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ZENODO
Dataset . 2018
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2018
License: CC BY
Data sources: Datacite
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Authors: TSML Archive Maintainers;
Abstract

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@ mais: corn ble: wheat bati dense: dense building bati indu: built indu bati diffus: diffuse building prairie temporaire: temporary meadow feuillus: hardwood friche: wasteland jachere: jachere soja: soy eau: water pre: pre resineux: softwood tournesol: sunflower sorgho: sorghum eucalyptus: eucalyptus colza: rapeseed mais ensillage: but drilling orge: barley pois: peas peupliers: poplars surface minerale: mineral surface graviere: gravel 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

Keywords

classification, univariate, ucr archive, tsml archive, time series

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average