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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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license: cc-by-4.0 |
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pretty_name: TimeSen2Crop |
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size_categories: |
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- 1M<n<10M |
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--- |
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>. |
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|TimeSen2Crop|| |
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|Category|Satellite| |
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|Num. Examples|1,135,511| |
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|Num. Channels|9| |
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|Length|365| |
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|Sampling Freq.|daily| |
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|Num. Classes|16| |
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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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(Note: ***TimeSen2Crop*** has been removed from the MONSTER benchmark.) |
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***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). |
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[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. |
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[2] Giulio Weikmann, Claudia Paris, and Lorenzo Bruzzone. (2021). TimeSen2Crop: A Million Labeled Samples Dataset of Sentinel 2 Image Time Series for Crop Type Classification. https://zenodo.org/records/4715631. CC BY 4.0. |
