Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
Department of Botany, Faculty of Science, University of Granada, 18071 Granada, Spain
ENSIAS, Mohammed V University, Rabat, 10170, Morocco
Department of Botany, Faculty of Science, University of Granada, 18071 Granada, Spain
Andalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04120, Almería, Spain
iEcolab, Inter-University Institute for Earth System Research, University of Granada, 18006 Granada, Spain
Emilio Guirado
Multidisciplinary Institute for Environment Studies Ramón Margalef, University of Alicante, San Vicente del Raspeig, 03690, Spain
Yassir Benhammou
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
ENSA, Hassan I University, Berrechid, 218, Morocco
Abdellatif El Afia
ENSIAS, Mohammed V University, Rabat, 10170, Morocco
Francisco Herrera
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
Siham Tabik
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071, Granada, Spain
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Total article views: 1,816 (including HTML, PDF, and XML)
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1,322
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1,112
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HTML: 666
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Viewed (geographical distribution)
Total article views: 2,928 (including HTML, PDF, and XML)
Thereof 2,748 with geography defined
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Total article views: 1,816 (including HTML, PDF, and XML)
Thereof 1,736 with geography defined
and 80 with unknown origin.
Total article views: 1,112 (including HTML, PDF, and XML)
Thereof 1,012 with geography defined
and 100 with unknown origin.
This dataset with millions of 22-year time series for seven spectral bands was built by merging Terra and Aqua satellite data and annotated for 29 LULC classes by spatial–temporal agreement across 15 global LULC products. The mean F1 score was 96 % at the coarsest classification level and 87 % at the finest one. The dataset is born to develop and evaluate machine learning models to perform global LULC mapping given the disagreement between current global LULC products.
This dataset with millions of 22-year time series for seven spectral bands was built by merging...
