TimeSpec4LULC: a global multispectral time series database for training LULC mapping models with machine learning

Autor: Rohaifa Khaldi, Domingo Alcaraz-Segura, Emilio Guirado, Yassir Benhammou, Abdellatif El Afia, Francisco Herrera, Siham Tabik
Přispěvatelé: Universidad de Alicante. Instituto Multidisciplinar para el Estudio del Medio 'Ramón Margalef'
Rok vydání: 2022
Předmět:
Zdroj: RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
Digibug. Repositorio Institucional de la Universidad de Granada
Consorcio Madroño
ISSN: 1866-3516
DOI: 10.5194/essd-14-1377-2022
Popis: This work was partially supported by DETECTOR (grant no. A-RNM-256-UGR18, Universidad de Granada/FEDER), LifeWatch SmartEcoMountains (grant no. LifeWatch-2019-10-UGR-01, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), BBVA DeepSCOP (Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018), DeepL-ISCO (grant no. A-TIC-458-UGR18, Ministerio de Ciencia e Innovacion/FEDER), SMART-DASCI (grant no. TIN2017-89517-P, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), BigDDL-CET (grant no. P18-FR-4961, Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER), RESISTE (grant no. P18-RT-1927, Consejeria de Economia, Conocimiento y Universidad from the Junta de Andalucia/FEDER), Ecopotential (grant no. 641762, European Commission), PID2020-119478GB-I00, the Conselleria de Educacion, Cultura y Deporte de la Generalitat Valenciana, the European Social Fund (grant no. APOSTD/2021/188), the European Research Council (ERC grant no. 647038/BIODESERT), and the Group on Earth Observations and Google Earth Engine (Essential Biodiversity Variables -ScaleUp project).
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 (https://doi.org/10.5281/zenodo.5913554; Khaldi et al., 2022), a smart opensource 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 500m 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 8 d temporal granularity into monthly composites; (3) merging TerraCAqua data into a combined time series; and (4) extracting, at the pixel level, 6 076 531 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.
DETECTOR (Universidad de Granada/FEDER) A-RNM-256-UGR18
LifeWatch SmartEcoMountains (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER)
LifeWatch-2019-10-UGR-01
BBVA DeepSCOP (Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica 2018)
DeepL-ISCO (Ministerio de Ciencia e Innovacion/FEDER) A-TIC-458-UGR18
SMART-DASCI (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER) TIN2017-89517-P
BigDDL-CET (Ministerio de Ciencia e Innovacion/Universidad de Granada/FEDER) P18-FR-4961
RESISTE (Consejeria de Economia, Conocimiento y Universidad from the Junta de Andalucia/FEDER) P18-RT-1927
European Commission 641762
Conselleria de Educacion, Cultura y Deporte de la Generalitat Valenciana
European Social Fund (ESF) APOSTD/2021/188
European Research Council (ERC)
European Commission 647038/BIODESERT
Group on Earth Observations and Google Earth Engine (Essential Biodiversity Variables -ScaleUp project) PID2020-119478GB-I00
Databáze: OpenAIRE
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