SlideShare a Scribd company logo
1 of 18
OM-JSON
Simon Cox | Research Scientist | Environmental Information Infrastructures
16 September 2015
LAND AND WATER, DATA61
a JSON implementation of O&M
Peter Taylor | Engineer | Autonomous Systems
O&M
OM_Observation
+ phenomenonTime
+ resultTime
+ validTime [0..1]
+ resultQuality [0..*]
+ parameter [0..*]
GF_PropertyType
GFI_Feature
OM_Process Any
+observedProperty
1
0..*
+featureOfInterest 1
0..*
+procedure1 +result
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
OGC - SWE DWG - 2015-09-16
Cox, OGC Abstract Specification – Topic 20: Observations and Measurements 2.0
ISO 19156:2011 Geographic Information – Observations and measurements
OGC - SWE DWG - 2015-09-16
Cox, OGC Implementation – Observations and Measurements - XML Implementation
OGC - SWE DWG - 2015-09-16
Why JSON?
OGC - SWE DWG - 2015-09-16
“So these days, if you want to
interchange tuples or tables of tuples
or numbers and strings, you have
JSON. If you want to do nontrivial
publishing automation, use XML. If
you want to interchange smart bitmaps
of page images, there’s PDF. I
personally think we’re probably done
with inventing low-level textual
interchange formats.”
Tim Bray
http://www.tbray.org/ongoing/When/20
1x/2013/02/10/XML-at-15
OM-JSON
OGC - SWE DWG - 2015-09-16
{
"id":"measure-instance-test",
"type" : "Measurement",
"phenomenonTime": { "instant":"2011-05-11T00:00:00+10:00" },
"observedProperty": {"href":"http://environment.data.gov.au/def/property/air_temperature"},
"procedure": {"href":"http://www.opengis.net/def/waterml/2.0/processType/Sensor"},
"featureOfInterest": {"href":"http://waterml2.csiro.au/rgs-api/v1/monitoring-point/419009/"},
"resultTime": "2011-05-12T09:00:00+10:00",
"result": {
"value": 3.2,
"uom": "http://qudt.org/vocab/unit#DegreeCelsius"
}
}
O&M terminology
JSON syntax
OM-JSON – time-series
OGC - SWE DWG - 2015-09-16
{
"id":"timeseries-instance-test",
"type" : "http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_DiscreteTimeSeriesObservation",
"phenomenonTime": { "begin":"2015-08-03", "end":"2015-08-05" },
"observedProperty": {"href":"http://environment.data.gov.au/def/property/air_temperature"},
"procedure": {"href":"http://www.opengis.net/def/waterml/2.0/processType/Sensor"},
"featureOfInterest": {"href":"http://waterml2.csiro.au/rgs-api/v1/monitoring-point/419009/"},
"resultTime": "2015-08-05T09:00:00+10:00",
"result": {
"defaultPointMetadata": {
"interpolationType": {"href": "http://www.opengis.net/def/waterml/2.0/interpolationType/Continuous"},
"uom" : "http://qudt.org/vocab/unit#DegreeCelsius" },
"points": [
{
"time": { "instant": "2015-08-03" },
"value": 3.2
},
{
"time": { "instant": "2015-08-04" },
"value": 3.5
},
{
"time": { "instant": "2015-08-05" },
"value": 3.6
}
]
}
}
TimeseriesML encoding
{
"id":"polygon-example",
"phenomenonTime": { "instant":"2011-05-11T00:00:00+10:00"},
"observedProperty": { "href":"http://environment.data.gov.au/def/property/shape"},
"procedure": { "href":"http://www.opengis.net/def/waterml/2.0/processType/Sensor"},
"featureOfInterest": { "href":"http://environment.data.gov.au/water/id/catchment/100862", "title":"Observed catchment"},
"resultTime": "2011-05-12T00:00:00+10:00",
"result": {
"type": "Polygon",
"coordinates" : [[
[-180.0, 10.0], [20.0, 90.0], [180.0, -5.0], [-30.0, -90.0], [-180.0, 10.0]
]]
}
}
OM-JSON
OGC - SWE DWG - 2015-09-16
GeoJSON for geometry
{
"id": "spec1",
"type": "Specimen",
"sampledFeature": {
"href": "http://example.org/featureA", "title": "test feature A"
},
"complex": [
{ "rel": "http://example.org/parent", "href": "http://example.org/sample2" },
{ "rel": "http://example.org/child", "href": "http://example.org/sample3" }
],
"samplingTime": { "instant": "2015-05-14" },
"relatedObservation": [
{ "href": "http://example.org/obs99" }
],
"samplingLocation": {
"type": "Point",
"coordinates": [ -30, -90 ]
},
"currentLocation": {
"href": "http://example.org/locations/store", "title": "bottom drawer"
},
"size": {
"value": 23.1,
"uom": "http://www.opengis.net/def/uom/ogc/0/m"
}
}
OM-JSON
OGC - SWE DWG - 2015-09-16
Link model – href, rel, title
{
"id": "sampling feature collection 77",
"sampledFeature": [
{ "href": "http://example.org/feature45", "title": "Derwent River" }
],
"member": [
{ "href": "http://example.org/sample23" },
{
"id": "spec56",
"type": "Specimen",
"samplingTime": { "instant": "2015-05-14" },
"samplingLocation": {
"type": "Point",
"coordinates": [ -30, -90 ]
},
"size": {
"value": 2,
"uom": "l"
}
}
]
}
OM-JSON
OGC - SWE DWG - 2015-09-16
Sampling feature collection
Members
- by-reference or in-line
- inherit common properties
{
"id": "observation collection a34",
"phenomenonTime": { "instant":"2015-05-11" },
"featureOfInterest": { "href":"http://example.org/sighting345" },
"member": [
{ "href": "http://example.org/o23" },
{
"id":"o96",
"type": "CategoryObservation",
"observedProperty": { "href": "http://environment.data.gov.au/def/property/taxon" },
"procedure": { "href":"http://www.opengis.net/def/waterml/2.0/processType/Manual" },
"resultTime": "2015-05-12T09:00:00+10:00",
"result": {
"term": "johnstons-crocodile" ,
"vocabulary": "http://environment.data.gov.au/def/object/"
}
}
]
}
OM-JSON
OGC - SWE DWG - 2015-09-16
Observation collection
Members
- by-reference or in-line
- inherit common properties
JSON Schema for OM-JSON
OGC - SWE DWG - 2015-09-16
Common types, for
use in other OGC
JSON encodings?
JSON-schema for
TimeseriesML
Collection objects,
carry common
properties
No external
dependencies
JSON Schema and validator
• Adapted version of JSON Schema Lint here:
http://waterml2.csiro.au/om-json/
• JSON Schema available here:
http://github.com/peterataylor/om-json
OGC - SWE DWG - 2015-09-16
Issues
This encoding focuses on the information model
How best to wrap these for use in API/Services?
E.g. using SOS (52North), SensorThings, SensorCloud (CSIRO)
Some (minor) differences in approaches to schema inheritance and
typing.
The 52North schema are quite similar and these could be harmonised
OGC - SWE DWG - 2015-09-16
Changes required to Abstract Spec (O&M)
add samplingStrategy to Observation
featureOfInterest used only for the feature with observed property
ObservationCollection, for common properties of member observations
procedure, featureOfInterest, samplingStrategy, observedProperty, phenomenonTime,
resultTime
add samplingElevation to Specimen
vertical offset from a 2-D location a common requirement
SamplingFeatureCollection, for common properties of member sampling features
sampledFeature
OGC - SWE DWG - 2015-09-16
Motion:
The SWE DWG recommends that OM-JSON – OGC 15-100r1 – be
published as an OGC Discussion Paper
Moved: Simon Cox
Seconded: Jeremy Tandy
Thirded: Josh Lieberman
NOTUC
OGC - SWE DWG - 2015-09-16
Discussion points
JSON Schema?
base types for OGC JSON implementations
52North version of JSON for SOS
OM-JSON vs SensorThings
use of GeoJSON for geometry, alternatives
UMLJSON rules
OGC - SWE DWG - 2015-09-16
LAND AND WATER
Thank youCSIRO Land and Water
Simon Cox
Research Scientist
t +61 3 9545 2365
e simon.cox@csiro.au
w people.csiro.au/C/S/Simon-Cox
CSIRO Data61
Peter Taylor
Engineer
t +61 3 6232 5530
e peter.taylor@csiro.au
w http://people.csiro.au/T/P/Peter-Taylor

More Related Content

Viewers also liked

Linked Energy Data Generation
Linked Energy Data GenerationLinked Energy Data Generation
Linked Energy Data GenerationFilip Radulovic
 
The SenseBox project & Internet of Things standardization recommendations for...
The SenseBox project & Internet of Things standardization recommendations for...The SenseBox project & Internet of Things standardization recommendations for...
The SenseBox project & Internet of Things standardization recommendations for...Arne Bröring
 
Rise of the Containers
Rise of the ContainersRise of the Containers
Rise of the Containerssaulius_vl
 
Sensor thingsapi webinar-#3-rest-for-iot-api-20151210
Sensor thingsapi webinar-#3-rest-for-iot-api-20151210Sensor thingsapi webinar-#3-rest-for-iot-api-20151210
Sensor thingsapi webinar-#3-rest-for-iot-api-20151210SensorUp
 
Open IoT Made Easy - Introduction to OGC SensorThings API
Open IoT Made Easy - Introduction to OGC SensorThings APIOpen IoT Made Easy - Introduction to OGC SensorThings API
Open IoT Made Easy - Introduction to OGC SensorThings APISensorUp
 
Raw material c.m.m.a.a.o. pvt. ltd
Raw material c.m.m.a.a.o. pvt. ltdRaw material c.m.m.a.a.o. pvt. ltd
Raw material c.m.m.a.a.o. pvt. ltdvishvasyadav45
 
Directiva sobre la elección del representante docente ante la comisión de pro...
Directiva sobre la elección del representante docente ante la comisión de pro...Directiva sobre la elección del representante docente ante la comisión de pro...
Directiva sobre la elección del representante docente ante la comisión de pro...UGEL RECUAY
 
Toronto Landscape Design
Toronto Landscape DesignToronto Landscape Design
Toronto Landscape Designwilburimler
 
2014 Mercedes-Benz CLA-Class Serving Orange County | Walter's Automotive CA
2014 Mercedes-Benz CLA-Class Serving Orange County | Walter's Automotive CA2014 Mercedes-Benz CLA-Class Serving Orange County | Walter's Automotive CA
2014 Mercedes-Benz CLA-Class Serving Orange County | Walter's Automotive CAWalter's Automotive
 

Viewers also liked (13)

Linked Energy Data Generation
Linked Energy Data GenerationLinked Energy Data Generation
Linked Energy Data Generation
 
The SenseBox project & Internet of Things standardization recommendations for...
The SenseBox project & Internet of Things standardization recommendations for...The SenseBox project & Internet of Things standardization recommendations for...
The SenseBox project & Internet of Things standardization recommendations for...
 
Rise of the Containers
Rise of the ContainersRise of the Containers
Rise of the Containers
 
Sensor thingsapi webinar-#3-rest-for-iot-api-20151210
Sensor thingsapi webinar-#3-rest-for-iot-api-20151210Sensor thingsapi webinar-#3-rest-for-iot-api-20151210
Sensor thingsapi webinar-#3-rest-for-iot-api-20151210
 
Open IoT Made Easy - Introduction to OGC SensorThings API
Open IoT Made Easy - Introduction to OGC SensorThings APIOpen IoT Made Easy - Introduction to OGC SensorThings API
Open IoT Made Easy - Introduction to OGC SensorThings API
 
Aep prospective engl
Aep prospective englAep prospective engl
Aep prospective engl
 
Raw material c.m.m.a.a.o. pvt. ltd
Raw material c.m.m.a.a.o. pvt. ltdRaw material c.m.m.a.a.o. pvt. ltd
Raw material c.m.m.a.a.o. pvt. ltd
 
Directiva sobre la elección del representante docente ante la comisión de pro...
Directiva sobre la elección del representante docente ante la comisión de pro...Directiva sobre la elección del representante docente ante la comisión de pro...
Directiva sobre la elección del representante docente ante la comisión de pro...
 
Toronto Landscape Design
Toronto Landscape DesignToronto Landscape Design
Toronto Landscape Design
 
2014 Mercedes-Benz CLA-Class Serving Orange County | Walter's Automotive CA
2014 Mercedes-Benz CLA-Class Serving Orange County | Walter's Automotive CA2014 Mercedes-Benz CLA-Class Serving Orange County | Walter's Automotive CA
2014 Mercedes-Benz CLA-Class Serving Orange County | Walter's Automotive CA
 
Tuberculosis
TuberculosisTuberculosis
Tuberculosis
 
学校Air
学校Air学校Air
学校Air
 
Resume template 13
Resume template 13Resume template 13
Resume template 13
 

Similar to OM-JSON implementation of O&M in JSON format

OGC Sensor Web Enablement SOS 2.0, SensorML and WaterML
OGC Sensor Web Enablement  SOS 2.0, SensorML and WaterMLOGC Sensor Web Enablement  SOS 2.0, SensorML and WaterML
OGC Sensor Web Enablement SOS 2.0, SensorML and WaterMLLuis Bermudez
 
SensorWeb SOS Pilot RIVM/Geonovum - Status
SensorWeb SOS Pilot RIVM/Geonovum - StatusSensorWeb SOS Pilot RIVM/Geonovum - Status
SensorWeb SOS Pilot RIVM/Geonovum - StatusJust van den Broecke
 
GeoServer for Spatio-temporal Data Handling With Examples For MetOc And Remot...
GeoServer for Spatio-temporal Data Handling With Examples For MetOc And Remot...GeoServer for Spatio-temporal Data Handling With Examples For MetOc And Remot...
GeoServer for Spatio-temporal Data Handling With Examples For MetOc And Remot...GeoSolutions
 
Geospatial Temporal Open Standards for Big Data from Space (BiDS2014)
Geospatial Temporal Open Standards for Big Data from Space (BiDS2014)Geospatial Temporal Open Standards for Big Data from Space (BiDS2014)
Geospatial Temporal Open Standards for Big Data from Space (BiDS2014)George Percivall
 
Comparison between OGC Sensor Observation Service and SensorThings API
Comparison between OGC Sensor Observation Service and SensorThings APIComparison between OGC Sensor Observation Service and SensorThings API
Comparison between OGC Sensor Observation Service and SensorThings APISensorUp
 
Optimizely Agent: Scaling Resilient Feature Delivery
Optimizely Agent: Scaling Resilient Feature DeliveryOptimizely Agent: Scaling Resilient Feature Delivery
Optimizely Agent: Scaling Resilient Feature DeliveryOptimizely
 
Stream Processing in SmartNews #jawsdays
Stream Processing in SmartNews #jawsdaysStream Processing in SmartNews #jawsdays
Stream Processing in SmartNews #jawsdaysSmartNews, Inc.
 
GeoPackage, OWS Context and the OGC Interoperability Program
GeoPackage, OWS Context and the OGC Interoperability ProgramGeoPackage, OWS Context and the OGC Interoperability Program
GeoPackage, OWS Context and the OGC Interoperability ProgramRaj Singh
 
A standard for geospatial observations and measurements
A standard for geospatial observations and measurementsA standard for geospatial observations and measurements
A standard for geospatial observations and measurementsSimon Cox
 
Paris Datageeks meetup 05102016
Paris Datageeks meetup 05102016Paris Datageeks meetup 05102016
Paris Datageeks meetup 05102016Michel Caradec
 
Pitfalls in alignment of observation models resolved using PROV as an upper o...
Pitfalls in alignment of observation models resolved using PROV as an upper o...Pitfalls in alignment of observation models resolved using PROV as an upper o...
Pitfalls in alignment of observation models resolved using PROV as an upper o...Simon Cox
 
Cloud-native application monitoring powered by Riverbed and Elasticsearch
Cloud-native application monitoring powered by Riverbed and ElasticsearchCloud-native application monitoring powered by Riverbed and Elasticsearch
Cloud-native application monitoring powered by Riverbed and ElasticsearchRichard Juknavorian
 
Spatio-temporal Data Handling With GeoServer for MetOc And Remote Sensing
Spatio-temporal Data Handling With GeoServer for MetOc And Remote SensingSpatio-temporal Data Handling With GeoServer for MetOc And Remote Sensing
Spatio-temporal Data Handling With GeoServer for MetOc And Remote SensingGeoSolutions
 
Sherlock: an anomaly detection service on top of Druid
Sherlock: an anomaly detection service on top of Druid Sherlock: an anomaly detection service on top of Druid
Sherlock: an anomaly detection service on top of Druid DataWorks Summit
 
Scaling Experimentation & Data Capture at Grab
Scaling Experimentation & Data Capture at GrabScaling Experimentation & Data Capture at Grab
Scaling Experimentation & Data Capture at GrabRoman
 
YOW2018 Cloud Performance Root Cause Analysis at Netflix
YOW2018 Cloud Performance Root Cause Analysis at NetflixYOW2018 Cloud Performance Root Cause Analysis at Netflix
YOW2018 Cloud Performance Root Cause Analysis at NetflixBrendan Gregg
 
eBay Pulsar: Real-time analytics platform
eBay Pulsar: Real-time analytics platformeBay Pulsar: Real-time analytics platform
eBay Pulsar: Real-time analytics platformKyoungMo Yang
 
Webinar september 2013
Webinar september 2013Webinar september 2013
Webinar september 2013Marc Gille
 
OSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at Runtime
OSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at RuntimeOSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at Runtime
OSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at RuntimeNETWAYS
 

Similar to OM-JSON implementation of O&M in JSON format (20)

OGC Sensor Web Enablement SOS 2.0, SensorML and WaterML
OGC Sensor Web Enablement  SOS 2.0, SensorML and WaterMLOGC Sensor Web Enablement  SOS 2.0, SensorML and WaterML
OGC Sensor Web Enablement SOS 2.0, SensorML and WaterML
 
SensorWeb SOS Pilot RIVM/Geonovum - Status
SensorWeb SOS Pilot RIVM/Geonovum - StatusSensorWeb SOS Pilot RIVM/Geonovum - Status
SensorWeb SOS Pilot RIVM/Geonovum - Status
 
GeoServer for Spatio-temporal Data Handling With Examples For MetOc And Remot...
GeoServer for Spatio-temporal Data Handling With Examples For MetOc And Remot...GeoServer for Spatio-temporal Data Handling With Examples For MetOc And Remot...
GeoServer for Spatio-temporal Data Handling With Examples For MetOc And Remot...
 
Geospatial Temporal Open Standards for Big Data from Space (BiDS2014)
Geospatial Temporal Open Standards for Big Data from Space (BiDS2014)Geospatial Temporal Open Standards for Big Data from Space (BiDS2014)
Geospatial Temporal Open Standards for Big Data from Space (BiDS2014)
 
Comparison between OGC Sensor Observation Service and SensorThings API
Comparison between OGC Sensor Observation Service and SensorThings APIComparison between OGC Sensor Observation Service and SensorThings API
Comparison between OGC Sensor Observation Service and SensorThings API
 
Optimizely Agent: Scaling Resilient Feature Delivery
Optimizely Agent: Scaling Resilient Feature DeliveryOptimizely Agent: Scaling Resilient Feature Delivery
Optimizely Agent: Scaling Resilient Feature Delivery
 
Stream Processing in SmartNews #jawsdays
Stream Processing in SmartNews #jawsdaysStream Processing in SmartNews #jawsdays
Stream Processing in SmartNews #jawsdays
 
GeoPackage, OWS Context and the OGC Interoperability Program
GeoPackage, OWS Context and the OGC Interoperability ProgramGeoPackage, OWS Context and the OGC Interoperability Program
GeoPackage, OWS Context and the OGC Interoperability Program
 
A standard for geospatial observations and measurements
A standard for geospatial observations and measurementsA standard for geospatial observations and measurements
A standard for geospatial observations and measurements
 
Paris Datageeks meetup 05102016
Paris Datageeks meetup 05102016Paris Datageeks meetup 05102016
Paris Datageeks meetup 05102016
 
SLALOM Project Technical Webinar 20151111
SLALOM Project Technical Webinar 20151111 SLALOM Project Technical Webinar 20151111
SLALOM Project Technical Webinar 20151111
 
Pitfalls in alignment of observation models resolved using PROV as an upper o...
Pitfalls in alignment of observation models resolved using PROV as an upper o...Pitfalls in alignment of observation models resolved using PROV as an upper o...
Pitfalls in alignment of observation models resolved using PROV as an upper o...
 
Cloud-native application monitoring powered by Riverbed and Elasticsearch
Cloud-native application monitoring powered by Riverbed and ElasticsearchCloud-native application monitoring powered by Riverbed and Elasticsearch
Cloud-native application monitoring powered by Riverbed and Elasticsearch
 
Spatio-temporal Data Handling With GeoServer for MetOc And Remote Sensing
Spatio-temporal Data Handling With GeoServer for MetOc And Remote SensingSpatio-temporal Data Handling With GeoServer for MetOc And Remote Sensing
Spatio-temporal Data Handling With GeoServer for MetOc And Remote Sensing
 
Sherlock: an anomaly detection service on top of Druid
Sherlock: an anomaly detection service on top of Druid Sherlock: an anomaly detection service on top of Druid
Sherlock: an anomaly detection service on top of Druid
 
Scaling Experimentation & Data Capture at Grab
Scaling Experimentation & Data Capture at GrabScaling Experimentation & Data Capture at Grab
Scaling Experimentation & Data Capture at Grab
 
YOW2018 Cloud Performance Root Cause Analysis at Netflix
YOW2018 Cloud Performance Root Cause Analysis at NetflixYOW2018 Cloud Performance Root Cause Analysis at Netflix
YOW2018 Cloud Performance Root Cause Analysis at Netflix
 
eBay Pulsar: Real-time analytics platform
eBay Pulsar: Real-time analytics platformeBay Pulsar: Real-time analytics platform
eBay Pulsar: Real-time analytics platform
 
Webinar september 2013
Webinar september 2013Webinar september 2013
Webinar september 2013
 
OSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at Runtime
OSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at RuntimeOSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at Runtime
OSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at Runtime
 

More from Simon Cox

Cross-domain data discovery and integration
Cross-domain data discovery and integration Cross-domain data discovery and integration
Cross-domain data discovery and integration Simon Cox
 
The SOSA ontology
The SOSA ontologyThe SOSA ontology
The SOSA ontologySimon Cox
 
Web standards support science data
Web standards support science dataWeb standards support science data
Web standards support science dataSimon Cox
 
Vocabularies, ontologies, standards for observations: developments from RDA, ...
Vocabularies, ontologies, standards for observations: developments from RDA, ...Vocabularies, ontologies, standards for observations: developments from RDA, ...
Vocabularies, ontologies, standards for observations: developments from RDA, ...Simon Cox
 
A common model for scientific observations and samples
A common model for scientific observations and samplesA common model for scientific observations and samples
A common model for scientific observations and samplesSimon Cox
 
OWL-Time and enhancements
OWL-Time and enhancementsOWL-Time and enhancements
OWL-Time and enhancementsSimon Cox
 
O&M Specimen model – alignments with PROV, BCO
O&M Specimen model – alignments with PROV, BCOO&M Specimen model – alignments with PROV, BCO
O&M Specimen model – alignments with PROV, BCOSimon Cox
 
Ontology alignment – is PROV-O good enough?
Ontology alignment – is PROV-O good enough?Ontology alignment – is PROV-O good enough?
Ontology alignment – is PROV-O good enough?Simon Cox
 
Re-use of standard ontologies in a water quality vocabulary
Re-use of standard ontologies in a water quality vocabularyRe-use of standard ontologies in a water quality vocabulary
Re-use of standard ontologies in a water quality vocabularySimon Cox
 
Observations to Information
Observations to InformationObservations to Information
Observations to InformationSimon Cox
 
A harmonized vocabulary for water quality
A harmonized vocabulary for water qualityA harmonized vocabulary for water quality
A harmonized vocabulary for water qualitySimon Cox
 
Harmonization of vocabularies for water data
Harmonization of vocabularies for water dataHarmonization of vocabularies for water data
Harmonization of vocabularies for water dataSimon Cox
 
Some problems with standard geospatial metadata
Some problems with standard geospatial metadataSome problems with standard geospatial metadata
Some problems with standard geospatial metadataSimon Cox
 
Information Viewpoints and Geoscience Service Architectures
Information Viewpoints and Geoscience Service Architectures Information Viewpoints and Geoscience Service Architectures
Information Viewpoints and Geoscience Service Architectures Simon Cox
 
Leverage and Delegation in Developing an Information Model for Geology
Leverage and Delegation in Developing an Information Model for Geology Leverage and Delegation in Developing an Information Model for Geology
Leverage and Delegation in Developing an Information Model for Geology Simon Cox
 
Technologies and practices for maintaining and publishing earth science vocab...
Technologies and practices for maintaining and publishing earth science vocab...Technologies and practices for maintaining and publishing earth science vocab...
Technologies and practices for maintaining and publishing earth science vocab...Simon Cox
 
Developing and publishing vocabularies
Developing and publishing vocabulariesDeveloping and publishing vocabularies
Developing and publishing vocabulariesSimon Cox
 

More from Simon Cox (17)

Cross-domain data discovery and integration
Cross-domain data discovery and integration Cross-domain data discovery and integration
Cross-domain data discovery and integration
 
The SOSA ontology
The SOSA ontologyThe SOSA ontology
The SOSA ontology
 
Web standards support science data
Web standards support science dataWeb standards support science data
Web standards support science data
 
Vocabularies, ontologies, standards for observations: developments from RDA, ...
Vocabularies, ontologies, standards for observations: developments from RDA, ...Vocabularies, ontologies, standards for observations: developments from RDA, ...
Vocabularies, ontologies, standards for observations: developments from RDA, ...
 
A common model for scientific observations and samples
A common model for scientific observations and samplesA common model for scientific observations and samples
A common model for scientific observations and samples
 
OWL-Time and enhancements
OWL-Time and enhancementsOWL-Time and enhancements
OWL-Time and enhancements
 
O&M Specimen model – alignments with PROV, BCO
O&M Specimen model – alignments with PROV, BCOO&M Specimen model – alignments with PROV, BCO
O&M Specimen model – alignments with PROV, BCO
 
Ontology alignment – is PROV-O good enough?
Ontology alignment – is PROV-O good enough?Ontology alignment – is PROV-O good enough?
Ontology alignment – is PROV-O good enough?
 
Re-use of standard ontologies in a water quality vocabulary
Re-use of standard ontologies in a water quality vocabularyRe-use of standard ontologies in a water quality vocabulary
Re-use of standard ontologies in a water quality vocabulary
 
Observations to Information
Observations to InformationObservations to Information
Observations to Information
 
A harmonized vocabulary for water quality
A harmonized vocabulary for water qualityA harmonized vocabulary for water quality
A harmonized vocabulary for water quality
 
Harmonization of vocabularies for water data
Harmonization of vocabularies for water dataHarmonization of vocabularies for water data
Harmonization of vocabularies for water data
 
Some problems with standard geospatial metadata
Some problems with standard geospatial metadataSome problems with standard geospatial metadata
Some problems with standard geospatial metadata
 
Information Viewpoints and Geoscience Service Architectures
Information Viewpoints and Geoscience Service Architectures Information Viewpoints and Geoscience Service Architectures
Information Viewpoints and Geoscience Service Architectures
 
Leverage and Delegation in Developing an Information Model for Geology
Leverage and Delegation in Developing an Information Model for Geology Leverage and Delegation in Developing an Information Model for Geology
Leverage and Delegation in Developing an Information Model for Geology
 
Technologies and practices for maintaining and publishing earth science vocab...
Technologies and practices for maintaining and publishing earth science vocab...Technologies and practices for maintaining and publishing earth science vocab...
Technologies and practices for maintaining and publishing earth science vocab...
 
Developing and publishing vocabularies
Developing and publishing vocabulariesDeveloping and publishing vocabularies
Developing and publishing vocabularies
 

Recently uploaded

Potsdam FH学位证,波茨坦应用技术大学毕业证书1:1制作
Potsdam FH学位证,波茨坦应用技术大学毕业证书1:1制作Potsdam FH学位证,波茨坦应用技术大学毕业证书1:1制作
Potsdam FH学位证,波茨坦应用技术大学毕业证书1:1制作ys8omjxb
 
Russian Call Girls in Kolkata Samaira 🤌 8250192130 🚀 Vip Call Girls Kolkata
Russian Call Girls in Kolkata Samaira 🤌  8250192130 🚀 Vip Call Girls KolkataRussian Call Girls in Kolkata Samaira 🤌  8250192130 🚀 Vip Call Girls Kolkata
Russian Call Girls in Kolkata Samaira 🤌 8250192130 🚀 Vip Call Girls Kolkataanamikaraghav4
 
VIP Kolkata Call Girl Salt Lake 👉 8250192130 Available With Room
VIP Kolkata Call Girl Salt Lake 👉 8250192130  Available With RoomVIP Kolkata Call Girl Salt Lake 👉 8250192130  Available With Room
VIP Kolkata Call Girl Salt Lake 👉 8250192130 Available With Roomishabajaj13
 
Contact Rya Baby for Call Girls New Delhi
Contact Rya Baby for Call Girls New DelhiContact Rya Baby for Call Girls New Delhi
Contact Rya Baby for Call Girls New Delhimiss dipika
 
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一Fs
 
The Intriguing World of CDR Analysis by Police: What You Need to Know.pdf
The Intriguing World of CDR Analysis by Police: What You Need to Know.pdfThe Intriguing World of CDR Analysis by Police: What You Need to Know.pdf
The Intriguing World of CDR Analysis by Police: What You Need to Know.pdfMilind Agarwal
 
Low Rate Call Girls Kolkata Avani 🤌 8250192130 🚀 Vip Call Girls Kolkata
Low Rate Call Girls Kolkata Avani 🤌  8250192130 🚀 Vip Call Girls KolkataLow Rate Call Girls Kolkata Avani 🤌  8250192130 🚀 Vip Call Girls Kolkata
Low Rate Call Girls Kolkata Avani 🤌 8250192130 🚀 Vip Call Girls Kolkataanamikaraghav4
 
定制(Management毕业证书)新加坡管理大学毕业证成绩单原版一比一
定制(Management毕业证书)新加坡管理大学毕业证成绩单原版一比一定制(Management毕业证书)新加坡管理大学毕业证成绩单原版一比一
定制(Management毕业证书)新加坡管理大学毕业证成绩单原版一比一Fs
 
Call Girls in Uttam Nagar Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Uttam Nagar Delhi 💯Call Us 🔝8264348440🔝Call Girls in Uttam Nagar Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Uttam Nagar Delhi 💯Call Us 🔝8264348440🔝soniya singh
 
VIP Kolkata Call Girl Alambazar 👉 8250192130 Available With Room
VIP Kolkata Call Girl Alambazar 👉 8250192130  Available With RoomVIP Kolkata Call Girl Alambazar 👉 8250192130  Available With Room
VIP Kolkata Call Girl Alambazar 👉 8250192130 Available With Roomdivyansh0kumar0
 
Complet Documnetation for Smart Assistant Application for Disabled Person
Complet Documnetation   for Smart Assistant Application for Disabled PersonComplet Documnetation   for Smart Assistant Application for Disabled Person
Complet Documnetation for Smart Assistant Application for Disabled Personfurqan222004
 
Call Girls In Mumbai Central Mumbai ❤️ 9920874524 👈 Cash on Delivery
Call Girls In Mumbai Central Mumbai ❤️ 9920874524 👈 Cash on DeliveryCall Girls In Mumbai Central Mumbai ❤️ 9920874524 👈 Cash on Delivery
Call Girls In Mumbai Central Mumbai ❤️ 9920874524 👈 Cash on Deliverybabeytanya
 
定制(CC毕业证书)美国美国社区大学毕业证成绩单原版一比一
定制(CC毕业证书)美国美国社区大学毕业证成绩单原版一比一定制(CC毕业证书)美国美国社区大学毕业证成绩单原版一比一
定制(CC毕业证书)美国美国社区大学毕业证成绩单原版一比一3sw2qly1
 
定制(AUT毕业证书)新西兰奥克兰理工大学毕业证成绩单原版一比一
定制(AUT毕业证书)新西兰奥克兰理工大学毕业证成绩单原版一比一定制(AUT毕业证书)新西兰奥克兰理工大学毕业证成绩单原版一比一
定制(AUT毕业证书)新西兰奥克兰理工大学毕业证成绩单原版一比一Fs
 
VIP Call Girls Kolkata Ananya 🤌 8250192130 🚀 Vip Call Girls Kolkata
VIP Call Girls Kolkata Ananya 🤌  8250192130 🚀 Vip Call Girls KolkataVIP Call Girls Kolkata Ananya 🤌  8250192130 🚀 Vip Call Girls Kolkata
VIP Call Girls Kolkata Ananya 🤌 8250192130 🚀 Vip Call Girls Kolkataanamikaraghav4
 
Magic exist by Marta Loveguard - presentation.pptx
Magic exist by Marta Loveguard - presentation.pptxMagic exist by Marta Loveguard - presentation.pptx
Magic exist by Marta Loveguard - presentation.pptxMartaLoveguard
 
VIP Kolkata Call Girl Kestopur 👉 8250192130 Available With Room
VIP Kolkata Call Girl Kestopur 👉 8250192130  Available With RoomVIP Kolkata Call Girl Kestopur 👉 8250192130  Available With Room
VIP Kolkata Call Girl Kestopur 👉 8250192130 Available With Roomdivyansh0kumar0
 
AlbaniaDreamin24 - How to easily use an API with Flows
AlbaniaDreamin24 - How to easily use an API with FlowsAlbaniaDreamin24 - How to easily use an API with Flows
AlbaniaDreamin24 - How to easily use an API with FlowsThierry TROUIN ☁
 

Recently uploaded (20)

Potsdam FH学位证,波茨坦应用技术大学毕业证书1:1制作
Potsdam FH学位证,波茨坦应用技术大学毕业证书1:1制作Potsdam FH学位证,波茨坦应用技术大学毕业证书1:1制作
Potsdam FH学位证,波茨坦应用技术大学毕业证书1:1制作
 
Hot Sexy call girls in Rk Puram 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in  Rk Puram 🔝 9953056974 🔝 Delhi escort ServiceHot Sexy call girls in  Rk Puram 🔝 9953056974 🔝 Delhi escort Service
Hot Sexy call girls in Rk Puram 🔝 9953056974 🔝 Delhi escort Service
 
Russian Call Girls in Kolkata Samaira 🤌 8250192130 🚀 Vip Call Girls Kolkata
Russian Call Girls in Kolkata Samaira 🤌  8250192130 🚀 Vip Call Girls KolkataRussian Call Girls in Kolkata Samaira 🤌  8250192130 🚀 Vip Call Girls Kolkata
Russian Call Girls in Kolkata Samaira 🤌 8250192130 🚀 Vip Call Girls Kolkata
 
VIP Kolkata Call Girl Salt Lake 👉 8250192130 Available With Room
VIP Kolkata Call Girl Salt Lake 👉 8250192130  Available With RoomVIP Kolkata Call Girl Salt Lake 👉 8250192130  Available With Room
VIP Kolkata Call Girl Salt Lake 👉 8250192130 Available With Room
 
Contact Rya Baby for Call Girls New Delhi
Contact Rya Baby for Call Girls New DelhiContact Rya Baby for Call Girls New Delhi
Contact Rya Baby for Call Girls New Delhi
 
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
定制(Lincoln毕业证书)新西兰林肯大学毕业证成绩单原版一比一
 
The Intriguing World of CDR Analysis by Police: What You Need to Know.pdf
The Intriguing World of CDR Analysis by Police: What You Need to Know.pdfThe Intriguing World of CDR Analysis by Police: What You Need to Know.pdf
The Intriguing World of CDR Analysis by Police: What You Need to Know.pdf
 
Low Rate Call Girls Kolkata Avani 🤌 8250192130 🚀 Vip Call Girls Kolkata
Low Rate Call Girls Kolkata Avani 🤌  8250192130 🚀 Vip Call Girls KolkataLow Rate Call Girls Kolkata Avani 🤌  8250192130 🚀 Vip Call Girls Kolkata
Low Rate Call Girls Kolkata Avani 🤌 8250192130 🚀 Vip Call Girls Kolkata
 
定制(Management毕业证书)新加坡管理大学毕业证成绩单原版一比一
定制(Management毕业证书)新加坡管理大学毕业证成绩单原版一比一定制(Management毕业证书)新加坡管理大学毕业证成绩单原版一比一
定制(Management毕业证书)新加坡管理大学毕业证成绩单原版一比一
 
Call Girls in Uttam Nagar Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Uttam Nagar Delhi 💯Call Us 🔝8264348440🔝Call Girls in Uttam Nagar Delhi 💯Call Us 🔝8264348440🔝
Call Girls in Uttam Nagar Delhi 💯Call Us 🔝8264348440🔝
 
VIP Kolkata Call Girl Alambazar 👉 8250192130 Available With Room
VIP Kolkata Call Girl Alambazar 👉 8250192130  Available With RoomVIP Kolkata Call Girl Alambazar 👉 8250192130  Available With Room
VIP Kolkata Call Girl Alambazar 👉 8250192130 Available With Room
 
Complet Documnetation for Smart Assistant Application for Disabled Person
Complet Documnetation   for Smart Assistant Application for Disabled PersonComplet Documnetation   for Smart Assistant Application for Disabled Person
Complet Documnetation for Smart Assistant Application for Disabled Person
 
Call Girls In Mumbai Central Mumbai ❤️ 9920874524 👈 Cash on Delivery
Call Girls In Mumbai Central Mumbai ❤️ 9920874524 👈 Cash on DeliveryCall Girls In Mumbai Central Mumbai ❤️ 9920874524 👈 Cash on Delivery
Call Girls In Mumbai Central Mumbai ❤️ 9920874524 👈 Cash on Delivery
 
Model Call Girl in Jamuna Vihar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in  Jamuna Vihar Delhi reach out to us at 🔝9953056974🔝Model Call Girl in  Jamuna Vihar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Jamuna Vihar Delhi reach out to us at 🔝9953056974🔝
 
定制(CC毕业证书)美国美国社区大学毕业证成绩单原版一比一
定制(CC毕业证书)美国美国社区大学毕业证成绩单原版一比一定制(CC毕业证书)美国美国社区大学毕业证成绩单原版一比一
定制(CC毕业证书)美国美国社区大学毕业证成绩单原版一比一
 
定制(AUT毕业证书)新西兰奥克兰理工大学毕业证成绩单原版一比一
定制(AUT毕业证书)新西兰奥克兰理工大学毕业证成绩单原版一比一定制(AUT毕业证书)新西兰奥克兰理工大学毕业证成绩单原版一比一
定制(AUT毕业证书)新西兰奥克兰理工大学毕业证成绩单原版一比一
 
VIP Call Girls Kolkata Ananya 🤌 8250192130 🚀 Vip Call Girls Kolkata
VIP Call Girls Kolkata Ananya 🤌  8250192130 🚀 Vip Call Girls KolkataVIP Call Girls Kolkata Ananya 🤌  8250192130 🚀 Vip Call Girls Kolkata
VIP Call Girls Kolkata Ananya 🤌 8250192130 🚀 Vip Call Girls Kolkata
 
Magic exist by Marta Loveguard - presentation.pptx
Magic exist by Marta Loveguard - presentation.pptxMagic exist by Marta Loveguard - presentation.pptx
Magic exist by Marta Loveguard - presentation.pptx
 
VIP Kolkata Call Girl Kestopur 👉 8250192130 Available With Room
VIP Kolkata Call Girl Kestopur 👉 8250192130  Available With RoomVIP Kolkata Call Girl Kestopur 👉 8250192130  Available With Room
VIP Kolkata Call Girl Kestopur 👉 8250192130 Available With Room
 
AlbaniaDreamin24 - How to easily use an API with Flows
AlbaniaDreamin24 - How to easily use an API with FlowsAlbaniaDreamin24 - How to easily use an API with Flows
AlbaniaDreamin24 - How to easily use an API with Flows
 

OM-JSON implementation of O&M in JSON format

  • 1. OM-JSON Simon Cox | Research Scientist | Environmental Information Infrastructures 16 September 2015 LAND AND WATER, DATA61 a JSON implementation of O&M Peter Taylor | Engineer | Autonomous Systems
  • 2. O&M OM_Observation + phenomenonTime + resultTime + validTime [0..1] + resultQuality [0..*] + parameter [0..*] GF_PropertyType GFI_Feature OM_Process Any +observedProperty 1 0..* +featureOfInterest 1 0..* +procedure1 +result 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 OGC - SWE DWG - 2015-09-16 Cox, OGC Abstract Specification – Topic 20: Observations and Measurements 2.0 ISO 19156:2011 Geographic Information – Observations and measurements
  • 3. OGC - SWE DWG - 2015-09-16 Cox, OGC Implementation – Observations and Measurements - XML Implementation
  • 4. OGC - SWE DWG - 2015-09-16
  • 5. Why JSON? OGC - SWE DWG - 2015-09-16 “So these days, if you want to interchange tuples or tables of tuples or numbers and strings, you have JSON. If you want to do nontrivial publishing automation, use XML. If you want to interchange smart bitmaps of page images, there’s PDF. I personally think we’re probably done with inventing low-level textual interchange formats.” Tim Bray http://www.tbray.org/ongoing/When/20 1x/2013/02/10/XML-at-15
  • 6. OM-JSON OGC - SWE DWG - 2015-09-16 { "id":"measure-instance-test", "type" : "Measurement", "phenomenonTime": { "instant":"2011-05-11T00:00:00+10:00" }, "observedProperty": {"href":"http://environment.data.gov.au/def/property/air_temperature"}, "procedure": {"href":"http://www.opengis.net/def/waterml/2.0/processType/Sensor"}, "featureOfInterest": {"href":"http://waterml2.csiro.au/rgs-api/v1/monitoring-point/419009/"}, "resultTime": "2011-05-12T09:00:00+10:00", "result": { "value": 3.2, "uom": "http://qudt.org/vocab/unit#DegreeCelsius" } } O&M terminology JSON syntax
  • 7. OM-JSON – time-series OGC - SWE DWG - 2015-09-16 { "id":"timeseries-instance-test", "type" : "http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_DiscreteTimeSeriesObservation", "phenomenonTime": { "begin":"2015-08-03", "end":"2015-08-05" }, "observedProperty": {"href":"http://environment.data.gov.au/def/property/air_temperature"}, "procedure": {"href":"http://www.opengis.net/def/waterml/2.0/processType/Sensor"}, "featureOfInterest": {"href":"http://waterml2.csiro.au/rgs-api/v1/monitoring-point/419009/"}, "resultTime": "2015-08-05T09:00:00+10:00", "result": { "defaultPointMetadata": { "interpolationType": {"href": "http://www.opengis.net/def/waterml/2.0/interpolationType/Continuous"}, "uom" : "http://qudt.org/vocab/unit#DegreeCelsius" }, "points": [ { "time": { "instant": "2015-08-03" }, "value": 3.2 }, { "time": { "instant": "2015-08-04" }, "value": 3.5 }, { "time": { "instant": "2015-08-05" }, "value": 3.6 } ] } } TimeseriesML encoding
  • 8. { "id":"polygon-example", "phenomenonTime": { "instant":"2011-05-11T00:00:00+10:00"}, "observedProperty": { "href":"http://environment.data.gov.au/def/property/shape"}, "procedure": { "href":"http://www.opengis.net/def/waterml/2.0/processType/Sensor"}, "featureOfInterest": { "href":"http://environment.data.gov.au/water/id/catchment/100862", "title":"Observed catchment"}, "resultTime": "2011-05-12T00:00:00+10:00", "result": { "type": "Polygon", "coordinates" : [[ [-180.0, 10.0], [20.0, 90.0], [180.0, -5.0], [-30.0, -90.0], [-180.0, 10.0] ]] } } OM-JSON OGC - SWE DWG - 2015-09-16 GeoJSON for geometry
  • 9. { "id": "spec1", "type": "Specimen", "sampledFeature": { "href": "http://example.org/featureA", "title": "test feature A" }, "complex": [ { "rel": "http://example.org/parent", "href": "http://example.org/sample2" }, { "rel": "http://example.org/child", "href": "http://example.org/sample3" } ], "samplingTime": { "instant": "2015-05-14" }, "relatedObservation": [ { "href": "http://example.org/obs99" } ], "samplingLocation": { "type": "Point", "coordinates": [ -30, -90 ] }, "currentLocation": { "href": "http://example.org/locations/store", "title": "bottom drawer" }, "size": { "value": 23.1, "uom": "http://www.opengis.net/def/uom/ogc/0/m" } } OM-JSON OGC - SWE DWG - 2015-09-16 Link model – href, rel, title
  • 10. { "id": "sampling feature collection 77", "sampledFeature": [ { "href": "http://example.org/feature45", "title": "Derwent River" } ], "member": [ { "href": "http://example.org/sample23" }, { "id": "spec56", "type": "Specimen", "samplingTime": { "instant": "2015-05-14" }, "samplingLocation": { "type": "Point", "coordinates": [ -30, -90 ] }, "size": { "value": 2, "uom": "l" } } ] } OM-JSON OGC - SWE DWG - 2015-09-16 Sampling feature collection Members - by-reference or in-line - inherit common properties
  • 11. { "id": "observation collection a34", "phenomenonTime": { "instant":"2015-05-11" }, "featureOfInterest": { "href":"http://example.org/sighting345" }, "member": [ { "href": "http://example.org/o23" }, { "id":"o96", "type": "CategoryObservation", "observedProperty": { "href": "http://environment.data.gov.au/def/property/taxon" }, "procedure": { "href":"http://www.opengis.net/def/waterml/2.0/processType/Manual" }, "resultTime": "2015-05-12T09:00:00+10:00", "result": { "term": "johnstons-crocodile" , "vocabulary": "http://environment.data.gov.au/def/object/" } } ] } OM-JSON OGC - SWE DWG - 2015-09-16 Observation collection Members - by-reference or in-line - inherit common properties
  • 12. JSON Schema for OM-JSON OGC - SWE DWG - 2015-09-16 Common types, for use in other OGC JSON encodings? JSON-schema for TimeseriesML Collection objects, carry common properties No external dependencies
  • 13. JSON Schema and validator • Adapted version of JSON Schema Lint here: http://waterml2.csiro.au/om-json/ • JSON Schema available here: http://github.com/peterataylor/om-json OGC - SWE DWG - 2015-09-16
  • 14. Issues This encoding focuses on the information model How best to wrap these for use in API/Services? E.g. using SOS (52North), SensorThings, SensorCloud (CSIRO) Some (minor) differences in approaches to schema inheritance and typing. The 52North schema are quite similar and these could be harmonised OGC - SWE DWG - 2015-09-16
  • 15. Changes required to Abstract Spec (O&M) add samplingStrategy to Observation featureOfInterest used only for the feature with observed property ObservationCollection, for common properties of member observations procedure, featureOfInterest, samplingStrategy, observedProperty, phenomenonTime, resultTime add samplingElevation to Specimen vertical offset from a 2-D location a common requirement SamplingFeatureCollection, for common properties of member sampling features sampledFeature OGC - SWE DWG - 2015-09-16
  • 16. Motion: The SWE DWG recommends that OM-JSON – OGC 15-100r1 – be published as an OGC Discussion Paper Moved: Simon Cox Seconded: Jeremy Tandy Thirded: Josh Lieberman NOTUC OGC - SWE DWG - 2015-09-16
  • 17. Discussion points JSON Schema? base types for OGC JSON implementations 52North version of JSON for SOS OM-JSON vs SensorThings use of GeoJSON for geometry, alternatives UMLJSON rules OGC - SWE DWG - 2015-09-16
  • 18. LAND AND WATER Thank youCSIRO Land and Water Simon Cox Research Scientist t +61 3 9545 2365 e simon.cox@csiro.au w people.csiro.au/C/S/Simon-Cox CSIRO Data61 Peter Taylor Engineer t +61 3 6232 5530 e peter.taylor@csiro.au w http://people.csiro.au/T/P/Peter-Taylor