Classification of Buildings and Vehicles in Google Map Satellite Images Using Random Forest Classifier

Abstract

The Google Map provides an additional feature for observing various places of the landscapes as bird’s eye view with the help of the satellite images. Google Map satellite images are developed with the help of geographical information systems (GIS) data, aerial images, drone images, and satellite images with the help of image fusion methods to produce a bird’s eye view of a landscape. This proposed research work is intended to classify the buildings and vehicles from the Google Map satellite images that are captured at a zoom level of 20 m. This research work carried out to survey an area for various applications which require number of buildings and number of vehicles. The random forest classifier is used for the pixel classification, and this technique is also referred as pixel segmentation. The random forest classifier produces a result of 87% accuracy.