Land use Land Cover Classification using Deep Neural Network

With remote sensor data, land use and land cover classification offers geographical information. The land use land cover classification process considers several factors for urban planning, natural resources management, environment management, and disaster monitoring. The recent advances in deep learning models to classify land use land cover classes. The objective of this research work is to use remote sensing and Geospatial Information Systems to perform land use land cover classification. The customized Sentinel-2 dataset is generated using QGIS to implement a Deep Neural network. In this study, pixel-based classification was performed using the UNet model. Accuracy assessment was performed using Overall Accuracy and F1-Score. The research showed overall classification accuracy of 95.5% and a value of F1- Score is 0.66. The UNet model is suitable for customized datasets to present essential information for sustainable environmental planning.