GEO-SPATIAL DATA ANALYSIS OVER OPEN STREET MAP USING LATENT SEMANTIC ANALYSIS AND GENETIC ALGORITHM
Jaideep Kumar Sonam · Journal of Critical Reviews · 2020
The expansion or improvements of metropolitan areas and rural area is moderately speedily going on, particularly the metropolis of India like (Delhi, Mumbai and more), in addition, amplify the requirement of information retrieval about the said location like transportation infrastructure, commodities, hospitality, business opportunities, and agricultural aspects. Escalating demand for such resources is very essential for the sake of progression and development for masses therefore, to omit the obstacles or to remit decrease the level of service. The proposed scheme uses Machine Learning Techniques i.e. Latent Semantic Analysis and Genetic Algorithm which inculcates for swift and prompt information espionage or information retrieval system from huge corpus or map repositories. However, the supervision model is prepared based on the investigation of service derived from the ratio of data volume and capacity in context to locations and references. The method used to obtain data from geometric data and land use are done with image interpretation and measurement vector data as XML from OpenStreetMap. The result of the study presents a level of accuracy of approx 79% the scheme mainly uses geometrical data available on OSM to extract land use data for information retrieval effectively.