Crop

This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The new generation Earth Observation (EO) satellites have been imaging the Earth frequently, completely and in high resolution. This introduces unprecedented opportunities to monitor the dynamics of any regions on our planet over time and revealing the constant flux that underpins the bigger picture of our World. 

This dataset is a subset of a bigger dataset [1], which originally comes from 46 geometrically and radiometrically corrected images taken by FORMOSAT-2 satellite. These images are corrected such that every pixel corresponds to the same geographic area on Earth. Each pixel represents an area of 64 square meter; with 1 million pixels per image, this results in an area of 64 square kilometer each. Each geographic area (x, y) (∼pixel) forms a time series of length of 46, showing the temporal evolution of that area.

There are 24 classes corresponding to what the land covers. Class label - class name in French and English translation@

1. mais: corn
2. ble: wheat
3. bati dense: dense building
4. bati indu: built indu
5. bati diffus: diffuse building
6. prairie temporaire: temporary meadow
7. feuillus: hardwood
8. friche: wasteland
9. jachere: jachere
10. soja: soy
11. eau: water
12. pre: pre
13. resineux: softwood
14. tournesol: sunflower
15. sorgho: sorghum
16. eucalyptus: eucalyptus
17. colza: rapeseed
18. mais ensillage: but drilling
19. orge: barley
20. pois: peas
21. peupliers: poplars
22. surface minerale: mineral surface
23. graviere: gravel
24. lac: lake

There is nothing to infer from the order of examples in the train and test set.

Data created by: C.W. Tan, G.I. Webb and F. Petitjean (see [1], [2]). Data edited by Hoang Anh Dau.

[1] Tan, Chang Wei, Geoffrey I. Webb, and François Petitjean. "Indexing and classifying gigabytes of time series under time warping." Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2017.
[2] http://bit.ly/SDM2017

Donator: F. Petitjean