New advancements in technology such as the rise of social networks have led to more geospatial data being produced every day. The current issue with the large volume of geospatial data is to store and process it because of the scalability of the data.In this thesis, two computing implementations, cloud computing and Internet computing, are studied and evaluated for their capability in storing,processing and visualizing large volumes of geospatial data. For the cloud computing implementation,the different concepts of cloud computing have been analysed according to their applications, models and services. Moreover, a case study using cloud computing platforms has also been implemented for storing and processing geotagged tweets retrieved for a national recreational park in Vancouver, BC. For the Internet computing platform,the Open Geospatial Consortium’s Web Processing Service has been investigated as a framework for sharing geospatial data and processing it over Internet. A raster calculation algorithm in Web Processing Service platforms has also been implemented on 2 scenes of Lands at satellite imagery to evaluate WPS’ capabilities in handling large volume of data. Results of this research suggest that internet computing can be used to handle geospatial data processing but,when dealing with large volumes of data,this study proves that Internet computing and current Geospatial Information Systems are not suitable to be used and cloud computing platform can be utilized to handle large volumes of geospatial data.
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