>CV_GridEnvelope

+low:CV_GridCoordinates+high:CV_GridCoordinates

<< Type >>CV_RectifiedGrid

+dimension:int+axisNames:Sequence<CharacterString>+extent:CV_GridEnvelope+origin:DirectPosition+offestVectors:Sequence<Vector>

<< Type >>CV_GridValueMatrix

+values:Sequencee<Record>+sequencingRule:CV_SequenceRule+startSequence:CV_GridCoordinates

<< Abstract >>SC_CRS

<< metaclass >>GF_FeatureType

<< Type >>CV_DiscreteGridPointCoverage

+domainExtent:EX_Extent[1..*]+rangeType:RecordType+commonPointRule :CommonPointRule

DiscreteGridPointCoverageProfile model

<< instantiates >>

*

CRS+

Coordinate Reference System

evaluator+ 0..1

valueAssignment+

PointFunction

valuation

Record

+attributes:Dctionary<AttributeName, Any>

<< metaclass >>RecordType

+typeName:TypeName+attributeTypes :Dictionary<AttributeName, TypeName>

TypeName

+aName:String

AttributeName

+aName:String+attributeType:TypeName

record+*

recordType+

RecordType

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Coordinate

{ shape->size = 1}

units as defined inUdunits package

TimeCoordinate

-calendar:String-month_lengths :String-leap_year: int-leap_month :int

Identified either via:- units (length, pressure, etc.)- positive attribute

Vertical Coordinate

-units:String-positive:up|down-formula_terms:String

LatLonCoordinate

CF-CoordinateVariable

-axis:X|Y|Z|T

DimensionalQuantity

-units:String

- same name as related dimension - numeric values- monotonic

Dimension

CoordinateVariable

local attributes override global ones

CF-Variable

-institution :String-source:String-references:String-comment:String-long_name :String-standard_name:String

Variable

-shape:Dimension[]

Dataset

-Conventions:String-history:String-title:String

CF-Dataset

-Conventions:String=CF-1.0-institution :String-source:String-reference:String-comment:String

CF complete Class Diagram

*

*

*

*

*

coordinates

<< DataType >>CV_GridCoordinates

+coordValues:Sequence<Integer>

<< DataType >>CV_GridEnvelope

+low:CV_GridCoordinates+high:CV_GridCoordinates

<< Type >>CV_RectifiedGrid

+dimension:int+axisNames:Sequence<CharacterString>+extent:CV_GridEnvelope+origin:DirectPosition+offestVectors:Sequence<Vector>

<< Type >>CV_GridValueMatrix

+values:Sequencee<Record>+sequencingRule:CV_SequenceRule+startSequence:CV_GridCoordinates

<< Abstract >>SC_CRS

<< metaclass >>GF_FeatureType

<< Type >>CV_DiscreteGridPointCoverage

+domainExtent:EX_Extent[1..*]+rangeType:RecordType+commonPointRule :CommonPointRule

DiscreteGridPointCoverageProfile model

<< instantiates >>

*

CRS+

Coordinate Reference System

evaluator+ 0..1

valueAssignment+

PointFunction

valuation

Record

+attributes:Dctionary<AttributeName, Any>

<< metaclass >>RecordType

+typeName:TypeName+attributeTypes :Dictionary<AttributeName, TypeName>

TypeName

+aName:String

AttributeName

+aName:String+attributeType:TypeName

record+*

recordType+

RecordType

0…1

0…1

1…n

Mapping Rules

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Concept type Definition NotesAn observation is a function from a given multidimensional real domain (d) to a multidimensional real co-domain (c).Note: a netCDF variable is a special case of Observation (with domain in d and c=1).

d = {b1, b2, …, bn}A dataset is a set of observation data.Note: a netCDF file is a special case of Dataset.

S: {3, SCS} A Spatial Domain is 3 with a law from 3 to a location in the physical universe (Spatial Coordinate System). A 2D Spatial (Planar) Domain is the restriction of S to 2.

c: {S, T} n

n

C = {c}

A coverage is a function defined from a Spatio-Temporal Domain (e.g. Lat, Lon, Height, Time) to a multidimensional real co-domain (n).Note: if a set of CF-netCDF coordinate variables is a Spatio-Temporal Domain, then CF-netCDF variables defined over the corresponding dimensions can be mapped to Coverages

Domain and Functional Definitions

Observation Data/Observation

b: d c

d, c

B= {b}Dataset

Spatial Domain

Coverage

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Concept type Definition Notes

g(b) =c

g: B C

Given an observation data, the Observation to Coverage operator generates a coverage.

s = {g1, g2, …, gn}

A Dataset to Coverages operator consists of a set of Observation to Coverage operators.

Hence, Given an dataset element, the Dataset to Coverages operator generates a set of coverage elements.

(Another task is the metadata elements mapping from dataset to the whole set of coverages).

p(c) = m p: C M

A Coverage Portrayal operator transforms a coverage to a map, by means of a combination of the following operations:

– Domain restriction (to a certain Z0 and T0);

– Co-domain restriction (to a scalar quantity).

Domain and Functional Definitions

Observation to Coverage Operator

Dataset to Coverage Operator

Coverage Portrayal Operator

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Data model harmonization: Implementation style

Abstract model level

HyperspatialDataset Coverage/Feature

netCDF + CFContent model level

ISO 19123 Coverage Model

GIS InformationCommunity

Earth SciencesInformationCommunity

Encoding level ncML GMLGGMLMLGGMLML

Mapping rules

Mapping rules

IGCD

CF-netCDF

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Data model harmonization

ISO 19123Data Model

GIInformationCommunity

Earth SciencesInformationCommunity

Information Society(e.g. Spatial Data Infrastructure)

ncML-GMLncML

EncodingModel

GML 3.xEncoding

Model

WCS 1.xContent Model

WFSContent Model

Data Models Mediation

netCDFData Model

CFMetadata

IGCD

CF-netCDF

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ncML-GML• Mediation Markup Language• An extension of ncML (netCDF Markup Language) based on GML

(Geography Markup Language) grammar

IGCD

CF-netCDF

Content Feature of interest

Content Models

Encoding Models

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Available Language specification and Tools

• The ncML-GML markup language implements the presented reconciliation model

• It is a Mediation Markup Language between ncML (netCDF Markup Language) and GML– An extension of ncML core schema, based on GML grammar

• NcML-GML version 0.7.3– based on GML 3.1.1

• N2G version 0.8– Java API for ncML-GML ver. 0.7.3

• WCS-G– WCS 1.0 server which supports ncML-GML/netCDF documents

• Subsetting (domain and range-set)– netCDF– ncML-GML 0.7.3

• Client for WCS-G– GI-go thick client

GGMLMLGGMLML

IGCD

CF-netCDF

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– O&M Coverage implementation for the Coverage domain– Scientific feature implementations as Coverage



ISO 19107,…

O&M Coverages

<<IGCD Model>>


CF-NetCDF Coverage

<<O&M Model>>


This allows to implement and harmonize other well-accepted data models

ES

GI<<O&M Model>>

OGC model

<<IGCD Model>>

ISO 19123

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From Event to Datasets (O&M)• Instruments and sensors observe and measure properties of Feature-of-

interests• Observations and measurements generate datasets • Datasets can be modeled/stored as either boundary or coverage data

– It depends on the observed property variability over the Feature_of_interest domain

Observation

PropertyFeatureOfInterest+domain

monitors

0..*

detects

characterizes 0..*

Dataset

+result0..*

Georelational Dataset CompositeDataset

Event

O&M Model

IGCD Model

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CoverageObservation implementation

Event

«FeatureT ype»observ ation::Observ ation

«FeatureT ype»DiscreteCov erageObserv ation

+ result: CV_DiscreteCoverage

«FeatureT ype»PointCov erageObserv ation

+ result: CV_DiscretePointCoverage

«FeatureT ype»TimeSeriesObserv ation

+ result: CV_DiscreteT imeInstantCoverage

«FeatureT ype»Cov erageObserv ation

+ result: CV_Coverage+ resultDefinition: RecordT ype

«FeatureT ype»ElementCov erageObserv ation

+ result: CV_DiscreteElementCoverage

proximateFeatureOfInterest

1

0..*

«FeatureType»Observ ation

+ quality: DQ_Element [0..1]+ responsible: CI_ResponsibleParty [0..1]+ result: Any

«FeatureType»Event

+ eventParameter: TypedValue [0..*]+ time: TM_Object

«DataType»TypedValue

+ property: ScopedName+ value: Any

«Union»Procedure

+ procedureType: ProcedureSystem+ procedureUse: ProcedureEvent

AnyIdenti fiableObject

«FeatureType»AnyIdentifiableFeature

AnyDefinition

«ObjectType»Phenomenon

+followingEvent 0..*+precedingEvent 0..*

+generatedObservation

0..*

+procedure 1

+observedProperty1{Definition must be of aphenomenon that is a propertyof the featureOfInterest}

+propertyValueProvider

0..*

+featureOfInterest

1

<<CoverageRangeSet>>Un-

UniformFeatureProperty

un-uniformObservedProperty

<<CoverageDomain>>

FeatureDomainExtentSet<GM_Object>

CoverageObservation. featureOfInterest is the Feature Domain Extent

CoverageDomain is a proximate FeatureOfInterest

CoverageObservation. observedProperty is un-uniform over the Feature Domain Extent

Observed Phenomenon is the Range-set of generated coverages.

O&M Model

IGCD Model

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SamplingFeature implementation

Event

«FeatureType»Observ ation

ObservationProcedure

«ObjectType»Surv eyProcedure

«FeatureType»SamplingFeature

+ property: TypedValue [0..*]+ responsible: CI_ResponsibleParty [0..1]

«DataType»SamplingFeatureRelation

+ role: GenericName

AnyIdentifiableObject

«FeatureType»AnyIdentifiableFeature

«DataType»TypedValue

+ property: ScopedName+ value: Any

+propertyValueProvider 0..*

+featureOfInterest 1

+relatedObservation 0..*

+relatedSamplingFeature 0..* +source

0..* +target

+surveyDetails 0..1

+sampledFeature

0..1

CV_featureOfInterest

1

Event

«FeatureType»observ ation::Observ ation

«FeatureType»DiscreteCov erageObserv ation

+ resul t: CV_DiscreteCoverage


+ result: CV_DiscretePointCoverage


+ result: CV_DiscreteT imeInstantCoverage

«FeatureType»Cov erageObserv ation

+ resul t: CV_Coverage+ resul tDefini tion: RecordT ype


+ resul t: CV_DiscreteElementCoverage

CoverageSamplingFeature

<<CoverageDomain>>

FeatureDomainExtent


tileShape: GM_Object [0..*]tileDistributionType: TypedDistr [0..*]tileResolution: TypedRes [0..*]

CoverageSurveyProcedure

0..1CV_surveyDeta

ils

observableShape [0..1]observableAxes: CS_Axis [1..*]observableFrequency: TM_int [1..*]Set<GM_Object> [0..1

0..*

CV_relatedObservation

In a coverage observation framework, to define a CoverageSamplingFeature as a SamplingFeature subtype.

CoverageSamplingFeature is a proximate Feature of interest

FeatureDomainExtent class is the simplest way to implement the CoverageSamplingFeature concept

O&M Model

IGCD Model

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• The CoverageSamplingFeature is characterized by the following fields: – the possible shapes of the observation coverage domain– the possible observation domain axes– the possible observation frequencies

• The CoverageSurveyProcedure characterizes the sampling observation procedure

– described by the following fields: shape(s), domain distribution(s) and resolution(s) of coverage tiles worked out by observations.

CV_featureOfInterest

1

Event

«FeatureT ype»observ ation::Observ ation

«FeatureT ype»DiscreteCov erageObserv ation

+ resul t: CV_DiscreteCoverage


+ resul t: CV_DiscretePointCoverage


+ resul t: CV_DiscreteT imeInstantCoverage

«FeatureT ype»Cov erageObserv ation

+ resul t: CV_Coverage+ resul tDefini tion: RecordType


+ resul t: CV_DiscreteElementCoverage

CoverageSamplingFeature

<<CoverageDomain>>FeatureDomainExtent


tileShape: GM_Object [0..*]tileDistributionType: TypedDistr [0..*]tileResolution: TypedRes [0..*]

CoverageSurveyProcedure

0..1CV_surveyDetails

observableShape [0..1]observableAxes: CS_Axis [1..*]observableFrequency: TM_int [1..*]Set<GM_Object> [0..1

0..*

CV_relatedObservation

SamplingFeature implementationO&M Model

IGCD Model

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ExtensiveSamplingFeatures implementations

• Most of extensive sampling features may be implemented as CoverageSamplingFeature sub-types.

– Specific values of CoverageSurveyProcedure and CoverageSamplingFeature fields characterize the site types for observation sampling.

Fields / ExtensiveSamplingFeature

FeatureDomainExtent.Observable (domain) Axes

CoverageSurveyProcedure.Tile Shape

CoverageSurveyProcedure.Tile Distribution

Scene Space (2-3 D) quadrilateral Regular/Irregular

Swath Space (2D) ellipse Regular

Profile Space (1D) andVertical (i.e. pressure or density)

quadrilateral Irregular

Scanning Radar Space (2D) circle sector Regular

Time Series (Profile Series)

Space (1D) or Vertical (i.e. pressure or density) and Time (Actual or Forecast time)

quadrilateral Regular/Irregular

O&M Model

IGCD Model

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– O&M Coverage implementation – Scientific feature implementations for the Coverage domain


O&M Coverages

<<IGCD Model>>


CF-NetCDF Coverage

<<O&M Model>>


This allows to implement and harmonize other well-accepted data models

ES

GI<<O&M Model>>

OGC model


ISO 19107,…

<<IGCD Model>>

ISO 19123

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Coverage is a Feature type• Discrete coverage modeled as a logical complex feature

– implicit geometry (derived from the coverage function concept)– a collections of tiles which cover/sample the coverage spatial&temporal domain

• Tiles may be modeled as simple features, characterized by explicit geometry.Location Properties

Property 1 Property 2 … Property m

(x1, y1) Value11 Value1

2 … Value1m


2 … Value2m


2 … Value3m

(xn, yn) Valuen1 Valuen

2 … Valuenm

GF Model IGCD Model

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Scientific Feature types are ExtensiveSamplingFeatures

(Coverage sub-types implem.)• Scientific Feature types (Extensive Sampling Features)

– Certain feature types are only associated with sampling, and have no significant function outside of their role in the observation process …. Feature types having this behaviour are similar across all application domains [O&M]

Fields / Scientific Feature types

FeatureDomainExtent.Observable (domain) Axes

CoverageSurveyProcedure.Tile Shape

CoverageSurveyProcedure.Tile Distribution

Grid Space (2-3 D) quadrilateral Regular/Irregular

Swath Space (2D) ellipse Regular

Ragged section Space (1D) andVertical (i.e. pressure or density)

quadrilateral Irregular

Scanning Radar Space (2D) circle sector Regular

Time Series (Profile Series)

Space (1D) or Vertical (i.e. pressure or density) and Time (Actual or Forecast time)

quadrilateral Regular/Irregular

GF Model IGCD Model

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Scientific Feature types are ExtensiveSamplingFeatures (Coverage sub-types implem)

• ISO 19123 recognizes some coverage sub-types which can be easily mapped to Scientific feature types

Coverage sub-types

GF Model IGCD Model

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Content Vs Container Features• ScientificFeatures ContainerFeatures

(ExtentSamplingFeatures) (Coverage sub-type implementations)• In order to facilitate interoperability, coverage can be modeled as a general container feature (i.e. proximate Feature-of-interest)

Observation values

Observation Sampling/functions

Encoding ModelAbstract Model

Application Elements

Application Elements Behavior

Applicationspecific

Applicationagnostic Arrays

Proximate FOI(Coverage Subtypes)

Application FOI

Inference Rules

Content Model

Binary File

XML elements

Semantics level

Dat

aset

sMet

adat

a XML elements

XML elements

Container

Content Impl

icite

ness

GF Model IGCD Model

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Future Work

• To extent the harmonization model between netCDF and GI Coverage realms– O&M concepts– Scientific Features (Sampling Features)

• To encode it in XML– Encode and access netCDF datasets

• ncML-Gml • CSML, O&M ML, other GML dialects?

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Main Conclusions• ES and GI data model interoperability is more

and more important for Society’s applications• The GI coverage concept seems to be a good

solution to bridge GI and ES data models• We need a unified framework to harmonize

concepts coming from O&M, GF and IGCD models in the coverage domain

• To facilitate the mediation between ES and GI communities– A valuable case in point: the FES realm (CF-netCDF)

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