Using Deep Learning Approach for Land-Use and Land-Cover Classification based on Satellite images

Abstract

The land cover is the apparent (bio)physical cover, and land use alludes to how the actual land type is being utilized. This research is fundamental to survey the degree to which social, monetary, and natural factors influence urbanization. This will likewise assist with urban planning. As laborious process of handcrafted feature extraction has not helped obtain high accuracies, this paper proposes use of Deep Learning approach that explores different Image Recognition Models using various ML classifiers on remote sensing images classifying the images from large Landsat satellite dataset into 9 different classes. It was observed that the highest accuracy of 97.4% was achieved by the Logistic Regression algorithm coupled with Inceptionv3 model. The proposed model shows the capability of increasing the accuracy of existing state-of-art-algorithms low resolution land classification maps. Thus, the improved results will contribute to better land maps helping with the growing demand of LULC information concerning climate change and sustainable development.