Evaluation of Sustainable Utilization of Urban Land Resources based on DensN et20 1 and Inception Version 3

Recent days, urbanization is developed tremendously that providing economic growth and accesses to markets, entrepreneurship and industries. This Urban development leverages land resources in creating booming cities with diverse industries, institutions, and transportation infrastructure. As these constructions are increasing it is also very important to evaluate the urban land resources for flexible future generation's capability to happen their personal needs. The existing methods in evaluation failed in modeling temporary alteration in urban land use and development. Therefore, this paper proposes DensNet201-InceptionVersion 3 (V3) for efficient evaluation of sustain ability of urban land resources. The proposed method employed on deep globe land cover classification dataset that incorporates various land cover classes' satellite images. The collected image data is preprocessed using min max scaling for normalizing all images pixel values into similar ranges. This preprocessed data optimal features are extracted using DensNet201. The obtained relevant features are classified using Inception V3for evaluating the utilization of urban land resources are sustainable or not. The proposed DensNet201-InceptionV3 achieved better results in terms of accuracy of (99.82%), Specificity of (99.45%), Sensitivity of (99.73%), and Recall of (99.76%) when compared with existing Artificial Neural Network (ANN).