mike botts – january 2008 1 sensorml and processing september 2009 mike botts...
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Mike Botts – January 2008 1
SensorML and Processing
September 2009
Mike Botts
Botts Innovative Research, Inc.
Mike Botts – January 2008 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
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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
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SensorML Processes
Physical ProcessesNon-Physical Processes
Atomic Processes
Composite Processes
Processes that are considered Indivisible either by design or necessity
Processes that are composed of other processes connected in some logical manner
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)
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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
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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
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SensorML Process Chains
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NASA Projects: SensorML-Enabled On-demand Processing (e.g. georeferencing and product algorithms)
AMSR-E SSM/I
Cloudsat LIS
TMI
TMI & MODIS footprints
MAS
Geolocation of satellite and airborne sensors using SensorML
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SensorML – Sensor Systems
Mike Botts, Alexandre Robin, Tony Cook - 2005
Sensor 1Scanner
System - Aircraft
Sensor 2IMU
Sensor 3GPS
IR radiation
Attitude
Location
Digital Numbers
Pitch, Roll, Yaw Tuples
Lat, Lon, Alt Tuples
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AIRDAS UAV Geolocation Process Chain Demo
AIRDAS data stream (consisting of navigation
data and 4-band thermal-IR scan-line data)
AIRDAS data stream geolocated using
SensorML-defined process chain
(software has no a priori knowledge of
sensor system)
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Supports description of Lineage for an Observation
Observation
SensorML
Within an Observation, SensorML can describe
how that Observation came to be using the
“procedure” property
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On-demand processing of sensor data
Observation
SensorML processes can be executed on-demand to
generate Observations from low-level sensor data
(without a priori knowledge of sensor system)
SensorML
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On-demand processing of higher-level products
Observation
SensorML
SensorML processes can be executed on-
demand to generate higher-level Observations
from low-level Observations (e.g. discoverable
georeferencing algorithms or classification
algorithms)
Observation
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Clients can discover, download, and execute SensorML process chains
For example, Space Time Toolkit is designed
around a SensorML front-end and a Styler back-
end that renders graphics to the screen
SensorML
OpenGL
SensorML-enabled Client (e.g. STT)
Stylers
SLD
SOS
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Incorporation of SensorML into Space Time Toolkit
Space Time Toolkit being retooled to be SensorML process chain executor + stylers
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Space Time Toolkit Sample Applications -2-
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SensorML can support generation of Observations within a Sensor Observation Service (SOS)
Observation
SensorML
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
SOS Web Service
request
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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