S e n s o r M o d e l
L a n g u a g e O G C
0 7 - 0 0 0
The CRS concept will also be applied to temporal domain when applicable. One local time frame that is useful for defining the geometry and dynamics of scanners is seconds
past the start of a scan scan start time. Also, for some sensor systems, time is recorded relative to a local clock or the start of the mission. In such cases, time frames and their
relationship to “Earth time” will be defined in SensorML.
7.6 Measurement observation concepts
A sensor is designed to measure a particular property within a given sample space. When these measurements are taken, they result in an observation that may be immediately
utilized or stored. In its lowest level, this observation is typically a proxy measurement of some property other than the desired physical property, itself. For example, an
observation may be the height of mercury in a thermometer or the voltage across a circuit. In order for these observations to be related to a more useful physical property, a
new observation will typically be derived using known sensor calibration functions and perhaps other processing algorithms.
SensorML allows one to describe whatever level of observations the creator of the document wishes to expose. For example, one might specify that the sensor measures raw
voltages and then provide calibration descriptions that would allow conversion to other physical quantities. Alternatively, or in addition to, the sensor description might specify
that the sensor measures temperature, and then expose the calibration used to derive those temperature values, or not.
A SensorML document will describe what physical properties are measured by the sensor, as well as information concerning the properties and quality of these
measurements. In addition, a SensorML document may provide or link to the values of these measurements using one or more data provider types. However, a SensorML
document does not typically contain the observation values resulting from the measurement.
7.7 Sensor response characteristics
The response characteristics of a sensor determine how the sensor will react to a particular stimulus i.e., phenomenon and how it will operate under given environmental
conditions. Within the sensor response characteristics will be specifications for sensitivity e.g., threshold, dynamic range, capacity, band width, etc., accuracy and precision, and
behavior under certain environmental conditions.
A very large number of sensor response characteristics can be defined using a general response model e.g., the detector model defined in Annex C. However, there may be
specific sensors that require addition of different parameters to fully describe their response behavior. Where possible, these should be derived from the general model.
7.8 Sample and collection geometry concepts
As discussed above, a sensor measures some property within a spatially and temporally defined sample. In the case of an in-situ sensor, this sample includes some spatial volume
Copyright © 2007 Open Geospatial Consortium, Inc. All Rights Reserved.
36
S e n s o r M o d e l
L a n g u a g e O G C
0 7 - 0 0 0
in the immediate vicinity of the sensor. This volume may be infinitesimally small or it may be unknown or unimportant. For remote sensors, the sample involves some volume
or surface area located away from the immediate vicinity of the sensor.
The geometry of a sample may be specified relative to any coordinate system. However, particularly for a remote sensor, the geometric descriptions in SensorML are typically
defined relative to the sensor’s local coordinate frames and not a geospatial coordinate frame. As discussed before, this allows the same sensor model to be “attached” to any
stationary or dynamic platform without a need to significantly change the SensorML description. In such a case, an individual sample’s geometry, such as perhaps its size,
shape, or point-spread function, is described relative to a local sample coordinate frame.
This sample frame can be related to the sensor’s frame by either a simple transformation or in the case of collection of samples, by a more complex transformation involving
arrays or scan patterns. Possible transformations for sample collections include unstructured grids, regular arrays, scanners, frame cameras, and mathematical functions.
Copyright © 2007 Open Geospatial Consortium, Inc. All Rights Reserved.
37
S e n s o r M o d e l
L a n g u a g e O G C
0 7 - 0 0 0
8 SWE Common Conceptual Models
This document defines several basic value types and data encodings that will exist in the Sensor Web Enablement SWE Common namespace. These include definitions that are
expected to be shared among all SWE encodings and services. It is anticipated that in future releases, these SWE Common components will be defined as a separate standard
on which SensorML depends.
SWE Common provides a set of data types and related components that are required in various places across the suite of OGC SWE technologies. These fall into the following
categories:
• primitive data types, complementing those implemented in GML • general purpose aggregate data types, including records, arrays, vectors and
matrices • aggregate data types with specialized semantics, including position, curve, and
time-aggregates • standard encodings to add semantics, quality indication and constraints to both
primitive and aggregate types • specialized components to support semantic definitions, as required above
• a notation for the description of XML and non-XML array encodings. The last item in the list relates to a particularly important use case, concerning the
transport of large datasets organized as records and arrays. Such data may be represented for transport in fully XML encoded form with each data item in a separate element.
However, it is often desirable to allow alternative encodings of large volumes of data for transport, for both efficiency reasons and also for compatibility with legacy systems.
8.1 Simple Data Types
