(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
Land Use and Land Cover (LULC) classification plays a crucial role in remote sensing applications such as urban planning, environmental monitoring, agricultural analysis, and climate studies. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have significantly improved classification accuracy for satellite imagery. This thesis presents a comparative study of two deep learning approaches for LULC classification using the EuroSAT dataset: a convolutional neural network trained from scratch and a transfer learning model based on a pre-trained VGG-19 architecture. The EuroSAT dataset consists of Sentinel-2 satellite images categorized into ten land cover classes. Experimental results demonstrate that transfer learning achieves superior classification performance compared to training a CNN from scratch, highlighting the effectiveness of pre-trained models for remote sensing image analysis.