Spatio-temporal Data on the Web Working Group - Publications
Recommendations
The Semantic Sensor Network Ontology (commonly known as "SSN" is an ontology for describing sensors and the observations they make of the physical world. SSN is published in a modular architecture that supports the judicious use of "just enough" ontology for diverse applications, including satellite imagery, large scale scientific monitoring, industrial and household infrastructure, citizen observers, and Web of Things.
Notes
This specification defines WebVMT, the Web Video Map Tracks format, which is an enabling technology whose main use is for marking up external metadata track resources in connection with the HTML
This document shows how dense geospatial raster data can be represented using the W3C RDF Data Cube ontology in concert with other popular ontologies. SPARQL queries can then be served through an OGC Discrete Global Grid System for observations, coupled with a triple store for observational metadata.
An extension to the RDF Data Cube ontology to support specification of key metadata required to interpret spatio-temporal data. QB4ST provides generalized support for numeric and other ordered references systems, particularly Spatial Reference Systems and Temporal Reference Systems.
This Note describes CoverageJSON, a data format for describing "coverage" data in JavaScript Object Notation (JSON), and provides an overview of its design and capabilities. The primary intended purpose of the format is to enable data transfer between servers and web browsers, to support the development of interactive, data-driven web applications.
This document describes use cases that demand a combination of geospatial and non-geospatial data sources and techniques. It underpins the collaborative work of the Spatial Data on the Web Working Groups operated by both W3C and OGC.
Candidate Recommendation Drafts
First Public Working Drafts
The Semantic Sensor Network ontology (SSN) describes actuators and their actuations, sensors and their observations, samplers and their samplings, the procedures implemented, the features of interest and samples changed or studied, and the actuated and observed properties. The core SSN classes and properties are defined using minimal axiomatization in a module called SOSA (Sensor, Observation, Sample, and Actuator) supplemented with additional axiomatization and terms in further modules. These allow SSN to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Alignments to some existing ontologies and specifications are provided. Patterns for application of SSN are provided.
Draft Notes
This document advises on best practices related to the publication and usage of spatial data on the Web; the use of Web technologies as they may be applied to location. The best practices are intended for practitioners, including Web developers and geospatial experts, and are compiled based on evidence of real-world application.