Datasets:

monster-monash
/

TimeSen2Crop

@@ -22,6 +22,8 @@ Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
 |License|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)|
 |Citations|[1] [2]|
 ***TimeSen2Crop*** consists of pixel-level Sentinel-2 data at a 10m resolution, extracted from the 15 Sentinel-2 tiles that cover Austria: see Figure [1, 2]. This is a pixel level dataset, where each time series representing changing values for a single pixel.  The dataset contains 16 classes representing different land cover types. The original data contains all Sentinel 2 images covering Austria acquired between September 2017 and August 2018 plus images for one tile acquired between September 2018 and August 2019. As the tiles are from different Sentinel-2 tracks and have been processed to remove images with cloud cover greater than 80%, the image acquisition dates for each tile differ and are irregular. This version of the dataset has been processed to interpolate each pixel to a daily time series representing one year of data (thus each pixel has a time series length of 365) and removing the "other crops" class. The processed dataset contains 1,135,511 multivariate time series, each with 9 channels (representing 9 spectral bands covering the visible and infrared frequencies) and 15 classes. Classes are unbalanced and unevenly distributed across the Sentinel-2 tiles. The dataset has been split into cross-validation folds based on geographic location by Sentinel-2 tile (i.e., such that, for each fold, time series from a given location appear in either the training set or test set but not both).
 [1] Giulio Weikmann, Claudia Paris, and Lorenzo Bruzzone. (2021). TimeSen2Crop: A million labeled samples dataset of Sentinel 2 image time series for crop-type classification. *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing*, 14:4699–4708.

 |License|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)|
 |Citations|[1] [2]|
+(Note: ***TimeSen2Crop*** has been removed from the MONSTER benchmark.)
 ***TimeSen2Crop*** consists of pixel-level Sentinel-2 data at a 10m resolution, extracted from the 15 Sentinel-2 tiles that cover Austria: see Figure [1, 2]. This is a pixel level dataset, where each time series representing changing values for a single pixel.  The dataset contains 16 classes representing different land cover types. The original data contains all Sentinel 2 images covering Austria acquired between September 2017 and August 2018 plus images for one tile acquired between September 2018 and August 2019. As the tiles are from different Sentinel-2 tracks and have been processed to remove images with cloud cover greater than 80%, the image acquisition dates for each tile differ and are irregular. This version of the dataset has been processed to interpolate each pixel to a daily time series representing one year of data (thus each pixel has a time series length of 365) and removing the "other crops" class. The processed dataset contains 1,135,511 multivariate time series, each with 9 channels (representing 9 spectral bands covering the visible and infrared frequencies) and 15 classes. Classes are unbalanced and unevenly distributed across the Sentinel-2 tiles. The dataset has been split into cross-validation folds based on geographic location by Sentinel-2 tile (i.e., such that, for each fold, time series from a given location appear in either the training set or test set but not both).
 [1] Giulio Weikmann, Claudia Paris, and Lorenzo Bruzzone. (2021). TimeSen2Crop: A million labeled samples dataset of Sentinel 2 image time series for crop-type classification. *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing*, 14:4699–4708.