SensorML and Processing September 2009

1. SensorMLand ProcessingSeptember 2009 Mike Botts mike.botts@botts-inc.net Botts Innovative Research, Inc.

2. 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

3. 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

4. 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

5. 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 • To browse ProcessModel

6. 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

7. SensorML Process Chains

8. 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

9. 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

10. 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)

11. 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

12. 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)

13. 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)

14. 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

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

16. Space Time Toolkit Sample Applications -2-

17. 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

18. Conclusions • SensorML is not just for sensors • SensorML provides a robust means of describing a process (both physical and non-physical) – including methodology • SensorML process chains provide an implementation-agnostic way to describe workflows or algorithms • SensorML process chains can include and mix processes that are implemented locally and those implemented on web services • SensorML for processing has been tested and demonstrated in operational environments • Propose that SensorML processes be at least one of the means for a WPS to describe the process