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tags:
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- satellite
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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***TiSeLaC*** (Time Series Land Cover Classification) was created for the time series land cover classification challenge held in conjunction with the 2017 European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases [1]. It was generated from a time series of 23 Landsat 8 images of Reunion Island, acquired in 2014. At the 30m spatial resolution of Landsat 8 images, Reunion Island is covered by 2866 x 2633 pixels, however only 99,687 of these pixels are included in the dataset.
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Class labels were obtained from the 2012 Corine Land Cover (CLC) map and the 2014 farmers' graphical land parcel registration (Régistre Parcellaire Graphique) and the nine most significant classes have been included in the dataset. The number of pixels in each class is capped at 20,000. The data was obtained from the winning entry's GitHub repository [2], which includes the spatial coordinates for each pixel.
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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: other
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pretty_name: Tiselac
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size_categories:
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- 10K<n<100K
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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|Tiselac||
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|Category|Satellite|
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|Num. Examples|99,687|
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|Num. Channels|10|
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|Length|23|
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|Sampling Freq.|16 days|
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|Num. Classes|9|
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|License|Other|
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|Citations|[1] [2]|
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***TiSeLaC*** (Time Series Land Cover Classification) was created for the time series land cover classification challenge held in conjunction with the 2017 European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases [1]. It was generated from a time series of 23 Landsat 8 images of Reunion Island, acquired in 2014. At the 30m spatial resolution of Landsat 8 images, Reunion Island is covered by 2866 x 2633 pixels, however only 99,687 of these pixels are included in the dataset.
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Class labels were obtained from the 2012 Corine Land Cover (CLC) map and the 2014 farmers' graphical land parcel registration (Régistre Parcellaire Graphique) and the nine most significant classes have been included in the dataset. The number of pixels in each class is capped at 20,000. The data was obtained from the winning entry's GitHub repository [2], which includes the spatial coordinates for each pixel.
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