Update README.md

README.md

@@ -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]|
 
-***LakeIce*** consists of pixel-level backscatter (reflection) values from satellite images of an area of approximately 6,000 km^2 in Yukon, Canada [1, 2]. The time series are extracted over three decades from ERS-1/2, Radarsat, and Sentinel-1 synthetic aperture radar satellites, which all use the C-band range of microwave frequencies (4-8GHz).  This is a pixel-level dataset, such that each time series represents values over time for single pixel.  The satellites used in this case have different spatial resolutions, resulting in a mixture of effective pixel sizes of between 12.5m, 30m, and 50m. The processed dataset contains 129,280 (univariate) time series each of length 161, representing daily data over near 6 months (October to March), with three classes, labelled manually, representing bedfast ice, floating ice, and land.  (The original data has been calibrated and speckle-filtered, and then interpolated to provide daily values for the relevant period [1].)  This version of the dataset has been split into stratified random cross-validation folds.
+***LakeIce*** consists of pixel-level backscatter (reflection) values from satellite images of an area of approximately 6,000 km<sup>2</sup> in Yukon, Canada [1, 2]. The time series are extracted over three decades from ERS-1/2, Radarsat, and Sentinel-1 synthetic aperture radar satellites, which all use the C-band range of microwave frequencies (4-8GHz).  This is a pixel-level dataset, such that each time series represents values over time for single pixel.  The satellites used in this case have different spatial resolutions, resulting in a mixture of effective pixel sizes of between 12.5m, 30m, and 50m. The processed dataset contains 129,280 (univariate) time series each of length 161, representing daily data over near 6 months (October to March), with three classes, labelled manually, representing bedfast ice, floating ice, and land.  (The original data has been calibrated and speckle-filtered, and then interpolated to provide daily values for the relevant period [1].)  This version of the dataset has been split into stratified random cross-validation folds.
 
 [1] Maria Shaposhnikova, Claude R Duguay, and Pascale Roy-Léveillée. (2023). Bedfast and floating-ice dynamics of rhermokarst lakes using a temporal deep-learning mapping approach: Case study of the Old Crow Flats, Yukon, Canada. *The Cryosphere*, 17(4):1697–1721.