@article {Khaldi20221377,
	title = {TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning},
	journal = {Earth System Science Data},
	volume = {14},
	number = {3},
	year = {2022},
	note = {cited By 0},
	pages = {1377-1411},
	abstract = {Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC 10.5281/zenodo.5913554;Currency sign, a smart open-source global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500gh{\texteuro}{\textasciimacron}m resolution, from 2000 to 2021, and was annotated using spatial-temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial-temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8gh{\texteuro}{\textasciimacron}d temporal granularity into monthly composites; (3) merging Terragh{\texteuro}{\textasciimacron}+gh{\texteuro}{\textasciimacron}Aqua data into a combined time series; and (4) extracting, at the pixel level, 6gh{\texteuro}{\textasciimacron}076gh{\texteuro}{\textasciimacron}531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial-temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing various machine learning models, including deep learning networks, to perform global LULC mapping. {\textcopyright} 2022 Rohaifa Khaldi et al.},
	keywords = {Land cover, Land use, Machine learning, MODIS, multispectral image, spatiotemporal analysis, Time series analysis},
	doi = {10.5194/essd-14-1377-2022},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127624906\&doi=10.5194\%2fessd-14-1377-2022\&partnerID=40\&md5=90f9e45c7b39e149ab387a54f4392d6a},
	author = {Khaldi, R. and Alcaraz-Segura, D. and Guirado, E. and Benhammou, Y. and El Afia, A. and Herrera, F. and Tabik, S.}
}
