1. www.csiro.au Exchanging observations and measurements: applications of a generic model and encoding Simon Cox CSIRO Exploration and Mining 15 December 2006

2. 2 of 18 AGU Fall 2006 Observations What is “an Observation” Observation act involves a procedure applied at a specific time and place Result of an observation is an estimate of some property value The property is associated with the observation domain or feature of interest

3. 3 of 18 AGU Fall 2006 Observations Observed property Sensible phenomenon or property-type  Length, mass, temperature, shape  location, event-time  colour, chemical concentration  count/frequency, presence  species or kind Expressed using a reference system or scale  Scale may also be ordinal or categorical  May require a complex structure “Sensible”, but not necessarily physical …

4. 4 of 18 AGU Fall 2006 Observations Feature-of-interest The observed property is associated with something  “Location” does not have properties, the substance or object at a location does  The property must be logically consistent with the feature-type, as defined in the application domain  E.g. rock-density, pixel-colour, city-population, ocean-surface-temperature … Observation-target

5. 5 of 18 AGU Fall 2006 Observations Procedures 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 Observers, algorithms, simulations, processing chains … “estimation” process

6. 6 of 18 AGU Fall 2006 Observations A common pattern: the observation model An Observation is an Event whose result is an estimate of the value of some Property of the Feature-of-interest, obtained using a specified Procedure The Feature-of-interest concept reconciles remote and in-situ observations

7. 7 of 18 AGU Fall 2006 Observations Proximate vs. Ultimate Feature-of-Interest The proximate feature-of-interest may sample a more meaningful domain-feature  Rock-specimen samples an ore-body  Well samples an aquifer  Sounding samples an ocean/atmosphere column  Cross-section samples a rock-unit  Scene samples the earth’s surface i.e. two feature types involved, with an association between them

8. 8 of 18 AGU Fall 2006 Observations Sampling features

9. 9 of 18 AGU Fall 2006 Observations Application to a domain feature of interest  Feature-type taken from a domain-model observed property  Member of feature-of-interest-type procedure  Suitable for property-type

10. 10 of 18 AGU Fall 2006 Observations Geology domain model - feature type catalogue Borehole  collar location  shape  collar diameter  length  operator  logs  related observations  … Fault  shape  surface trace  displacement  age  … Ore-body  commodity  deposit type  host formation  shape  resource estimate  … Conceptual classification Multiple geometries Geologic Unit  classification  shape  sampling frame  age  dominant lithology  … License area  issuer  holder  interestedParty  shape(t)  right(t)  …

11. 11 of 18 AGU Fall 2006 Observations Water resources feature type catalogue Aquifer Storage Stream Well Entitlement Observation …

12. 12 of 18 AGU Fall 2006 Observations Meteorology feature type catalogue Front Jetstream Tropical cyclone Lightning strike Pressure field Rainfall distribution … Bottom two are a different kind of feature

13. 13 of 18 AGU Fall 2006 Observations Some property values are not constant  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 Coverage over some axis of the feature

14. 14 of 18 AGU Fall 2006 Observations 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

15. 15 of 18 AGU Fall 2006 Observations Development and validation O&M conceptual model and XML encoding, developed in the context of  XMML Geochemistry/Assay data  OGC Sensor Web Enablement – environmental and remote sensing Subsequently applied in  Water resources/water quality (WQDP, 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

16. 16 of 18 AGU Fall 2006 Observations Status “Observations and Measurements” OGC Best Practice paper – 2006 Currently in public RFC, Adopted Specification – 2007? ISO specification – 2008-9?

17. 17 of 18 AGU Fall 2006 Observations Sensor Observation Service WFS/ Obs getFeature, type=Observation WCS getCoverage getCoverage (result) Sensor Registry getRecord SOS getObservation getResult describeSensor getFeatureOfInterest WFS getFeature

18. 18 of 18 AGU Fall 2006 Observations Summary A unified model for observations is possible  Using careful separation of concerns, and modularization of domain- specific aspects  Highly normalized model Applicable to both “constant” and “coverage” properties  i.e. both measurements and images/time-series Requires careful attention to “feature-of-interest” and intention OGC XML encoding and web-service interface available

19. www.csiro.au Thank You CSIRO Exploration and Mining NameSimon Cox TitleResearch Scientist Phone+61 8 6436 8639 EmailSimon.Cox@csiro.au Webwww.seegrid.csiro.au Contact CSIRO Phone1300 363 400 +61 3 9545 2176 Emailenquiries@csiro.au Webwww.csiro.au

20. 20 of 18 AGU Fall 2006 Observations 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

21. 21 of 18 AGU Fall 2006 Observations Advanced procedures Modelling, simulation, algorithms, 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

22. 22 of 18 AGU Fall 2006 Observations Features, Coverages & Observations (1) Observations and Features  An observation provides evidence for estimation of a property value for the feature-of-interest Features and Coverages (1)  The value of a property that varies on a feature defines a coverage whose domain is the feature Observations and Coverages (1)  An observation of a property sampled at different times/positions on a feature-of-interest estimates a discrete coverage whose domain is the feature-of-interest  feature-of-interest is one big feature – property value varies within it

23. 23 of 18 AGU Fall 2006 Observations Features, Coverages & Observations (2) Observations and Features  An observation provides evidence for estimation of a property value for the feature-of-interest Features and Coverages (2)  The values of the same property from a set of features constitutes a discrete coverage over a domain defined by the set of features Observations and Coverages (2)  A set of observations of the same property on different features provides an estimate of the range-values of a discrete coverage whose domain is defined by the set of features-of-interest  feature-of-interest is lots of little features – property value constant on each one

24. 24 of 18 AGU Fall 2006 Observations premises: O&M is the high-level information model SOS is the primary information-access interface SOS can serve: an Observation (Feature)  getObservation == “getFeature” (WFS/Obs) operation a feature of interest (Feature)  getFeatureOfInterest == getFeature (WFS) operation or Observation/result (often a time-series == discrete Coverage)  getResult == “getCoverage” (WCS) operation or Sensor == Observation/procedure (SensorML document)  describeSensor == “getFeature” (WFS) or “getRecord” (CSW) operation Sensor service optional – probably required for dynamic sensor use-cases

25. 25 of 18 AGU Fall 2006 Observations SOS vs WFS, WCS, CS/W? WFS/ Obs getFeature, type=Observation WCS getCoverage getCoverage (result) Sensor Registry getRecord SOS getObservation getResult describeSensor getFeatureOfInterest WFS getFeature SOS interface is effectively a composition of (specialised) WFS+WCS+CS/W operations e.g. SOS::getResult == “convenience” interface for WCS

26. 26 of 18 AGU Fall 2006 Observations ISO 19101, 19109 General Feature Model Properties include  attributes  associations between objects  value may be object with identity  operations Metaclass diagram

27. 27 of 18 AGU Fall 2006 Observations ISO 19123 Coverage model

28. 28 of 18 AGU Fall 2006 Observations Discrete coverage model

29. 29 of 18 AGU Fall 2006 Observations Value estimation process: observation An Observation is a kind of “Event Feature type”, whose result is a value estimate, and whose other properties provide metadata concerning the estimation process

30. 30 of 18 AGU Fall 2006 Observations Observation model – Value-capture-centric view An Observation is an Event whose result is an estimate of the value of some Property of the Feature-of-interest, obtained using a specified Procedure

31. 31 of 18 AGU Fall 2006 Observations “Cross-sections” through collections SpecimenAu (ppm) Cu-a (%)Cu-b (%)As (ppm)Sb (ppm) ABC-1231.233.454.230.50.34 A Row gives properties of one feature A Column = variation of a single property across a domain (i.e. set of features) A Cell describes the value of a single property on a feature, often obtained by observation or measurement

32. 32 of 18 AGU Fall 2006 Observations Feature of interest may be any feature type from any domain-model … observations provide values for properties whose values are not asserted i.e. the application-domain supplies the feature types

33. 33 of 18 AGU Fall 2006 Observations Observations support property assignment These must match if the observation is coherent with the feature property Some properties have interesting types …

34. 34 of 18 AGU Fall 2006 Observations Observations and coverages If the property value is not constant across the feature-of- interest  varies by location, in time the corresponding observation result is a coverage individual samples must be tied to the location within the domain, so result is set of e.g.  time-value  position-value  (stationID-value ?) Time-series observations are a particularly common use-case

35. 35 of 18 AGU Fall 2006 Observations Conceptual object model: features Digital object corresponding with identifiable, typed, object in the real world  mountain, road, specimen, event, tract, catchment, wetland, farm, bore, reach, property, license-area, station Feature-type is characterised by a specific set of properties Specimen  ID (name)  description  mass  processing details  sampling location  sampling time  related observation  material ……

36. 36 of 18 AGU Fall 2006 Observations Spatial function: coverage (x 1,y 1 ) (x 2,y 2 ) Variation of a property across the domain of interest  For each element in a spatio-temporal domain, a value from the range can be determined  Used to analyse patterns and anomalies, i.e. to detect features (e.g. storms, fronts, jetstreams) Discrete or continuous domain  Domain is often a grid  Time-series are coverages over time

37. 37 of 18 AGU Fall 2006 Observations Features vs Coverages Feature  object-centric  heterogeneous collection of properties  “summary-view” Coverage  property-centric  variation of homogeneous property  patterns & anomalies Both needed; transformations required

38. 38 of 18 AGU Fall 2006 Observations “Cross-sections” through collections SpecimenAu (ppm) Cu-a (%)Cu-b (%)As (ppm)Sb (ppm) ABC-1231.233.454.230.50.34 A Row gives properties of one feature A Column = variation of a single property across a domain (i.e. set of locations)

39. 39 of 18 AGU Fall 2006 Observations Some feature types only exist to support observations

40. 40 of 18 AGU Fall 2006 Observations 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

41. 41 of 18 AGU Fall 2006 Observations 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

42. 42 of 18 AGU Fall 2006 Observations Science relies on observations Evidence & validation Involves sampling Cross-domain terminology and information-model

43. 43 of 18 AGU Fall 2006 Observations What is “an Observation” Observation act involves a procedure applied at a specific time and place Result of an observation is an estimate of some property value The property is associated with the observation domain or feature of interest The location of the procedure might not be the location of interest for spatial analysis of results