• Azwina Mol
• G S Bishara
• Mushrifa Hussain
• Prof. Nivin KS
• Prof. Daya Naik

Land Use and Land Cover (LULC) classification is essential for effectively understanding and managing the Earth's surface resources. Satellite imagery serves as a critical tool for this task, offering large-scale, high-resolution data that captures diverse geographic regions. This project utilizes the EuroSAT dataset, a standardized set of multispectral satellite images, to classify various land cover types, including forests, water bodies, urban areas, and agricultural fields. The deep learning approach leverages the ResNet50 model, which is trained on smaller land patches and then evaluated for its ability to classify larger, unseen land areas accurately. Key aspects of this methodology include data preprocessing, model fine-tuning, and efficient use of the JPG (EuroSAT_RGB) dataset format, chosen for its accessibility and ease of use for beginners. The results demonstrate the model's capability to deliver precise classifications with clear visual outputs, supporting a wide range of applications, from urban planning and resource management to environmental monitoring. This research not only highlights the effectiveness of deep learning in LULC classification but also establishes a scalable framework for future innovations in remote sensing.

Satellite imagery, Land Use and Land Cover (LULC) classification, EuroSAT dataset, ResNet50, deep learning, remote sensing, environmental monitoring, urban planning, resource management, land analysis, geographic information systems (GIS), convolutional neural networks (CNN), digital image processing, Earth observation, sustainable development, geospatial analysis, environmental impact assessment, automated mapping.

"SATELLITE IMAGERY-BASED LAND USE AND LAND COVER CLASSIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.676-681, July-2025, Available :http://www.jetir.org/papers/JETIRGX06128.pdf

"SATELLITE IMAGERY-BASED LAND USE AND LAND COVER CLASSIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp676-681, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06128.pdf