Analyzing land use land cover changes in Mysuru taluk, Karnataka state, India using vision transformers

This study presents an analysis of land use and land cover changes in Mysuru Taluk, Karnataka, India, over a two-decade period (2004–2014 and 2014–2024), using Vision Transformers (ViTs) to enhance classification accuracy. Using Linear Imaging Self-Scanning Sensor (LISS-III) remote sensing data, our approach combines the powerful feature extraction capabilities of ViTs to address the complexities inherent in multi-temporal satellite data. Traditional methods for LULC mapping face challenges due to variability in land features and temporal changes, which impact classification accuracy. By employing ViTs, we aim to overcome these limitations through their self-attention approach, which can capture long-range dependencies in the data, thus offering a more refined classification process. Our study results show improved overall classification accuracy across the assessed years, achieving 95.07 % in 2004, 95.79 % in 2014, and reaching 96.74 % in 2024. These progressive results highlight the efficiency of ViTs in accurately classifying and detecting subtle land cover changes over time. Further, change detection analysis results show that the built-up area increased by 17.25 %, and agricultural land decreased by 16.24 % over two decades. The findings will assist policymakers and urban planners develop strategies to manage urbanization effectively while minimizing environmental impacts.