SensorML

SensorML is an approved Open Geospatial Consortium standard. SensorML provides standard models and an XML encoding for describing sensors and measurement processes. SensorML can be used to describe a wide range of sensors, including both dynamic and stationary platforms and both in-situ and remote sensors.

Functions supported include

Examples of supported sensors are

SensorML provides standard models and an XML encoding for describing any process, including the process of measurement by sensors and instructions for deriving higher-level information from observations. It provides a provider-centric view of information in a sensor web, which is complemented by Observations and Measurements which provides a user-centric view.

Processes described in SensorML are discoverable and executable. All processes define their inputs, outputs, parameters, and method, as well as provide relevant metadata. SensorML models detectors and sensors as processes that convert real phenomena to data.

SensorML does not encode measurements taken by sensors; measurements can be represented in TransducerML, as observations in Observations and Measurements, or in other forms, such as IEEE 1451.

Electronic Specification Sheet -

In its simplest application, SensorML can be used to provide a standard digital means of providing specification sheets for sensor components and systems.

Discovery of sensor, sensor systems, and processes -

SensorML is a means by which sensor systems or processes can make themselves known and discoverable. SensorML provides a rich collection of metadata that can be mined and used for discovery of sensor systems and observation processes. This metadata includes identifiers, classifiers, constraints (time, legal, and security), capabilities, characteristics, contacts, and references, in addition to inputs, outputs, parameters, and system location.

Lineage of Observations -

SensorML can provide a complete and unambiguous description of the lineage of an observation. In other words, it can describe in detail the process by which an observation came to be .... from acquisition by one or more detectors to processing and perhaps even interpretation by an analyst. Not only can this provide a confidence level with regard to an observation, in most cases, part or all of the process could be repeated, perhaps with some modifications to the process or by simulating the observation with a known signature source.

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