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Abstract — Clustering can help to make large datasets more manageable by grouping together similar objects. However, most clustering approaches are unable to scale to very large datasets (eg. more than 10 million objects). The K-Tree is a data structure and clustering algorithm that has proven to be scalable with large streaming datasets. Here, we apply the K-Tree to spatial data (satellite images) and extend from a single threaded to a multicore environment. We show that the K-Tree is able to cluster larger datasets more efficiently than baseline approaches.
This report is a compilation of published material covering technology developments that relate directly to spatial technologies or that operate in support of spatial technologies.
There is a sense from this review that the world has a nascent realisation that the digital transformation is providing us with a positioning and location capability that is precise, ‘always on’, and is tracked, stored and retrievable for instant or future use. Once privacy concerns have been addressed and effective safeguards against the potential dangers are in place, the transformative applications of this capability will be enormous.