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7 Design Criteria and Assumptions for
SensorML
7.1 Basic definition of a sensor
Sensors are devices for the measurement of physical quantities. A sensor measurement can be modeled as a process by which an input phenomenon is observed by the sensor at
some discrete moment in time. Some measure of some property of that phenomenon is then output from the sensor. The values of the measurement are dependent on the
sampling and response characteristic of that sensor, as well as on the sampling and detection methodologies. Often, either through hardware processing or subsequent
processing in software, the raw observations are processed to higher-level knowledge. The SensorML model does not try to define where observation measurement and
observation processing begin or end. These are both simply considered as part of the process.
There are a great variety of sensor types from simple visual thermometers to complex electron microscopes to radiometers on-board earth orbiting satellites. In some cases,
sensing may be accomplished by a person rather than a device, and the result of the “measurement” may be a category rather than a numeric quantity.
Typically, sensors fall into one of two basic types. In-situ sensors measure a physical property of the medium immediately surrounding the sensor, while remote sensors
measure physical properties which can be associated with features at some distance from the sensor, generally by measuring radiation reflected or emitted from an observed object.
Regardless, any geometric properties described within the SensorML schema are defined within the sensor’s local coordinate frame and are only related to the geospatial domain
through it frame’s association with the platform, mount, and their association with some geospatial reference frame. For example, to fully describe a wind profiler’s wind speed
and direction measurements, the height of the sensor needs to be known as that sensor could be situated on the roof of a building, mounted to a 10-meter tower, or sitting at
ground-level.
7.2 Applications of SensorML