Urban land price assessment based on GIS and deep learning

Abstract

The land price reflects the supply and demand of the land market and the economic life of the city, is indispensable to regulate urban land use and optimize the allocation of land resources. Due to the complex factors affecting the price of urban land, involving natural factors, social factors, economic factors, market factors, there is currently no model method at home and abroad that can effectively integrate these factors for residential land price assessment.This research explore the identification method of urban land price influencing factors in artificial intelligence environment, and combine deep learning algorithm with urban land price evaluation method. The deep neural network is used to integrate the spatial characteristics of land influencing factors. By establishing the deep hybrid neural network with space features, the linear relationship and causal relationship of influencing factors and the land price are automatically identified. The deep learning algorithm for the factors affecting of Shenzhen urban land price, promote the intelligent evaluation of land price.