Articles | Volume 14, issue 3
https://doi.org/10.5194/essd-14-1377-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-1377-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning
Rohaifa Khaldi
CORRESPONDING AUTHOR
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
Domingo Alcaraz-Segura
CORRESPONDING AUTHOR
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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Short summary
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...
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