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Sensor Web Enablement (SWE) and SensorML

Learn about Sensor Web Enablement (SWE) and SensorML, the standards and web services that enable the discovery, access, and tasking of sensors and sensor data. Explore the applications in heterogeneous sensor networks, decision support tools, surveillance, and more.

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Sensor Web Enablement (SWE) and SensorML

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  1. Sensor Web Enablement (SWE)and SensorMLJanuary 2008 Mike Botts mike.botts@uah.edu Principal Research Scientist University of Alabama in Huntsville

  2. OpenGeospatial Consortium (OGC)

  3. Open Geospatial Consortium • The Open Geospatial Consortium, Inc (OGC) is an international industry consortium of 334+ companies, government agencies and universities participating in a consensus process to develop publicly available interface specifications and encodings. • Open Standards development by consensus process • Interoperability Programs provide end-to-end implementation and testing before spec approval • Standard encodings (e.g. GML, SensorML, O&M, etc.) • Geography Markup Language (GML) – Version 3.2 • Style Layer Description language (SLD) • SensorML • Observations and Measurement (O&M) • Standard Web Service interfaces; e.g.: • Web Map Service (WMS) • Web Feature Service (WFS) • Web Coverage Service (WCS) • Catalog Service • Open Location Services – used by communications and navigation industry • Sensor Web Enablement Services (SOS, SAS, SPS)

  4. Sensor Web Enablement

  5. Basic Desires • Quicklydiscover sensors and sensor data (secure or public) that can meet my needs – location, observables, quality, ability to task • Obtain sensor information in a standard encoding that is understandable by me and my software • Readily access sensor observations in a common manner, and in a form specific to my needs • Task sensors, when possible, to meet my specific needs • Subscribe to and receive alerts when a sensor measures a particular phenomenon

  6. Heterogeneous sensor network Airborne Satellite Decision Support Tools In-Situ monitors Bio/Chem/Rad Detectors Surveillance • sparse • disparate • mobile/in-situ • extensible Sensor Web Enablement • discovery • access • tasking • alert notification web services and encodings based on Open Standards (OGC, ISO, OASIS, IEEE) Models and Simulations • nested • national, regional, urban • adaptable • data assimilation • vendor neutral • extensive - flexible • adaptable Sensor Web Enablement Framework M. Botts -2004

  7. Background -1- OGC Web Services Testbed 1.2: • Sponsors: EPA, General Dynamics, NASA, NIMA • Specs: SOS, O&M, SensorML, SPS, WNS • Demo: Terrorist, Hazardous Spill and Tornado • Sensors: weather stations, wind profiler, video, UAV, stream gauges • Specs advanced through independent R&D efforts in Germany, Australia, Canada and US • Sensor Web Work Group established • Specs: SOS, O&M, SensorML, SPS, WNS, SAS • Sensors: wide variety OGC Web Services Testbed 1.1: • Sponsors: EPA, NASA, NIMA • Specs: SOS, O&M, SensorML • Demo: NYC Terrorist • Sensors: weather stations, water quality SensorML initiated at University of Alabama in Huntsville: NASA AIST funding 1999 - 2000 2001 2002 2003-2004

  8. Background -2- OGC Web Services Testbed 3.0: • Sponsors: NGA, ORNL, LMCO, BAE • Specs: SOS, O&M, SensorML, SPS, TransducerML • Demo: Forest Fire in Western US • Sensors: weather stations, wind profiler, video, UAV, satellite SAS Interoperabilty Experiment OGC Web Services Testbed 4.0: • Sponsors: NGA, NASA, ORNL, LMCO • Specs: SOS, O&M, SensorML, SPS, TransducerML, SAS • Demo: Emergency Hospital • Sensors: weather stations, wind profiler, video, UAV, satellite OGC Web Services Testbed 5.1 • Sponsors: NGA, NASA, • Specs: SOS,SensorML,TML • Demo: Streaming JPIP of Georeferenceable Imagery; Geoporocessing Workflow • Sensors: Satellite and airborne imagery SWE Specifications toward approval: SensorML – V0.0 TransducerML – V0.0 SOS – V0.0 SPS – V0.0 O&M – Best Practices SAS – Best Practices 2006 2005 2007

  9. SWE Specifications • Information Models and Schema • Sensor Model Language (SensorML) for In-situ and Remote Sensors - Core models and schema for observation processes: support for sensor components, georegistration, response models, post measurement processing • Observations and Measurements (O&M) – Core models and schema for observations • TransducerML – adds system integration and multiplex streaming clusters of observations • Web Services • Sensor Observation Service - Access Observations for a sensor or sensor constellation, and optionally, the associated sensor and platform data • Sensor Alert Service – Subscribe to alerts based upon sensor observations • Sensor Planning Service – Request collection feasibility and task sensor system for desired observations • Web Notification Service –Manage message dialogue between client and Web service(s) for long duration (asynchronous) processes • Sensor Registries – Discover sensors and sensor observations

  10. Status • Current specs are in various stages • SensorML (and SWE Common) – Version 1.0.1 • TransducerML – Version 1.0 • Observations & Measurement – Version 1.0 • WNS – Request for Comments • SOS – Version 1.0 • SPS – Version 1.0 • SAS – Request for Comments • Approved SWE standards can be downloaded: • Specification Documents: http://www.opengeospatial.org/standards • Specification Schema: http://schemas.opengis.net/

  11. Known Open Source Softwarefor SWE • 52 North / University of Muenster • Full suite of SWE services (SOS, SPS, SAS, WNS) • University of Alabama in Huntsville • SWE Common parser/writer, SensorML parser, process chain executor and process model library • editors for SensorML/O&M instances and profiles Space Time Toolkit SWE client • SOS/WCS services • SWE portrayal service (initially KML) • Texas A&M / Marine Metadata Initiative • Non ebRIM registry based on ontology • light weight clients, several services • MapServer / GDAL • SWE services incorporated into MapServer • NASA GSFC / GeoBlinky • Several components used with the EO1 SAT activities • Northrop Grumman • Several components used within the PulseNet activity • SAIC • Ongoing development of several Open-Source SWE components under MASINT funding There is an initiative to begin to look at joint development and management of Open Source SWE software

  12. Example Known External Activities using SWE • Community Sensor Models (NGA) – SensorML encoding of the CSM; CSM likely to be the ISO19130 standard • Multi-Int Metadata Standards (DIA) – Committed to SensorML and SWE as direction • OGC OWS5.1Georeferenceable Imagery (NGA/NASA) – will be demonstrating use of SensorML within JPEG2000 and JPIP for support of geolocation of streaming imagery • Oak Ridge National Labs SensorNet – funded project will be adding SensorML support in SensorNet nodes for threat monitoring • Northrop Grumman IRAD (NGC TASC) – demonstrated end-to-end application of SensorML/SWE for legacy surveillance sensors in field • Empire Challenge (NGA - SAIC) – planning to test SWE for sensor observation processing and integration in 2008 testbed • European Space Agency – developing SensorML profiles for supporting sensor discovery and processing within the European satellite community • Canadian GeoConnections Projects – used SensorML in water monitoring network • Hurricane Missions (NASA MSFC) – using SensorML for geolocation and processing of satellite and airborne sensors • Sensors Anywhere (SAny) – intending to use SensorML/SWE Common within large European sensor project • NASA ESTO – funded 30 3-year projects on Sensor Webs; 5 SBIR topics with SensorML and Sensor Web called out

  13. SensorML

  14. What is SensorML? • XML encoding for describing sensor processes • Including sensor tasking, measurement, and post-processing of observations • Detectors, actuators, sensors, etc. are modeled as processes • Open Standard – • Approved by Open Geospatial Consortium in 2007 • Supported by Open Source software (COTS development starting) • Not just a metadata language • enables on-demand execution of algorithms • Describes • Sensor Systems • Processing algorithms and workflows

  15. Why is SensorML Important? • Importance: • Discovery of sensors and processes / plug-n-play sensors – SensorML is the means by which sensors and processes make themselves and their capabilities known; describes inputs, outputs and taskable parameters • Observation lineage – SensorML provides history of measurement and processing of observations; supports quality knowledge of observations • On-demand processing – SensorML supports on-demand derivation of higher-level information (e.g. geolocation or products) without a priori knowledge of the sensor system • Intelligent, autonomous sensor network – SensorML enables the development of taskable, adaptable sensor networks, and enables higher-level problem solving anticipated from the Semantic Web

  16. Non-Physical Processes Physical Processes Atomic Processes Composite Processes SensorML Processes Processes where physical location or physical interface of the process is not important (e.g. a fast-Fourier process) Processes where physical location or physical interface of the process is important (e.g. a sensor system) Processes that are considered Indivisible either by design or necessity Processes that are composed of other processes connected in some logical manner

  17. Example Atomic Processes • Transducers (detectors, actuators, samplers, etc.) • Spatial transforms (static and dynamic) • Vector, matrix, quaternion operators • “Sensor models” • scanners, frame cameras, SAR • polynomial models (e.g. RPC, RSM) • tie point model • Orbital models • Geospatial transformations (Map projection, datum, coordinate system) • Digital Signal Processing / image processing modules • Decimators, interpolators, synchronizers, etc. • Data readers, writers, and access services • Derivable Information (e.g. wind chill) • Human analysts

  18. Example Composite Processes • Sensor Systems, Platforms • Observation lineage • from tasking to measurement to processing to analysis • Executable on-demand process chains: • geolocation and orthorectification • algorithms for higher-level products • e.g. fire recognition, flood water classification, etc. • Image processing, digital signal processing • Uploadable command instructions or executable processes

  19. Status of SensorML and SWE Common • SensorML history • Influenced by interoperability challenges for satellite sensors at NASA • Started at UAH in 1998 under NASA AIST funding; brought into OGC in 2000 • Approved as Public Discussion Paper (2002) • Approved as Recommended Paper (2004) • OGC 05-086 approved as Best Practices Document in Bonn (Nov 2005) • OGC 05-086r3 approved as Version 0.0 Technical Specification in July 2006 • OGC 07-000 approved as Technical Specification Version 1.0 on June 23, 2007 • Current: document (OGC 07-000) • Approved Version 1.0 of SensorML and SWE Common data types • Official document available at OGC ( http://www.opengeospatial.com ) • Official Reference Schema resides online at http://schemas.opengis.net/ • Doc and schema also available at http://vast.uah.edu/SensorML

  20. Where has SensorML been demonstrated and tested ?

  21. Known Demonstrations and Testbeds for SensorML • Previous OGC OWS Testbeds • OWS 1.1 (2001) – description and access to in-situ sensors • OWS 1.2 (2002) – discovery, access, and georeferencing of remote sensors; fusion with in-situ sensors • OWS 3.0 (2005) – discovery, on-demand processing of radar, satellite, UAV, weather station, and plume model observations • OWS 4.0 (2006) – discovery, on-demand processing of CBRNE, plume model, and weather sensors • NASA SMART ODM (2006-present) – have used SensorML to georeference satellite data and to automatic determination of coincidence between sensor and numerical model data • Northrop Grumman IRAD(PulseNet 2006-2007) – demonstrated end-to-end application of SensorML/SWE for legacy surveillance sensors in field • Empire Challenge2007 (NGA) – SensorML used for discovery and data access of disparate sensor sources • Canadian GeoConnections Projects(2005) – used SensorML in water monitoring network (discovery and data access) • NASA Hurricane Missions(2006-present) – using SensorML for geolocation and processing of satellite and airborne sensors • NASA SensorML Project(2006-present) – incorporation and demonstration of SensorML execution engine into Space Time Toolkit and SWE services

  22. Previous OGC OWS Testbeds: SensorML-Enabled Discovery and Georeferencing Weather LaPlata Tornado UAV for Fire Detection Radiation plumesand weather

  23. A Few Known Ongoing Activities using SensorML • Community Sensor Models (NGA) – SensorML encoding of the CSM; CSM likely to be the ISO19130 standard • Multi-INT Metadata Standards (DIA and DISA) – Committed to SensorML and SWE as direction • OGC OWS5.1 Georeferenceable Imagery (NGA/NASA) – will be demonstrating use of SensorML within JPEG2000 and JPIP for support of georeferencing of streaming imagery • Oak Ridge National Labs SensorNet – funded project will be adding SensorML support in SensorNet nodes for threat monitoring (including georeferenced streaming video) • Empire Challenge 2008 (NGA) – planning to test SensorML for sensor observation processing and fusion in 2008 testbed • European Space Agency – developing SensorML profiles for supporting sensor discovery and processing within the European satellite community • Hurricane Missions (NASA MSFC) – working toward using SensorML for geolocation and processing of satellite and airborne sensors during real-time missions • Sensors Anywhere (SAny) – intending to use SensorML/SWE Common within large European sensor project • NASA– funded 30 3-year projects developing capabilities for SensorML and Sensor Webs; Also recently announce call for SBIR proposals with SensorML and Sensor Web topics identified

  24. PulseNet: SensorML-Enabled Discovery, Data Access, and Tasking Credit: Northrop Grumman PulseNet Project

  25. NASA Projects: SensorML-Enabled On-demand Processing (e.g. georeferencing and product algorithms) AMSR-E SSM/I TMI & MODIS footprints MAS TMI Geolocation of satellite and airborne sensors using SensorML Cloudsat LIS

  26. Sensor 1Scanner Sensor 2IMU Sensor 3GPS SensorML – Sensor Systems System - Aircraft IR radiation Digital Numbers Pitch, Roll, Yaw Tuples Attitude Lat, Lon, Alt Tuples Location Mike Botts, Alexandre Robin, Tony Cook - 2005

  27. AIRDAS UAV Geolocation Process Chain Demo AIRDAS data stream geolocated using SensorML-defined process chain(software has no a priori knowledge ofsensor system) AIRDAS data stream (consisting of navigation data and 4-band thermal-IR scan-line data)

  28. SensorML for Discovery

  29. SensorML provides metadata suitable for discoveryof sensors and processes Find all remote sensor systems measuring in the visible spectral range with ground resolution less than 20m.

  30. Discovery Based on SensorML Credit: Northrop Grumman PulseNet Project

  31. Specific Discovery Needs • Unique resource ids used throughout SWE; • sensor centric example: • Find sensors that can do what I need (returns id=“urn:ogc:id:sensor:123”) • Get me a full description of this sensor urn:ogc:id:sensor:123 • Now, find a service (SPS) that lets me task this sensor urn:ogc:id:sensor:123 • Find all services (SOS) where I can get observations from this sensor urn:ogc:id:sensor:123 • Find all processes that can be applied to this sensor output to generate the information I require • Catalog profiles for each need: • SPS, SOS, SAS services • sensors and processes • observations • terms (either through dictionaries or ontologies)

  32. Need for Term Definitions used in SensorML and SWE • Observable properties / phenomena / deriveable properties (“urn:ogc:def:property:*) • temperature, radiance, species , exceedingOfThreshold, earthquake, etc. • rotation angles, spectral curve, histogram, etc. • Capabilities, Characteristics, Interfaces, etc. (“urn:ogc:def:property:*”) • Width, height, material composition, etc. • Ground resolution, dynamic range, peak wavelength, etc. • RS-232, USB-2, bitSize, baud rate, base64, etc. • Sensor and process terms (“urn:ogc:def:property:*”) • IFOV, focal length, slant angle, etc. • Polynomial coefficients, matrix, image, etc. • Identifiers and classifiers (“urn:ogc:def:identifierType:*; urn:ogc:def:identifier:*” ) • Identifiers – longName, shortName, model number, serial number, wingID, missionID, etc. • Classifiers – sensorType, intendedApplication, processType, etc. • Role types (“urn:ogc:def:role:*”) • Expert, manufacturer, integrator, etc. • Specification document, productImage, algorithm, etc. • Sensor and process events (“urn:ogc:def:classifier:eventType:*”) • Deployment, decommissioning, calibration, etc.

  33. Help, Help, Help • We need authoritative bodies with access to subject-matter-experts (SME) to step forward to establish resolvable term dictionaries for sensors, processes, and observations • Potential authoritative bodies • IC community – GIG, MASINT WG, Multi-INT Interoperability Lab ?? • Civilian satellite community – Committee for Earth Observation Satellites (CEOS) • Others - ??? • Way forward • Create namespace for terms • Develop web interface for submitting term (Wikipedia perhaps, XML-based?) • Term • Definition • References • Relationship (?) – or allow separate ontologies to provide this • Level of authorization • Set up web services for resolving and getting filtered list of terms • Set up authentication process and authentication levels (e.g. submitted, under review, approved, rejected) • Accepting SensorML and SWE without creating authorized terms won’t accept interoperability

  34. Where and how SensorML can be used

  35. SensorML Observation Supports description of Lineage for an Observation Within an Observation, SensorML can describe how that Observation came to be using the “procedure” property

  36. SensorML Observation On-demand processing of sensor data SensorML processes can be executed on-demand to generate Observations from low-level sensor data (without a priori knowledge of sensor system)

  37. SensorML Observation Observation On-demand processing of higher-level products SensorML processes can be executed on-demand to generate higher-level Observations from low-level Observations (e.g. discoverable georeferencing algorithms or classification algorithms)

  38. On-demand Geolocation using SensorML AMSR-E SSM/I TMI & MODIS footprints MAS TMI Geolocation of satellite and airborne sensors using SensorML Cloudsat LIS

  39. SensorML Clients can discover, download, and execute SensorML process chains SensorML-enabled Client (e.g. STT) SLD OpenGL SOS Stylers For example, Space Time Toolkit is designed around a SensorML front-end and a Styler back-end that renders graphics to the screen

  40. Incorporation of SensorML into Space Time Toolkit Space Time Toolkit being retooled to be SensorML process chain executor + stylers

  41. Space Time Toolkit Sample Applications -2-

  42. SensorML-Enabled Web Services

  43. Observation SensorML can support generation of Observations within a Sensor Observation Service (SOS) SOS Web Service SensorML request For example, SensorML has been used to support on-demand generation of nadir tracks and footprints for satellite and airborne sensors within SOS web services

  44. Incorporation of SensorML into Web Services SensorML process chains have been used to drive on-demand data within services (e.g. satellite nadir tracks, sensor footprints, coincident search output)

  45. SensorML can support tasking of sensors within a Sensor Planning Service (SPS) SPS Web Service SensorML request For example, SensorML will be used to support tasking of video cam (pan, tilt, zoom) based on location of target (lat, lon, alt)

  46. SPS control of Web Cam

  47. SensorML for Portrayal

  48. SWE Visualization Clients can render graphics to screen SensorML-enabled Client (e.g. STT) SLD SensorML OpenGL SOS Stylers

  49. SWE Portrayal Service can “render” to various graphics standards SWE Portrayal Service SLD SensorML KML Collada SOS Google Earth Client Stylers For example, a SWE portrayal service can utilize a SensorML front-end and a Styler back-end to generate graphics content (e.g. KML or Collada)

  50. SensorML to Google Earth (KML – Collada) AMSR-E SSM/I MAS TMI LIS

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