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A Geospatial Semantic Enrichment and Query Service for Geotagged Photographs.

Ennis A, Nugent C, Morrow P, Chen L, Ioannidis G, Stan A, Rachev P - Sensors (Basel) (2015)

Bottom Line: Nevertheless, it still remains a challenge to discover the "right" information for the appropriate purpose.To achieve this we have developed and implemented a semantic geospatial data model by which a photograph can be enrich with geospatial metadata extracted from several geospatial data sources based on the raw low-level geo-metadata from a smartphone photograph.We present the details of our method and implementation for searching and querying the semantic geospatial metadata repository to enable a user or third party system to find the information they are looking for.

View Article: PubMed Central - PubMed

Affiliation: School of Computing and Mathematics, University of Ulster, Coleraine BT370QB, UK. ennis-a1@email.ulster.ac.uk.

ABSTRACT
With the increasing abundance of technologies and smart devices, equipped with a multitude of sensors for sensing the environment around them, information creation and consumption has now become effortless. This, in particular, is the case for photographs with vast amounts being created and shared every day. For example, at the time of this writing, Instagram users upload 70 million photographs a day. Nevertheless, it still remains a challenge to discover the "right" information for the appropriate purpose. This paper describes an approach to create semantic geospatial metadata for photographs, which can facilitate photograph search and discovery. To achieve this we have developed and implemented a semantic geospatial data model by which a photograph can be enrich with geospatial metadata extracted from several geospatial data sources based on the raw low-level geo-metadata from a smartphone photograph. We present the details of our method and implementation for searching and querying the semantic geospatial metadata repository to enable a user or third party system to find the information they are looking for.

No MeSH data available.


JavaScript Object Notation (JSON) object returned from API search query.
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sensors-15-17470-f005: JavaScript Object Notation (JSON) object returned from API search query.

Mentions: Figure 5 shows the JSON object returned from the API query. It contains one POI and its attributes, such as place name, its features, and feature description. This can then be used by a smartphone app, for example, to display the relevant information back to the user.


A Geospatial Semantic Enrichment and Query Service for Geotagged Photographs.

Ennis A, Nugent C, Morrow P, Chen L, Ioannidis G, Stan A, Rachev P - Sensors (Basel) (2015)

JavaScript Object Notation (JSON) object returned from API search query.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541944&req=5

sensors-15-17470-f005: JavaScript Object Notation (JSON) object returned from API search query.
Mentions: Figure 5 shows the JSON object returned from the API query. It contains one POI and its attributes, such as place name, its features, and feature description. This can then be used by a smartphone app, for example, to display the relevant information back to the user.

Bottom Line: Nevertheless, it still remains a challenge to discover the "right" information for the appropriate purpose.To achieve this we have developed and implemented a semantic geospatial data model by which a photograph can be enrich with geospatial metadata extracted from several geospatial data sources based on the raw low-level geo-metadata from a smartphone photograph.We present the details of our method and implementation for searching and querying the semantic geospatial metadata repository to enable a user or third party system to find the information they are looking for.

View Article: PubMed Central - PubMed

Affiliation: School of Computing and Mathematics, University of Ulster, Coleraine BT370QB, UK. ennis-a1@email.ulster.ac.uk.

ABSTRACT
With the increasing abundance of technologies and smart devices, equipped with a multitude of sensors for sensing the environment around them, information creation and consumption has now become effortless. This, in particular, is the case for photographs with vast amounts being created and shared every day. For example, at the time of this writing, Instagram users upload 70 million photographs a day. Nevertheless, it still remains a challenge to discover the "right" information for the appropriate purpose. This paper describes an approach to create semantic geospatial metadata for photographs, which can facilitate photograph search and discovery. To achieve this we have developed and implemented a semantic geospatial data model by which a photograph can be enrich with geospatial metadata extracted from several geospatial data sources based on the raw low-level geo-metadata from a smartphone photograph. We present the details of our method and implementation for searching and querying the semantic geospatial metadata repository to enable a user or third party system to find the information they are looking for.

No MeSH data available.