Update README.md

README.md

@@ -1,14 +1,14 @@
----
-tags:
-- time series
-- time series classification
-- monster
-- satellite
-license: cc-by-4.0
-pretty_name: TimeSen2Crop
-size_categories:
-- 1M<n<10M
----
+---
+tags:
+- time series
+- time series classification
+- monster
+- satellite
+license: cc-by-4.0
+pretty_name: TimeSen2Crop
+size_categories:
+- 1M<n<10M
+---
 Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
 
 |TimeSen2Crop||
@@ -22,7 +22,7 @@ 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 [1]. 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) 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).
+***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.