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--- |
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tags: |
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- time series |
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- time series classification |
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- monster |
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- satellite |
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pretty_name: LakeIce |
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size_categories: |
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- 100K<n<1M |
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license: cc-by-4.0 |
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--- |
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>. |
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|LakeIce|| |
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|Category|Satellite| |
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|Num. Examples|129,280| |
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|Num. Channels|1| |
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|Length|161| |
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|Sampling Freq.|daily| |
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|Num. Classes|3| |
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|License|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)| |
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|Citations|[1] [2]| |
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***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. |
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[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. |
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[2] Maria Shaposhnikova, Claude R Duguay, and Pascale Roy-Léveillée. (2022). Annotated time series of lake ice C-band synthetic aperture radar backscatter created using Sentinel-1, ERS-1/2, and RADARSAT-1 imagery of Old Crow Flats, Yukon, Canada. <https://doi.org/10.1594/PANGAEA.947789>. CC BY 4.0. |
