OGC Sensor Web Enablement Airborne Application March 18
OGC Sensor Web Enablement Airborne Application March 18, 2008 Dr. Mike Botts mike. botts@uah. edu Principal Research Scientist University of Alabama in Huntsville Mike Botts – October 2008 1
What is SWE? • SWE is technology to enable the realization of Sensor Webs – much like TCP/IP, HTML, and HTTPD enabled the WWW • SWE is a suite of standards from OGC (Open Geospatial Consortium) – 3 standard XML encodings (Sensor. ML, O&M, TML) – 4 standard web service interfaces (SOS, SAS, SPS, WNS) • SWE is a Service Oriented Architecture (SOA) approach • SWE is an open, consensus-based set of standards Helping the World to Communicate Geographically
Why SWE? • Break down current stovepipes • Enable interoperability not only within communities but between traditionally disparate communities – – different sensor types: in-situ vs remote sensors, video, models, CBRNE different disciplines: science, defense, intelligence, emergency management, utilities, etc. different sciences: ocean, atmosphere, land, bio, target recognition, signal processing, etc. different agencies: government, commercial, private, Joe Public • Leverage benefits of open standards – competitive tool development – more abundant data sources – utilize efforts funded by others • Backed by the Open Geospatial Consortium process – 350+ members cooperating in consensus process – Interoperability Process testing – CITE compliance testing Helping the World to Communicate Geographically
What are the benefits of SWE? • Sensor system agnostic - Virtually any sensor or model system can be supported • Net-Centric, SOA-based – Distributed architecture allows independent development of services but enables on-the-fly connectivity between resources • Semantically tied – Relies on online dictionaries and ontologies for semantics – Key to interoperability • Traceability – observation lineage – quality of measurement support • Implementation flexibility – wrap existing capabilities and sensors – implement services and processing where it makes sense (e. g. near sensors, closer to user, or inbetween) – scalable from single, simple sensor to large sensor collections Helping the World to Communicate Geographically
Basic Desires • Quickly discover sensors and sensor data (secure or public) that can meet my needs – based on location, observables, quality, ability to task, etc. • Obtain sensor information in a standard encoding that is understandable by my software and enables assessment and processing without a-priori knowledge • 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 Helping the World to Communicate Geographically
SWE Specifications • Information Models and Schema – Sensor Model Language (Sensor. ML) for In-situ and Remote Sensors - Core models and schema for observation processes: support for sensor components and systems, geolocation, response models, post measurement processing – Observations and Measurements (O&M) – Core models and schema for observations; archived and streaming – Transducer Markup Language (TML) – 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 (eb. RIM)– Discover sensors and sensor observations Helping the World to Communicate Geographically
Why is Sensor. ML Important? – Discovery of sensors and processes / plug-n-play sensors – Sensor. ML is the means by which sensors and processes make themselves and their capabilities known; describes inputs, outputs and taskable parameters – Observation lineage – Sensor. ML provides history of measurement and processing of observations; supports quality knowledge of observations – On-demand processing – Sensor. ML 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 – Sensor. ML enables the development of taskable, adaptable sensor networks, and enables higher-level problem solving anticipated from the Semantic Web Helping the World to Communicate Geographically
Sensor. ML Descriptions • UAV Sensor System Description – Provides as detailed description of the system as you desire – Example: Sensor. ML XML – Example: Pretty View version mined from Sensor. ML • Community Sensor Models (CSM) – Tigershark System – KCM-HD camera (Sensor. ML encoding) Helping the World to Communicate Geographically
Executing Sensor. ML Processes • Flexibility of execution engine – UAH Open Source Execution Engine Sensor. ML – Compute server (e. g. NGA IPL) – COTS (e. g. ERMapper, Matlab, etc. ) – Web services (e. g. BPEL, Grid) • Flexibility of execution location – Client Sensor. ML – Web Service – Middleware – On-board sensor or platform Helping the World to Communicate Geographically
Sensor. ML On-Demand Processing • Streaming AIRDAS observations from an SOS – navigation observations • ASCII encoded • latitude, longitude, altitude, pitch, roll, true heading – scan lines observations • base 64 encoded (could also be pure binary or ASCII) – video • On-demand geolocation of streaming data using Sensor. ML – video – Space Time Toolkit knows nothing about doing geolocation – Sensor. ML provides the required expertise • Could be any algorithm or process • Discoverable processes, as well as sensors – Space Time Toolkit only knows how to execute Sensor. ML Helping the World to Communicate Geographically
SWE Visualization Clients can render graphics to screen Sensor. ML-enabled Client (e. g. STT) SLD Sensor. ML Open. GL SOS Stylers For example, Space Time Toolkit executes Sensor. ML process chain on the front-end, and renders graphics on the screen based on stylers (uses OGC Style Layer Description standard) Mike Botts – January 2008 11
Incorporation of SWE into Space Time Toolkit has been retooled to be Sensor. ML process chain executor + SLD stylers Mike Botts – January 2008 12
SWE Portrayal Service can “render” to various graphics standards SWE Portrayal Service SLD Sensor. ML KML Collada SOS Stylers Google Earth Client For example, a SWE portrayal service can utilize a Sensor. ML frontend a Styler back-end to generate graphics content (e. g. KML or Collada) However, it’s important that the data content standards (e. g. SWE) exist to support the graphical exploration and exploitation ! Mike Botts – March 2008 13
SWE to Google Earth (KML – Collada) AMSR-E SSM/I MAS TMI LIS Mike Botts – March 2008 14
Demo: Tigershark UAV-HD Video Tigershark Sensor. ML-enabled Client (e. g. STT) SLD SOS Sensor. ML JP 2 Open. GL NAV Stylers The Tigershark SOS has two offerings: (1) time-tagged video frames (in JP 2) and (2) aircraft navigation (lat, lon, alt, pitch, roll, true heading) both served in O&M. A Sensor. ML process chain (using CSM frame sensor model) geolocates streaming imagery onthe-fly within the client software (enabled with Sensor. ML process execution engine) Helping the World to Communicate Geographically
Demo: Tigershark UAV-HD Video -2 • Empire Challenge 2008 – Purpose of Demo: illustrate on-demand geolocation and display of HD video from Tigershark UAV – Client: UAH Space Time Toolkit – Services: • SOS – Tigershark video and navigation (ERDAS) • SOS – Troop Movement (Northrop Grumman) • Sensor. ML – On-demand processing (Botts Innovative Research, Inc. ) • Virtual Earth – base maps – Download this demo Helping the World to Communicate Geographically
Demo: Real-time Video streaming • UAH Dual Web-based Sky Cameras – Purpose of Demo: demonstrate streaming of binary video with navigation data; on-demand geolocation using Sensor. ML – Client: • 52 North Video Test Client • UAH Space Time Toolkit – Services: • SOS – video and gimbal settings (UAH, 52 North) • SPS – Video camera control (52 North, UAH) • Sensor. ML – On-demand processing (UAH) • Virtual Earth – base maps – Download this demo Helping the World to Communicate Geographically
Other Known Applications -1 • Community Sensor Models (NGA/CSM-WG) – Sensor. ML encoding of the CSM; CSM likely to be the ISO 19130 standard • CBRNE Tiger Team (DIA, ORNL, JPEO, NIST, STRATCOM) – Sensor. ML and SWE as future direction, with CCSI from JPEO and possibly IEEE 1451 • Sensor. Net (Oak Ridge National Labs) – funded project to add SWE support into Sensor. Net nodes for threat monitoring – Developing Sensor. Net/SWE architecture for North Alabama (SMDC, DESE, UAH, ORNL) • Pulse. Net (Northrop Grumman TASC) – demonstrated end-to-end application of Sensor. ML/SWE for legacy surveillance sensors (demonstrated at EC 07 and EC 08) • Sensor Web (SAIC - Melbourne, FL) – Developing end-to-end SWE components for MASINT and multi-sensor intelligence (demonstrated at EC 08) • European Space Agency – developing Sensor. ML profiles for supporting sensor discovery and processing within the European satellite community – establishing SPS and SOS services for satellite sensors • NASA – funded 30 3 -year projects (2006) based on RFP citing Sensor. ML and Sensor Webs; additional RFP in 2008 – 5 SBIR topics with Sensor. ML and Sensor Web cited – Received 2008 Business Innovative of Year Award for Sensor Web 2. 0 based on SWE (new proposals under review) • Empire Challenge 2007 & 2008 – Pulse. Net demonstrated at EC 07 – SAIC Sensor Web and OGC SWE Pilot Project participated at EC 08 Helping the World to Communicate Geographically
Other Known Applications -2 • Sensors Anywhere (S@NY), OSIRIS, and NSPIRES – Using Sensor. ML and SWE within several large European Union sensor projects • Marine Metadata Initiative, OOSTethys, GOMOOS, Q 2 O (NOAA) – Implementing and demonstrating SWE in several oceans monitoring activities – Developing Sensor. ML models and encodings for supporting QA/QC in ocean observations • Department of Homeland Security – In 2007 SBIR, requested Sensor. ML and SWE proposals • ASUS Wireless Home Monitoring System – $23 billion/year company in Taiwan building commercial Zigbee Home Monitoring system using SWE • DLR German-Indonesian Tsunami Warning System • Others – Landslide monitoring in Germany – Water quality monitoring in Europe and Canada – Mining and water management in Australia – Building monitoring in Australia – SWE a part of GEOSS and CEOS activities – Hurricane monitoring at NASA – Vaisala weather sensor vendor joined OGC and creating Sensor. ML descriptions of their sensor systems Helping the World to Communicate Geographically
Conclusions • SWE has been tested and has proven itself – Useful, flexible, efficient, extensible – Simple to add to both new and existing legacy systems – Enables paradigm shifts in access to and processing of observations • SWE is getting buy-in from scattered sensor communities – Large agencies like NGA, DIA, NASA, ESA, DLR, NOAA – Smaller communities as well – SWE open to improvements by the user communities • Tools are being developed to support SWE (Open Source and Commercial) – Tools will ease buy-in – Tools will assist in realizing the full benefits of SWE • SWE would be useful to airborne sensor community – Standard sensor system descriptions – Efficient observation streaming – On-demand georectification and processing – Flexibility for service (on-board or on-ground) Helping the World to Communicate Geographically
Relevant Links Open Geospatial Consortium http: //www. opengeospatial. org Sensor Web Enablement Working Group http: //www. ogcnetwork. net/SWE Public Forum http: //mail. opengeospatial. org/mailman/listinfo/swe. users Sensor. ML information http: //vast. uah. edu/Sensor. ML Public Forum http: //mail. opengeospatial. org/mailman/listinfo/sensorml Helping the World to Communicate Geographically
Additional Slides Helping the World to Communicate Geographically
Sensor. ML Process Editors Currently, Sensor. ML documents are edited in XML (left), but will soon be edited using human friendly view (below) Currently, we diagram the process (right top) and then type the XML version; soon the XML will be generated from the diagram itself (right bottom) Mike Botts – January 2008 23
Java Class Generator Tool Takes an instance of a Sensor. ML Process. Model and generates the template for the Java class that can execute the Process. Model Programmer needs add only execution code Mike Botts – March 2008 24
Sensor. ML Table Viewer • Will provide simple view of all data in Sensor. ML document • Web-based servlet or standalone; upload Sensor. ML file and see view • Ongoing effort: initial version in May 2008 • Future version will support resolvable links to terms, as well as plotting of curves, display of images, etc Mike Botts – March 2008 25
Simple Sensor. ML Forms for the Mass Market User fills out simple form with manufacturer name and model number, as well as other info. Then detailed Sensor. ML generated. Mike Botts – March 2008 26
Where and how Sensor. ML can be used Mike Botts – March 2008 27
Supports description of Lineage for an Observation Sensor. ML Observation Within an Observation, Sensor. ML can describe how that Observation came to be using the “procedure” property Mike Botts – March 2008 28
On-demand processing of sensor data Sensor. ML Observation Sensor. ML processes can be executed on-demand to generate Observations from low-level sensor data (without a priori knowledge of sensor system) Mike Botts – March 2008 29
On-demand processing of higher-level products Sensor. ML Observation Sensor. ML processes can be executed ondemand to generate higher-level Observations from low-level Observations (e. g. discoverable georeferencing algorithms or classification algorithms) Mike Botts – March 2008 30
Sensor. ML can support generation of Observations within a Sensor Observation Service (SOS) SOS Web Service Sensor. ML request Observation For example, Sensor. ML has been used to support on-demand generation of nadir tracks and footprints for satellite and airborne sensors within SOS web services Mike Botts – March 2008 31
Sensor. ML can support tasking of sensors within a Sensor Planning Service (SPS) SPS Web Service Sensor. ML request For example, Sensor. ML will be used to support tasking of video cam (pan, tilt, zoom) based on location of target (lat, lon, alt) Mike Botts – March 2008 32
SWE Visualization Clients can render graphics to screen Sensor. ML-enabled Client (e. g. STT) SLD Sensor. ML Open. GL SOS Stylers Mike Botts – March 2008 33
SWE Portrayal Service can “render” to various graphics standards SWE Portrayal Service SLD Sensor. ML KML Collada SOS Stylers Google Earth Client For example, a SWE portrayal service can utilize a Sensor. ML front-end a Styler back-end to generate graphics content (e. g. KML or Collada) Mike Botts – March 2008 34
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