An Intelligent GIS Database Framework Featuring Building Query Functionality Using n-grams Encoding and k-means Classification

In the current work we present the design and synthesis of a GIS framework with emphasis in building classification and identification. Its fundamental functionality comprises the submission of the geometric representation of a building as a query sample, subsequently encoded and employed by a classifying mechanism in order to discover similar occurrences within a pre-processed GIS-based building database. The encoding of both the query sample and the existing building database relies on n-grams whereas the classification and identification scheme combines k-means and a set of global and local features for the construction of similarity classes.