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TimeSpec4LULC is archived in 30 different ZIP files owning the name of the 29 LULC classes (one class is divided into two files since it is too large). Within each ZIP file, there exists a set of seven CSV files, each one corresponding to one of the seven spectral bands. The naming of each file follows this structure: IdOfTheClass_NameOfTheClass_ModisBand.csv For example, for band 1 of the Barren Lands class, the filename is: 01_BarrenLands_MCD09A1b01.csv Inside each CSV file, rows represent the collected pixels for that class. The first 11 columns contain the following metadata: - “IdOfTheClass”: Id of the class. - “NameOfTheClass”: Name of the class. - “IdOfTheLevel0”: Id of the FAO-L0 (i.e., countries). - “IdOfTheLevel1”: Id of the FAO-L1 (i.e., departments, states, or provinces depending on the country). - “IdOfThePixel”: Id of the pixel. - “PurityOfThePixel”: Spatial and inter-annual consensus for this class across multiple land-cover products, i.e., Purity of the pixel. - “DataAvailability”: percentage of non-missing data per band throughout the time series. - “Index_GHM”: average of Global Human Modification index (gHM). - “Lat”: Latitude of the pixel center. - “Lon”: Longitude of the pixel center. - “.geo”: (Longitude, Latitude) of the pixel center with more precision. And, the last 223 columns contain the 223 monthly observations of the time series for one spectral band from 2002-07 to 2021-01. Along with the dataset, an Excel file named 'Countries_Departments_FAO-GAUL' containing the FAO-L0 and the FAO-L1 Ids and names (following the FAO-GAUL standards) is provided.
This research has been supported by DETECTOR (A-RNM-256-UGR18 Universidad de Granada/FEDER), LifeWatch SmartEcomountains (LifeWatch-2019-10-UGR-01 Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), BBVA DeepSCOP (Ayudas Fundación BBVA a Equipos de Investigación Científica 2018), Ramón y Cajal Programme (RYC-2015-18136), DeepL-ISCO (A-TIC-458-UGR18 Ministerio de Ciencia e Innovación/FEDER), SMART-DASCI (TIN2017-89517-P Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), BigDDL-CET (P18-FR-4961 Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), RESISTE (P18-RT-1927 Consejería de Economía, Conocimiento, y Universidad from the Junta de Andalucía/FEDER), and Ecopotential (641762 European Commission).
Land cover, Land use and land cover mapping, Time series, MODIS, Multispectral, Land use, Global dataset, Deep learning, Remote sensing, Google Earth Engine, Land use and land cover change, Modelling
Land cover, Land use and land cover mapping, Time series, MODIS, Multispectral, Land use, Global dataset, Deep learning, Remote sensing, Google Earth Engine, Land use and land cover change, Modelling
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