Accurate classification of Land Use and Land Cover (LULC) from satellite imagery is vital for environmental monitoring, sustainable urban development, and resource management. With the increasing availability of multi-spectral data from Earth observation missions such as Sentinel-2, deep learning provides powerful solutions for automating LULC classification. In this study, we present a lightweight Convolutional Neural Network (CNN) architecture tailored for 4-band satellite imagery. Unlike conventional approaches that rely solely on RGB inputs, our model incorporates Red, Green, Blue, and Near-Infrared (NIR) bands to capture a broader range of surface and vegetation characteristics. The architecture combines stacked convolutional blocks with batch normalization, pooling layers, and dropout regularization, ensuring both strong accuracy and efficient computation. Training was further enhanced through data augmentation strategies such as rotation, flipping, and zooming. Using the EuroSAT dataset (27,000 images across 10 classes), the model achieved a test accuracy of 96% and a macro-averaged F1-score of 0.96, with excellent performance in challenging categories such as Residential, SeaLake, and Forest. The compact design of the model makes it highly suitable for deployment in time-sensitive or resource-limited scenarios, including monitoring of city growth, assessing agricultural productivity, and supporting rapid response to environmental hazards.