Terrain identification and land price estimation using deep learning

Abstract

The real estate market is becoming one of the most competitive in terms of pricing and same tends to vary significantly based on various factors. This paper focuses on identifying the type of land and estimating the price using convolutional neural network. Deep learning algorithms exhibit marvelous performance over conventional machine learning algorithms identifying the complex patterns. In this paper two Convolutional Neural Network (CNN) models are used. One is self-proposed model and another is ResNet152V2 model. Models are trained using aerial land image dataset. The ResNet152V2model showed high accuracy compared to the self proposed model. This system helps the land owner to acquire some basic essential details about the land.