Prediction of Population Density & Poverty Rate Using Uncertain Mosaics with Satellite Imagery - Jonnadula Prasanna; Mounika Susarla; K. Suvarna Vani; Harsha Vardhan Govada; Samuel Mories Mundru; M. S. R. Murthy (International Journal of Intelligent Systems and Applications in Engineering, 2022)
This research work involves combination of Random Forest optimization along with Satellite Imagery (SIML) which is having potential for addressing major global problems by remotely accessing socio-economic and meteorological conditions in data poor areas, although SIML’s resource requirements will limits its access and utilization. The Random Forest along with Satellite Imagery (SIML) is enabling better characterizations for population densities and poverty Rates. Further, this Random Forest Applications proves to be a path which is effective to convert such huge amount of uncertain image data into formed assess of conditions of ground.The satellite imagesare collected from GIS (Geographic information system) and then process collected images to RFO for removing mosaic with regression concept. This proposed model generates accuracy 98.78%, recall 97.34%, throughput 97.75% sensitivity 98.45% and efficiency 96.25%. The following outcomes has been competed with present technology and out performers the methodology.