N 2401/N 2402 New Work Item Proposal - Observation and Sampling schema Simon Cox Research Scientist 28 May 2008

ISO/TC 211 Copenhagen 2008 CSIRO Outline Background and history Observation schema Sampling features Applications Issues Next steps

ISO/TC 211 Copenhagen 2008 CSIRO Background & history

ISO/TC 211 Copenhagen 2008 CSIRO Origins and motivation XMML Boreholes Geophysics, gravity obs. Geochemistry/Assay data OCG Web Services/Sensor Web Enablement Remote sensing Environmental monitoring Wildfire detection Geology field & lab observations, boreholes Climate/Atmospheres/Oceans Ecosystems/Taxonomic obs. Water quality, groundwater …

ISO/TC 211 Copenhagen 2008 CSIRO Motivation for developing a common model Cross-domain data discovery and fusion Re-usable service interfaces

ISO/TC 211 Copenhagen 2008 CSIRO OGC Sensor Web Enablement OGC Web Services testbeds OWS – OWS Core elements of OGC SWE suite SensorML – provider-centric information viewpoint O&M – consumer-centric information viewpoint SOS, SAS – http interface to observations SPS – tasking interface sweCommon – data-types & encodings, including coverage encoding TML – low-level sensor streams

ISO/TC 211 Copenhagen 2008 CSIRO O&M states OGC IPR 2002 (OWS-1.2) OGC “Recommendation” 2003 (OWS-2) OGC “Best Practice” 2006 (OWS-4) OGC “Standard” 2007 ISO/OGC NWIP 2008

ISO/TC 211 Copenhagen 2008 CSIRO Acknowledgements XMML Consortium OGC testbed sponsors Geoscience Australia eWater CRC CSIRO

ISO/TC 211 Copenhagen 2008 CSIRO Observation schema

ISO/TC 211 Copenhagen 2008 CSIRO Science relies on observations Provides evidence & validation Involves sampling

ISO/TC 211 Copenhagen 2008 CSIRO What is “an Observation” Observation act involves a procedure applied at a specific time (Fowler & Odell, 1997ish) The result of an observation is an estimate of some property The observation domain is a feature of interest at or over some time

ISO/TC 211 Copenhagen 2008 CSIRO Examples The 7th banana weighed 270gm on the kitchen scales this morning The attitude of the foliation at outcrop 321 of the Leederville Formation was 63/085, measured using a Brunton on Specimen H69 was identified on by Amy Bachrach as Eucalyptus Caesia The image of Camp Iota was obtained by Aster in 2003 Sample WMC997t collected at Empire Dam on was found to have 5.6 g/T Au as measured by ICPMS at ABC Labs on The X-Z Geobarometer determined that the ore-body was at depth 3.5 km at 1.75 Ga The simulation run on indicated that the pressure in the hanging- wall at 618 Ma was reduced 4 MPa

ISO/TC 211 Copenhagen 2008 CSIRO In “pictures”

ISO/TC 211 Copenhagen 2008 CSIRO Generic pattern for observation metadata An Observation is an action whose result is an estimate of the value of some property of the feature-of-interest, obtained using a specified procedure Where’s the “location”? In the feature-of-interest, reconciling remote and in-situ observations Many complexities are encapsulated in the Process (sensor), PropertyType, Result

ISO/TC 211 Copenhagen 2008 CSIRO Is O&M just an application schema? Yes – but domain neutral Fundamental data-estimation pattern

ISO/TC 211 Copenhagen 2008 CSIRO Observation model is corollary of GFM Observation role wrt target is propertyValueProvider result type (and encoding) must be consistent with PropertyType observedProperty is bound to target type

ISO/TC 211 Copenhagen 2008 CSIRO Procedure vs. observedProperty observedProperty supports discovery by observation users “show me all the observations of temperature and wind-speed” procedure provides strict definition “how was that value obtained?” …or provider-centric discovery “show me all the data collected by instrument X”

ISO/TC 211 Copenhagen 2008 CSIRO Some properties vary within a feature colour of a Scene or Swath varies with position shape of a Glacier varies with time temperature at a Station varies with time rock density varies along a Borehole Variable values may be described as a Function on some axis of the feature Corresponding Observation/result is a Function If domain is spatio-temporal, also known as coverage or map

ISO/TC 211 Copenhagen 2008 CSIRO Variable property  coverage valued result

ISO/TC 211 Copenhagen 2008 CSIRO Other specializations by result-type

ISO/TC 211 Copenhagen 2008 CSIRO Assignment of property values For each property of a feature, the value is either i.asserted name, owner, price, boundary (cadastral feature types) ii.estimated colour, mass, shape (natural feature types) i.e. error in the value is of interest

ISO/TC 211 Copenhagen 2008 CSIRO When is the Observation viewpoint interesting? If the data-acquisition metadata is of concern

ISO/TC 211 Copenhagen 2008 CSIRO Sampling features

ISO/TC 211 Copenhagen 2008 CSIRO Proximate vs ultimate feature-of-interest The ultimate (“project”) thing of interest may not be directly or fully accessible 1.Sensed property is a proxy e.g. want land-cover, but observe colour Post-processing required FoI may change during processing – e.g. “scene”  “tract” 2.Proximate feature of interest embodies a sample design Rock-specimen samples an ore-body or geologic unit Well samples an aquifer Profile samples an ocean/atmosphere column Cross-section samples a rock-unit Some standard designs are common

ISO/TC 211 Copenhagen 2008 CSIRO Sampling Manifolds N.B. SF provide bounds-for sub- sampling, but not details of their spatial decomposition

ISO/TC 211 Copenhagen 2008 CSIRO Basic sampling features Domain feature type

ISO/TC 211 Copenhagen 2008 CSIRO Specimen Specimens are SamplingFeatures used for ex-situ observation and analysis

ISO/TC 211 Copenhagen 2008 CSIRO Applications

ISO/TC 211 Copenhagen 2008 CSIRO Boreholes and logs

ISO/TC 211 Copenhagen 2008 CSIRO Water quality measured along a ferry track

ISO/TC 211 Copenhagen 2008 CSIRO Water quality of aquifers observed in wells

ISO/TC 211 Copenhagen 2008 CSIRO Specimens and “outcrops”

ISO/TC 211 Copenhagen 2008 CSIRO Specimen processing chains

ISO/TC 211 Copenhagen 2008 CSIRO Domain profile feature of interest Feature-type is taken from a domain-model (e.g. Geology) observed property Belongs to feature-of-interest-type procedure Standard procedures, suitable for the property-type

ISO/TC 211 Copenhagen 2008 CSIRO In GeoSciML

ISO/TC 211 Copenhagen 2008 CSIRO Deployments GeoSciML (IUGS Testbed) Climate Science Markup Language v2 (UK Research Councils) Water Observations Markup Language (Australia/US) Various database schemas have been designed to match GA, NVCL

ISO/TC 211 Copenhagen 2008 CSIRO Issues

ISO/TC 211 Copenhagen 2008 CSIRO Document format & structure ISO format (not OGC) Project leader will need some help here … (e.g. where is the ISO template? One-part or two-parts? Clarify scope statements Title O&M, vs O&S Abstract spec? Include XML implementation? Status? Use of stereotype «FeatureType» in UML

ISO/TC 211 Copenhagen 2008 CSIRO Model Some properties are promoted to top level – are these justified?

ISO/TC 211 Copenhagen 2008 CSIRO Relationship to ISO & IS or TR UK suggests incorporate content in revision of ISO (Observations) and (Sampling) O&S are domain-independent application schemas AnyFeature-->GF_FeatureType PropertyType-->GF_PropertyType Sampling features provide context for coverage results Manifold, but not decomposition details – i.e. domain-extent & CRS Clarification required in ISO re. coverages feature-properties may be non-constant  GF_AssociationType may link a feature to a coverage New stereotype for properties? «estimatedProperty» for those feature-properties whose values are obtained through observation, and hence have a finite error

ISO/TC 211 Copenhagen 2008 CSIRO Relationship to GML gml:Observation – permissible for observation target not to be a feature?

ISO/TC 211 Copenhagen 2008 CSIRO Dependencies Feature & Coverage models - OK Property-types & registers ISO revision deadlock Process (SensorML) SWE Common – coverages & records

ISO/TC 211 Copenhagen 2008 CSIRO Next Steps?

Thank you Exploration & Mining Simon Cox Research Scientist Phone: Web: Contact Us Phone: or Web:

ISO/TC 211 Copenhagen 2008 CSIRO Observed property Length, mass, temperature, shape location, event-time, orientation colour, chemical concentration count/frequency, presence species or kind (classification) Expressed using a reference system or scale Scale may also be ordinal or categorical May require a complex structure “Sensible”, but not necessarily physical …

ISO/TC 211 Copenhagen 2008 CSIRO Feature-of-interest The observed property is associated with something Location does not have properties, the substance or object at a location does Observed property must be logically consistent with the feature-of-interest E.g. rock-density, pixel-colour, city-population, ocean-surface-temperature … i.e. the Observation “target”

ISO/TC 211 Copenhagen 2008 CSIRO Procedure Instruments & Sensors Respond to a stimulus from local physics or chemistry Intention may concern local or remote source Sample may be in situ or re-located

ISO/TC 211 Copenhagen 2008 CSIRO Procedures are usually process chains Procedure often includes data processing, to transform “raw” data to semantically meaningful values Voltage  orientation count  radiance  NDVI Position + orientation  scene-location Mercury meniscus level  temperature Shape/colour/behaviour  species assignment This requires consideration of “sensor”-models and calibrations

ISO/TC 211 Copenhagen 2008 CSIRO Advanced procedures Modelling, simulation, classification are procedures “raw” data == modeling constraints (sensor-outputs=process-inputs) “processed” data == simulation results (outputs) “interpreted” data == classification results (outputs) SensorML provides a model and syntax for describing process- chains

ISO/TC 211 Copenhagen 2008 CSIRO Observations, features and coverages Feature summary Property-value evidence Multiple observations one feature, different properties: feature summary evidence A property-value may be a coverage Same property on multiple samples is a another kind of coverage Multiple observations different features, one property: coverage evidence

ISO/TC 211 Copenhagen 2008 CSIRO “Cross-sections” through collections SpecimenAu (ppm) Cu-a (%)Cu-b (%)As (ppm)Sb (ppm) ABC A Row gives properties of one feature A Column = variation of a single property across a domain (i.e. set of locations)

ISO/TC 211 Copenhagen 2008 CSIRO SOS getObservation getResult describeSensor getFeatureOfInterest Accessing data using the “Observation” viewpoint WFS/ Obs getFeature, type=Observation WCS getCoverage getCoverage (result) Sensor Register getRecordById WFS getFeature e.g. SOS::getResult == “convenience” interface for WCS

ISO/TC 211 Copenhagen 2008 CSIRO WFS/ SFS Accessing data using the “Sampling Feature Service” viewpoint WFS getFeature WCS getCoverage getCoverage (property value) SOS getObservation Common data source getFeature (sampling Feature) getFeature (coverage property value) getFeature (relatedObservation) getCoverage (result) Sensor Register getRecordById (procedure) getFeature (featureOfInterest) getObservation (relatedObs) getResult (property value)

ISO/TC 211 Copenhagen 2008 CSIRO WFS Accessing data using the “Domain Feature” viewpoint WCS getCoverage (property value) getFeature SOS getResult (property value) The “George Percivall preferred™” viewpoint #1 – observations are property-value-providers for features ??

ISO/TC 211 Copenhagen 2008 CSIRO WCS Accessing data using the “just the data” viewpoint WFS getFeature/geometry (domain exent) getCoverage SOS getResult (lots of ‘em) (range values) The “George Percivall preferred™” viewpoint #2 – observations are range-value-providers for coverages

ISO/TC 211 Copenhagen 2008 CSIRO Conclusions Different viewpoints of same information for different purposes Summary vs. analysis Some values are determined by observation Sometimes the description of the estimation process is necessary Transformation between views important Management of observation evidence can be integrated (Bryan Lawrence issues) For rich data processing, rich data models are needed Explicit or implicit Data models (types, features) are important constraints on service specification

ISO/TC 211 Copenhagen 2008 CSIRO Development and validation of O&M model Developed in the context of XMML Geochemistry/Assay data OGC Sensor Web Enablement – environmental and remote sensing Subsequently applied in Water resources/water quality (AWDIP, WRON) Oceans & Atmospheres (UK CLRC, UK Met Office) Natural resources (NRML) Taxonomic data (TDWG) Geology field data (GeoSciML) I could have put dozens of logos down here

ISO/TC 211 Copenhagen 2008 CSIRO O&M Status OGC Standard December 2007 ISO/TC 211 New Work Item June 2008 Core piece of OGC Sensor Web Enablement suite See also: SensorML, Sensor Observation Service, SAS, SPS Basis for Water Observations ML Key aspect of GeoSciML