Mekanchi - Work @ heights - Drone ServicesShashank Arya
This video shows a sample of how Drones can be used for visual inspection of terrains. The same can be applied for various purposes such as Visual Inspection, Thermal Inspection, Leak detection, welding control, Cleaning validation, Topography Study of Aerial Map, Visual Inspection of offshore oil and gas towers and flares, Inspection of confined spaces - boilers and storage tanks, Building and rooftop inspection, Bridge inspection, Tower Inspections, Flare tip inspections, Measurement of methane gas emissions, Inspection of Sulphur, NOx and other particulate emissions from ship exhaust, Digital and/or infrared camera inspection, On-site rotor blade inspection for Wind Turbines, High-flying inspection of chimneys, Leak inspection of distant heating piping, Transmission Lines Installation and Inspection.
Possibilities of Open Source Code. FMI has a strong open source initiative and many open source software.
Presented at WMO Executive Council (EC-69) Side-Event.
Mekanchi - Work @ heights - Drone ServicesShashank Arya
This video shows a sample of how Drones can be used for visual inspection of terrains. The same can be applied for various purposes such as Visual Inspection, Thermal Inspection, Leak detection, welding control, Cleaning validation, Topography Study of Aerial Map, Visual Inspection of offshore oil and gas towers and flares, Inspection of confined spaces - boilers and storage tanks, Building and rooftop inspection, Bridge inspection, Tower Inspections, Flare tip inspections, Measurement of methane gas emissions, Inspection of Sulphur, NOx and other particulate emissions from ship exhaust, Digital and/or infrared camera inspection, On-site rotor blade inspection for Wind Turbines, High-flying inspection of chimneys, Leak inspection of distant heating piping, Transmission Lines Installation and Inspection.
Possibilities of Open Source Code. FMI has a strong open source initiative and many open source software.
Presented at WMO Executive Council (EC-69) Side-Event.
NHE How better procurement can save time, money and livesLinda Murphy
In the November/December 2014 issue of National Health Executive Chris Slater, head of supplies and procurement at Leeds Teaching Hospitals NHS Trust, and the trust's e-business manager Graham Medwell, explain its pioneering work on e-enablement, inventory control, materials management and freeing up clinicians for frontline work.
The science community has developed many models for representation of scientific data and knowledge. For example, the biomedical communities OBO Foundry federates applications covering various aspects of life sciences, which are united through reference to a common foundational ontology (BFO). The SWEET ontology, originally developed at NASA and now governed through ESIP, is a single large unified ontology for earth and environmental sciences. On a smaller scale, GeoSciML provides a UML and corresponding XML representation of geological mapping and observation data.
Key concepts related to scientific data and observations have now been incorporated into domain-neutral ontologies developed by the World Wide Web consortium. OWL-Time has been enhanced to support temporal reference systems needed for science, and deployed in a linked data representation of the geologic timescale. The Semantic Sensor Network ontology (SSN) has been extended to cover samples and sampling, including relationships between samples. Specific extensions for science are being added to the Data Catalog vocabulary (DCAT) used by data repositories such as RDA and CSIRO-DAP.
These standard vocabularies can be used directly for science data, or can provide a bridge to specialized domain ontologies. The W3C vocabularies support cross-disciplinary applications directly. The W3C vocabularies are aligned with the core ontologies that are the building blocks of the semantic web. The W3C vocabularies are hosted on well known, reliable infrastructure, and are being selectively adopted by the general schema.org discovery framework.
Presented at C3DIS 2018-05-29
http://www.c3dis.com/2017
A common model for scientific observations and samplesSimon Cox
Summary of O&M model and its various implementations (OMXML, OM-JSON, SOSA/SSN) with particular attention to Sampling model. Making the case that a x-disciplinary model for science observations is feasible (and has already been attempted).
Presented at meeting of ICSU/CODATA Commission on Standards, The Royal Society, London, 2017-11-13
Time, Change and Habits in Geospatial-Temporal Information StandardsGeorge Percivall
Keynote for HIC 2014 – 11th International Conference on Hydroinformatics, New York, USA August 17 – 21, 2014
Time, Change and Habits in Geospatial-Temporal Information Standards
Time and change are fundamental to our scientific understanding of the world. Standards for geospatial-temporal information exist but new needs outstrip current standards. Geospatial-temporal information includes capturing change in features and coverages and modeling the processes that inform change. Key standards for time, calendars, and temporal reference systems are in place. Time series modeling from the WaterML standard is a recent advance of high value to hydrology. The OGC Moving Features standard will establish an encoding format for changes in “rigid” features. Interoperability standards are needed for Coverages with values that change based on observations, analytical expressions, or simulations. Applying a coverage model to time-varying, fluid Earth systems was the topic of the ground breaking GALEON Interoperability Experiment. Standards developments for spatial-temporal process models is progressing with WPS, OpenMI and ESMF - supporting a Model Web concept. A robust framework for sharing geospatial-temporal information is now coming into place based on developments captured in standards by ISO, WMO, ITU, ICSU and OGC - including the newly established OGC Temporal domain working group. The new framework will enable capabilities in expressing and sharing scientific investigations including research on the emergence of forms over time. With these new capabilities we may come to understand Peirce’s observation that over time “all things have a tendency to take habits.”
Introducing a new encoding of the ISO 19156 Observations and Measurements model, to support transport of observation data using the JSON encoding beloved of web developers
Measuring Change with Radar Imagery_Richard Goodman - Intergraph Geospatial W...IMGS
ERDAS IMAGINE Radar Tools:
Radar Mapping Suite - Add-on module
Operational software - Not a toolkit!
Directly read data into viewer - No import required - No resampling of data
New Radar Analyst ribbon - Fast feature extraction -
Visualisation aids
Interferometry tools - CCD - D-InSAR
A critical step towards smarter and safer cities is to endow them with the abilities to massively gather a wide variety of data sets and to automatically feed those data to decision support tools and applications that leverage artificial intelligence. This involves sensing, communication and networking infrastructure, as well as services, which we currently call Internet of Things. We use examples from the Porto Living Lab infrastructure, specifically UrbanSense and SenseMyCity, to illustrate communication technologies available to collect and share the data, how to use opportunistic communication, and how to address interoperability.
About the potential of novel, alternative rain sensors, such as microwave links (MWL) used for telecommunication, crowd sensing, or cheap ubiquitous sensors.
Dynamic Line Rating: Principles - Applications - BenefitsLeonardo ENERGY
Video recording at https://youtu.be/xzWoQkVVhFc
This webinar introduces the physics of Dynamic Line Rating (DLR), and calculation methods based on CIGRE and IEEE standards. Various approaches are discussed: direct measurement technologies (sensors) as well as weather model-based simulations. We describe applications implemented by grid operators for some years already. These illustrate how Dynamic Line Rating data have been integrated into grid operators’ tools and processes, in particular how forecasts are used. Furthermore, some analytics will be shared that demonstrate the benefits of Dynamic Line Rating for reducing OPEX and CAPEX. This includes examples on increasing cross-border trading, reducing investment on new line infrastructure and reducing congestions, which helps to make decisions on reinforcement and investment.
ScaleMaster 2.0: a ScaleMaster extension to monitor automatic multi-scales ge...Guillaume Touya
Presentation at the International Cartographic Conference (ICC'13 Dresden) of the paper: "ScaleMaster 2.0: a ScaleMaster extension to monitor automatic multi-scales generalizations" by G. Touya and J.F. Girres
apidays LIVE Paris - Sopra Steria: path to the industrialization of sustaina...apidays
apidays LIVE Paris - Responding to the New Normal with APIs for Business, People and Society
December 8, 9 & 10, 2020
Sopra Steria: path to the industrialization of sustainable IT with Euromaster
Jérémy Sintes, Digital Transformation & Sobriety Senior Consultant & Pauline Villatte, Business Analyst at Sopra Steria
explained using only the 1000 most commonly used words in English. Presented as part of the 'Up-goer challenge' at a symposium on Linking Environmental Data and Samples, Canberra, May/June 2017 - see https://confluence.csiro.au/display/LEDS/Linking+Environmental+Data+and+Samples
For the actual performance see https://youtu.be/dq9ZxjBVVbk
PROV ontology supports alignment of observational data (models)Simon Cox
Paper presented at MODSIM 2017
https://www.mssanz.org.au/modsim2017/papersbysession.html session C2.
The W3C PROV ontology provides a flexible process-flow model that can capture many specific applications. A provenance trace is the retrospective view of a workflow, with specific instance data added. Thus it provides a basis for the description of any chain of activities which generate interesting outputs, such as observations, actuations, or acts of sampling. Furthermore, its relatively generic structure and naming allows it to be used as an alignment bridge with other ontologies that have previously challenged simple mappings. In this paper we will show a harmonization of a number of important ontology patterns that can be linked through the PROV-O Owl implementation of PROV.
The alignments stack is as follows:
- PROV-O aligned to W3C OWL-Time
- PROV-O aligned to BFO
- W3C SSN/SOSA aligned to PROV-O
- OBOE, OBI and BCO (from the obo foundation) aligned to SOSA/SSN and thus PROV-O
Some of the alignments have been proposed previously, but the set described here augments both them and is larger in aggregate than previous work.
The availability of these alignments supports the fusion of data from a range of disciplines particularly in earth and environmental sciences, in particular observational data where the act of sampling and observation is understood in a provenance context.
Vocabularies, ontologies, standards for observations: developments from RDA, ...Simon Cox
Overview of recent developments in RDA Vocabulary Services Interest Group, W3C/OGC SPatial Data on the Web Working Group, and ICSU/CODATA Commission on Standards.
Presented at National Oceanography Centre, Liverpool, UK 2017-11-17
Pitfalls in alignment of observation models resolved using PROV as an upper o...Simon Cox
AGU Fall Meeting, 2015-12-16
A number of models for observation metadata have been developed in the earth and environmental science communities, including OGC’s Observations and Measurements (O&M), the ecosystems community’s Extensible Observation Ontology (OBOE), the W3C’s Semantic Sensor Network Ontology (SSNO), and the CUAHSI/NSF Observations Data Model v2 (ODM2). In order to combine data formalized in the various models, mappings between these must be developed. In some cases this is straightforward: since ODM2 took O&M as its starting point, their terminology is almost completely aligned. In the eco-informatics world observations are almost never made in isolation of other observations, so OBOE pays particular attention to groupings, with multiple atomic ‘Measurements’ in each oboe:Observation which does not have a result of its own and thus plays a different role to an om:Observation. And while SSN also adopted terminology from O&M, mapping is confounded by the fact that SSNO uses DOLCE as its foundation and places ssn:Observations as ‘Social Objects’ which are explicitly disjoint from ‘Events’, while O&M is formalized as part of the ISO/TC 211 harmonised (UML) model and sees om:Observations as value assignment activities.
Foundational ontologies (such as BFO, GFO, UFO or DOLCE) can provide a framework for alignment, but different upper ontologies can be based in profoundly different world-views and use of incommensurate frameworks can confound rather than help. A potential resolution is provided by comparing recent studies that align SSNO and O&M, respectively, with the PROV ontology. PROV provides just three base classes:
Entity, Activity and Agent. om:Observation is sub-classed
from prov:Activity, while ssn:Observation is sub-classed from prov:Entity. This confirms that, despite the same name, om:Observation and ssn:Observation denote different aspects of the observation process: the observation event, and the record of the observation event, respectively.
Alignment with the simple PROV classes has clarified this issue in a way that had previously proved difficult to resolve. The simple 3-class base model from PROV appears to provide just enough logic to serve as a lightweight upper ontology, particularly for workflow or process-based information.
Ontology alignment – is PROV-O good enough?Simon Cox
Presentation to OGC Geosemantics Summit, 2015-06-03.
I explain the incompatibiity between the Observation classes in SSN and O&M, and how this can be understood mostly clearly through alignment with PROV. Compared with other 'upper ontologies' PROV provides a very easy to understand framework, with only 3 top level classes, two of which are disjoint.
Presentation describing recent work on observation-related vocabularies, undertaken by CSIRO as part of a contribution to Australia's National Environmental Information Infrastructure.
Presented at the 2nd workshop of the Ocean Data Interoperability Platform, La Jolla, Ca. 3rd-6th December, 2013
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
1. Observations to Information
Simon Cox | Research Scientist | Environmental Information Systems
12 December 2013
LAND AND WATER
2013 Leptoukh Lecture
2. Standards for observation data
• Motivation and development
• Earth-science and environmental applications
• Renewal
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh2 |
4. A bit of history
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
• 1993-2000 AGCRC
• Web-mapping
• Reporting research online
• 1995
4 |
5. A bit of history
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
• 1993-2000 AGCRC
• Web-mapping
• Reporting research online
• 1999-2004 XMML, ADX
• Exploration data standards
5 |
6. A bit of history
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
• 1993-2000 AGCRC
• Web-mapping
• Reporting research online
• 1999-2004 XMML, ADX
• Exploration data standards
6 |
7. A bit of history
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
• 1993-2000 AGCRC
• Web-mapping
• Reporting research online
• 1999-2004 XMML, ADX
-2010 AuScope
• Exploration data standards
7 |
8. A bit of history
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
• 1993-2000 AGCRC
• Web-mapping
• Reporting research online
• 1999-2004 XMML, ADX
-2010 AuScope
• Exploration data standards
• 2002-2005 OGC SWE
• Sensors anywhere
8 |
9. A bit of history
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
• 1993-2000 AGCRC
• Web-mapping
• Reporting research online
• 1999-2004 XMML, ADX
-2010 AuScope
• Exploration data standards
• 2002-2005 OGC SWE
• Sensors anywhere
• 2005-2013 Fluid Earth
• Water informatics
9 |
10. A bit of history
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
• 1993-2000 AGCRC
• Web-mapping
• Reporting research online
• 1999-2004 XMML, ADX
-2010 AuScope
• Exploration data standards
• 2002-2005 OGC SWE
• Sensors anywhere
• 2005-2013 WIRADA
• Water informatics
10 |
11. Motivation for a standard
All of society’s grand challenges require data to
be shared and integrated across cultures, scales
and technologies
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh11 |
12. Motivation for a standard
• Integrated analysis and modelling
• Discovery & data integration a
significant challenge
• Standard vocabulary
Many private contracts
one public agreement!
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
Remote sensing
Sensor
Value
Parameter
Scene
Earth science
Algorithm, code,
simulator
Model, field
Variable
Volume, grid
Metrology
Instrument
Value
Measurand
Sample
Chemistry
Instrument,
analytical process
Analysis
Analyte
Sample
Environmental
monitoring
Gauge, sensor
Value, time-series
Parameter
Station
Observations &
Measurements
procedure
result
observed property
feature of interest
12 |
14. 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
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
Cox, OGC Abstract Specification – Topic 20: Observations and Measurements 2.0
ISO 19156:2011 Geographic Information – Observations and measurements14 |
15. Scope
• In situ observations
• Remote sensing
• Ex-situ observations
• Numerical models/simulations
• Forecasts
Any action whose result is an estimate of a property value
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh15 |
16. Specimen
Sampling features
Observation
SamplingFeature
+ Parameter
+ lineage
Feature
0..*
SpatialSamplingFeature
+ positionalAccuracy
+relatedObservation 0..*
SamplingSolidSamplingPoint SamplingCurve SamplingSurface
Intention
+sampledFeature
SamplingFeatureComplex
+ role
0..*
+relatedSamplingFeature
0..*
+relatedObservation
0..*
Profile
Section
Station
Borehole
MapHorizonScene
Mine
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
Cox, OGC Abstract Specification – Topic 20: Observations and Measurements 2.0
ISO 19156:2011 Geographic Information – Observations and measurements
GM_Object
+shape
16 |
Harmonized with
CSML, NCAR
17. OGC Sensor Web Enablement
• SensorML
• O&M
• Sensor Observation Service
• Sensor Planning Service
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh17 |
18. AGU Fall 2013 | IN42A-01 | Cox | Leptoukh
SOS
getObservation
getResult
describeSensor
getFeatureOfInterest
Sensor Observation Service
Feature
service
getFeature,
type=Observation
Gridded data
service
getCoverage
getCoverage
(result)
Sensor
Register
getRecordById
Feature
service
getFeature
18 |
27. WaterML-WQ constrains
O&M and WaterML
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh27 |
«FeatureType»
measurement::
OM_Measurement
AnyFeature
«FeatureType»
observ ation::OM_Observ ation
+ parameter :NamedValue [0..*]
+ phenomenonTime :TM_Object
+ resultQuality :DQ_Element [0..*]
+ resultTime :TM_Instant
+ validTime :TM_Period [0..1]
constraints
{observedProperty shall be a phenomenon associated with the feature of interest}
{procedure shall be suitable for observedProperty}
{result type shall be suitable for observedProperty}
{a parameter.name shall not appear more than once}
Units of Measure::Measure
{root}
+ value :Number
+ convert(UnitOfMeasure*) :Measure
«FeatureType»
General Feature
Instance::GFI_Feature
«metaclass»
General Feature Model::
GF_PropertyType
{root}
«metaclass»
General Feature Model::
GF_FeatureType
«FeatureType»
observation::
OM_Process
Metadata entity set
information::
MD_Metadata
«FeatureType»
cov erageObserv ation::
OM_DiscreteCov erageObserv ation
«FeatureType»
Timeseries Observ ation::
TimeseriesObserv ation
«FeatureType»
Timeseries (TVP) Observ ation::
TimeseriesTVPObserv ation
The XML element om:result SHALL have a uom property
that is an instance of the owl:Class
http://qudt.org/schema/qudt#Unit as defined in
http://resources.data.gov.au/water/def/water-quality/wq-
quantity.
The XML element om:observedProperty SHALL have
an xlink:href property that is an instance of the
http://qudt.org/schema/qudt#Quantity scheme as
defined in
http://resources.data.gov.au/water/def/water-
quality/wq-quantity.
The XML element om:featureOfInterest SHOULD have an xlink:href property that is an instance of
a GroundWaterML 1 GroundWaterBody feature or sub-type of HydrologicUnit feature as specified
in the XML schema at http://ngwd-bdnes.cits.nrcan.gc.ca/service/gwml/schemas/gwml.xsd
OR
The XML element om:featureOfInterest SHOULD have an xlink:href property that is an instance of
an OGC HY_Features HY_HydroFeature or sub-type as specified at "HY_Features: a Common
Hydrologic Feature Model Discussion Paper OGC 11-039r2"
«Type»
Measurement (TVP) Timeseries::
MeasurementTimeseriesTVP
Timeseries
«FeatureType»
Interleav ed (TVP) Timeseries::
TimeseriesTVP
TimeValuePair
«Type»
Measurement (TVP) Timeseries::
MeasureTimeValuePair
+ value :Measure
«FeatureType»
WQ_Measurement::
WQ_Measurement
«FeatureType»
WQ_MeasurementTimeSeriesTVPObserv ation::
WQ_MeasurementTimeSeriesTVPObserv ation
«FeatureType»
WQ_MeasurementTimeseriesTVP::
WQ_MeasurementTimeSeriesTVP
«metaclass»
WQ_Observ ation::
WQ_PropertyType
O&M Classes
WaterML 2 Classes
Water Quality Classes
Legend
+generatedObservation 0..*
ProcessUsed
+procedure
1
Phenomenon
+observedProperty
1
Metadata
+metadata 0..1
+result
Range
+collection
0..*CoverageFunction
+element
0..*
+result
0..*
+relatedObservation 0..*
+propertyValueProvider
0..*
Domain
+featureOfInterest
1
+carrierOfCharacteristics
0..*
+theGF_FeatureType
1
«instanceOf»
• Subject is a groundwater
or geofabric feature
• observed property is
water-quality property
• units of measure must match
31. Existing Activity
SWE Adoption
Seismic Profile O&M
Diviacco, P. et al. 2011 Marine Seismic Metadata for an Integrated European Scale Data Infrastructure
31
36. 36
12Z
7-May 9-May8-May6-May5-May
00Z00Z12Z00Z12Z12Z00Z12Z 00Z
result
forecast : OM_Observation
parameter.name = “analysisTime”
parameter.value = 2010-05-06T00:00Z
phenomenonTime.begin = 2010-05-06T00:00Z
phenomenonTime.end = 2010-05-09T12:00Z
resultTime = 2010-05-06T04:30Z
validTime [optional – not specified]
resultQuality [optional – not specified]
ISO19156 Observations and measurements:
also suitable for numerical simulations – including forecasts
37. 37
Common constraints applicable to all WMO
METCE Observation types
WMO METCE
«FeatureType»
OM_ComplexObservation
(ISO 19156)
«Type»
Record
(ISO 19103)
+result
«FeatureType»
ComplexSamplingMeasurement
«FeatureType»
Process
+procedure
«FeatureType»
OM_Observation
(ISO 19156)
«FeatureType»
Process
+procedure
(ISO 19156)
«FeatureType»
GFI_FeatureType
+featureOfInterest
(ISO 19156)
«FeatureType»
SF_SpatialSamplingFeature
(ISO 19156)
+featureOfInterest
All specialisations of OM_Observation defined in WMO METCE require:
• association role ‘featureOfInterest’ shall be of type SF_SpatialSamplingFeature
• association role ‘procedure’ shall be of type Process (from WMO METCE)
38. SamplingFeature as OM_Observation.featureOfInterest
38
In meteorology, we define a sampling regime that enables us to observe,
measure or simulate the real-world. Sampling Features (from ISO 19156
‘Observations and measurements’) provide a way to characterise this sampling
regime and the relationship to the real-world.
:ComplexSamplingMeasurement03839:SF_SamplingPoint
+featureOfInterest
:Point
@srsName “EPSG:4326”
pos = “50.737 -3.405”
+shape
84579:NamedPlace
name = “Exeter Airport” +sampledFeature
55. Lessons
• Ontology for observations, models and forecasts
• X-domain interoperability
• Extensible, specialize with vocabularies, link to other ontologies
• Standards
• A generic model can provide a checklist for design, and a basis for
harmonization and incremental design
• Consensus introduces an overhead
• Change
• Common conceptual model supports multiple implementations
• Widely used
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh55 |
56. Additional credits
O&M: Fowler & Odell; GeoSciML team; OGC; ISO/TC 211;
Rob Atkinson, Rob Woodcock(CSIRO)
WaterML: CUAHSI; Gavin Walker, Pete Taylor, Laurent Lefort (CSIRO);
Paul Sheahan (BOM)
Soil, WQ: Bruce Simons, Peter Wilson, Jonathan Yu (CSIRO); Alistair Ritchie
(Landcare NZ)
SeaDataNet: Jordi Sorribas, Paolo Diviacco
AQD: Michael Lutz, Ale Sarretta (JRC); Kathi Schleidt (Env. Agency Austria)
WMO: Jeremy Tandy (UK Met Office); Aaron Braeckel (UCAR)
NEII: Andrew Woolf, Dom Lowe (BOM)
ODM2: Jeff Horsburgh
IGSN: Kerstin Lehnert (LDEO), Jens Klump (GFZ)
Vocabulary services: Stuart Williams (Epimorphics), Roy Lowry, Adam Leadbetter
(BODC)
AGU Fall 2013 | IN42A-01 | Cox | Leptoukh56 |
57. LAND AND WATER
Thank youCSIRO Land and Water
Simon Cox
Research Scientist
t +61 3 9252 6342
e simon.cox@csiro.au
w www.csiro.au/people/simon.cox