Land Use and Land Cover Classification Using Sentinel-2 Satellite Imagery

Abstract: Land Use and Land Cover (LULC) classification is an important task in remote sensing for environmental monitoring, agriculture, and urban planning. This paper presents a deep learning-based approach for classifying satellite images into different land cover categories using a Convolutional Neural Network (CNN). The model is trained on the EuroSAT dataset, which consists of Sentinel-2 satellite images categorized into 10 classes such as forest, residential, river, and agricultural land. The proposed model uses multiple convolutional layers along with batch normalization and dropout to improve performance and reduce overfitting. Experimental results show that the model achieves high accuracy and performs effectively in distinguishing different land cover types. This system can be used for real-world applications such as land monitoring and disaster management.

Keywords: LULC, CNN, Deep Learning, Satellite Imagery, EuroSAT, Remote Sensing