• NISCHAL SINGH
• Vaishnavi Nanaware
• Sara Patil
• Vedant Kale
• Prof. Varunakshi Bhojane

Land Use / Land Cover (LULC) generally refers to the categorization or classification of human activities and natural elements on the landscape within a specific time frame based on established scientific and statistical methods of analysis of appropriate source materials. The rapid industrialization and urbanization of an area require quick preparation of actual land use/land cover (LU/LC) maps to detect and avoid overuse and damage of the landscape beyond sustainable development limits. This project focuses on leveraging deep learning techniques for land use/land cover (LULC) classification of satellite images to address the challenges posed by rapid industrialization and urbanization. It involves analyzing various deep learning models, selecting appropriate ones based on past research, and developing a hybrid model for improved accuracy. The performance of individual models and the hybrid approach will be compared to determine the most effective method for automatic image interpretation in LULC classification tasks.

Dataset, Satellite Images, U-Net, DenseNet121, Image Segmentation, Hybrid Model

"EFFECTIVE PERFORMANCE OF DIFFERENT DEEP LEARNING MODELS FOR LAND USE LAND COVER CLASSIFICATION OF SATELLITE IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l125-l130, April-2024, Available :http://www.jetir.org/papers/JETIR2404B16.pdf

"EFFECTIVE PERFORMANCE OF DIFFERENT DEEP LEARNING MODELS FOR LAND USE LAND COVER CLASSIFICATION OF SATELLITE IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl125-l130, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B16.pdf