Urban monthly land dynamics Sentinel-2 benchmark dataset

<p>The public data set of the paper "<strong>Time-series land cover change detection using deep learning-based temporal semantic segmentation</strong>" uses monthly synthesized Sentinel-2 for time series semantic change detection. A total of 32894 samples were collected. Each timestamp has a land cover type annotation. Anyone can use this data set to conduct further research. We will add more areas in the future.&nbsp;</p> <p><strong>Data description:</strong></p> <ol> <li>The time series length of the sample is 48, 48 months.</li> <li>Label mapping: 0 is water body, 1 is woodland, 2 is grassland, 3 is bare soil, 4 is impervious surface, 5 is cropland.</li> <li>For any implementation details, you can refer to the paper or github.</li> </ol> <p><strong>Paper citations:</strong></p> <p>He H, Yan J, Liang D, Sun Z, Li J, Wang L. Time-series land cover change detection using deep learning-based temporal semantic segmentation. Remote Sensing of Environment. 2024, 305:114101.</p>