GEOSPATIAL EXPAT PARSER USING K-MEANS FOR QUERYING OPEN STREET MAP

The importance and motivations behind geospatial data extraction has been changing and developing step by step, energized more commitment by the OSM. This development gave progressive techniques for sharing furthermore, processing information by crowd-sourcing, for example, OSM, likewise called "the wikification of maps" by a few analysts. At the point when crowd-sourcing gathers immense information which withhold in corpus data, with help of overall territorial jurisdiction with fluctuating level of mapping background, the concentration of this scheme ought to be on breaking down the information as opposed to gathering it, using semantic parser we provide the scheme to query OSM information by looking at it with restrictive information or information of administrative guide offices and surveys the exploration work for appraisal of OSM and furthermore talks about the future bearings using Machine Readable Language. Therefore, in this scheme we propose to use the corpus data more than 500 MB from maps to extract an accurate semantic structure that will build the basis of a natural language interface to OSM. Furthermore, we use response-based learning on parser results to adapt a statistical machine translation system for relational database access to OSM.