Part of MONSTER: https://arxiv.org/abs/2502.15122.

Tiselac
Category	Satellite
Num. Examples	99,687
Num. Channels	10
Length	23
Sampling Freq.	16 days
Num. Classes	9
License	Other
Citations	[1] [2]

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, sampled approximately every 16 days, acquired in 2014. This is a pixel level dataset, where each time series represents changing values for a single pixel. Ten time series features are provided for each pixel, seven surface reflectances covering visible and infrared frequencies and three indices derived from these bands - the Normalised Difference Vegetation Index, the Normalised Difference Water Index, and the Brightness Index. 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. Class labels were obtained from the 2012 Corine Land Cover (CLC) map and the 2014 farmers' graphical land parcel registration (Régistre Parcellaire Graphique - RPG) 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. The processed dataset consists of 99,687 multivariate time series each of length 23 (i.e., representing approximately one year of data per time series at a sampling period of approximately 16 days).

We provide training and testing splits designed to give spatial separation between the splits, which should lead to realistic estimations of the generalisation capability of trained models. We first divided the original pixel grid into a coarse grid, with each grid cell sized at 300 x 300 pixels, then computed the number of dataset pixels in each cell (the cell size). These cells are processed in descending order of size, and allocated to the fold with the fewest pixels.

[1] Dino Ienco. (2017). TiSeLaC : Time Series Land Cover Classification Challenge. https://sites.google.com/site/dinoienco/tiselac-time-series-land-cover-classification-challenge (via Internet Archive).

[2] Nicola Di Mauro, Antonio Vergari, Teresa M.A. Basile, Fabrizio G. Ventola, and Floriana Esposito. (2017). End-to-end learning of deep spatio-temporal representations for satellite image time series classification. In Proceedings of the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases.