Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 543-547, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
08 Jun 2016
S. Koswatte, K. Mcdougall, and X. Liu School of Civil Engineering and Surveying, Faculty of Health, Engineering and Sciences, University of Southern Queensland, West Street, QLD 4350, Australia
Keywords: Geospatial Semantics, SDI, Crowdsourced Data, Ontologies, QLD Floods Abstract. Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.
Conference paper (PDF, 786 KB)

Citation: Koswatte, S., Mcdougall, K., and Liu, X.: SEMANTIC LOCATION EXTRACTION FROM CROWDSOURCED DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 543-547,, 2016.

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