Urban monthly land dynamics Sentinel-2 benchmark dataset

The public data set of the paper "<b>Time-series land cover change detection using deep learning-based temporal semantic segmentation</b>" 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.<b>Data description:</b>The time series length of the sample is 48, 48 months.The dimension of the time series is 13: [Sentinel-2 data (10) + Lable (1) + Lon(1) + Lat (1) ].Label mapping: 0 is water body, 1 is woodland, 2 is grassland, 3 is bare soil, 4 is impervious surface, 5 is cropland.For any implementation details, you can refer to the paper or github.<b>Paper citations:</b>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.<br><b>Sentinel-2 time-series image </b><b>dataset:</b>Shenzhen_Link: https://pan.baidu.com/s/1ua7qguFZ5TYpjkSECF9Z8Q?pwd=cug2Wuhan_Link: https://pan.baidu.com/s/1-vaumdV00OHsgwKIVE_wGA?pwd=cug2Xiong'an New Area_Link: https://pan.baidu.com/s/1Led0sXkUeebW39D-LPKAqw?pwd=cug2Cairo_Link: https://pan.baidu.com/s/1lf49HLEmaDQtwEspfl_0FA?pwd=cug2Melbourne_Link: https://pan.baidu.com/s/1Rtnv9nW9O-NzOItK5UmGGA?pwd=cug2San Francisco_Link: https://pan.baidu.com/s/1Zt-MhbsPl7PyUcU35V0-ug?pwd=cug22025.03.07: Added datasets for San Pablo and Mexicali for 2020-2023.<br>