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GML, KML, Sensor Data, Imagery

GML has now become of age. It is used as a standard content encoding mechanism for all kinds of geographic information from commercial aviation to climatology and avalanche hazards. Nonetheless we have only begun to see the impact of this and related open standards on the world around us. GML can be used to make a wide range of geographic and geographically related data ubiquitous – including not only conventional "vector" features like roads and railways, but also sensor data (observations/measurements), and all kinds of coverages such as remotely sensed imagery and soil distribution. And now with the almost global acceptance of KML (formerly Keyhole Markup Language) we can readily turn that GML into visual presentations in Google Earth and a host of other mapping (e.g. ESRI tools) and visual presentation environments.

Think of the impact for agriculture. The growing population of the world, climate change, and restrictions on fossil fuel consumption are going to demand that our agricultural sector be more efficient and productive. This means measuring more parameters such as soil moisture, soil pH, detailed soil chemistry and measuring at a larger and larger number of locations. This information tied to measurements of biomass production and micro-terrain will lead to better models of plant growth and hence better management techniques including more effective use of water, fertilizers, pest control and other measures.

Sensors can provide GML observations directly to a standard "data logger" (WFS), located near the farm site, and this information can then be directly correlated with coverage information in GMLJP2 such as satellite imagery, terrain models, and hyperspectral imagery from aircraft or UAV's, this information being served from another WFS or WCS. A farm enterprise can then easily integrate or fuse this information to build a comprehensive picture of their farming enterprise.

Figure 1. shows the basic elements of the scheme, namely:

1. Sensors directly generate WFS transactions or respond to WFS requests – providing GML observations to the data logging WFS.

2. Higher level WFS aggregate and integrate this data with data from other WFS.

3. WFS/WCS serve coverage information such as satelite imagery, terrain models etc.

4. Enterprise level WFS provides an integrated picture of a farm or group of farms , including the farm geometry (extent/layout) and the various management parameters (e.g. crop production distribution by type)

5. Enterprise level FPS (Feature Portrayal Service) Web Map Service draws data from the various WFS to generate visual presentations in KML which can be viewed in Google Earth and similar tools.

6. The Web Registry Service (WRS) tracks the sensors and their descriptions (e.g. using SensorML , TML or IEEE descriptions) and provides ancillary information such as descriptions of the web services, the farms themselves, and the coordinate systems used to define the location of the farms and the sensor networks.


Figure 1: GML, KML – Geographic Features, Sensor Data and Imagery

Note that we have been able to deal with a very complex process using a comparatively small number of standards and by extension a small number of software components.

Of course, none of this is restricted to agriculture. The exact same standards apply equally well to homeland security, environmental protection, and urban infrastructure development – which is a good thing since these are all interrelated!!

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