Land use Land Cover Classification using Deep Neural Network Purva Suryawanshi 1 , Suraj Sawant 2 and Amit Joshi 3 1 Post Graduate Student, Computer Engineering Department, College of Engineering, Pune, India Email: suryawanshipd20.comp@coep.ac.in 2-3 Assistant Professor, Computer Engineering Department, College of Engineering, Pune, India Email: {sts.comp, adj.comp}@coep.ac.in Abstract—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. Index Terms— Remote sensing, Land use land cover, GIS, Deep Neural network, UNet, Sentinel-2. I. INTRODUCTION Land Use Land Cover(LULC) classification is important for mapping and environmental applications, such as urban planning [1], natural resources management, and crop yield prediction [2]. To provide useful land information, remote sensing is essential and often used with GIS techniques. Remote sensing technology has a high potential to perform pixel-based LULC classification. Remotely sensed imagery has traditionally been classified into land cover (LC) and land use (LU). Pixel-based image classification is assigning labels to each pixel of the image [3]. High-resolution remote sensing images provide spectral signatures, texture features, and spatial structure of ground objects which are essential for LULC classification [4]. Sentinel-2 L2A satellite images have been used to research various geographical applications. The effectiveness of Sentinel-2 has been explored, particularly in LULC mapping, and it has demonstrated a high potential for application[5]. Several techniques have been developed for LULC classification, including traditional machine-learning algorithms and advanced deep learning networks. Traditional machine learning algorithms such as the support vector machine (SVM) and the maximum likelihood classifier (MLC) are used to classify images into several classes. The image's low-level features, such as color, geometry characteristics, greyscale and spatial texture, are extracted for classification. These methods are less efficient, costly, and time-consuming for high-resolution satellite imagery[6]. The deep learning architectures allow the high-level feature extraction, and robust features Grenze ID: 01.GIJET.9.1.562 © Grenze Scientific Society, 2023 Grenze International Journal of Engineering and Technology, Jan Issue