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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.


Related in: MedlinePlus

Snippet of RDF, for a POI, created by MediaPlace. The 256 bit SHA hash has been abbreviated for improved presentation clarity.
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sensors-15-17470-f003: Snippet of RDF, for a POI, created by MediaPlace. The 256 bit SHA hash has been abbreviated for improved presentation clarity.

Mentions: For each POI extracted from the datasets a “poi” node is created which is identified with a 256 bit SHA hash. This SHA hash is created from the place name, latitude and longitude. This ensures that each POI node has a unique identifier that each attribute can be linked to. It also enables checking if identification of the POI has already been added to the semantic geospatial metadata repository, therefore stopping it from being added twice. Figure 3 shows a short example of some of the triples that are created for a given POI.


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)

Snippet of RDF, for a POI, created by MediaPlace. The 256 bit SHA hash has been abbreviated for improved presentation clarity.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17470-f003: Snippet of RDF, for a POI, created by MediaPlace. The 256 bit SHA hash has been abbreviated for improved presentation clarity.
Mentions: For each POI extracted from the datasets a “poi” node is created which is identified with a 256 bit SHA hash. This SHA hash is created from the place name, latitude and longitude. This ensures that each POI node has a unique identifier that each attribute can be linked to. It also enables checking if identification of the POI has already been added to the semantic geospatial metadata repository, therefore stopping it from being added twice. Figure 3 shows a short example of some of the triples that are created for a given POI.

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.


Related in: MedlinePlus