Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning.

Autor: Benhammou Y; Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071, Granada, Spain. yassir.benhammou@lifewatch.eu.; Systems Analysis and Modeling for Decision Support Laboratory, Higher National School of Applied Sciences of Berrechid, Hassan 1st University, Berrechid, 218, Morocco. yassir.benhammou@lifewatch.eu.; LifeWatch-ERIC ICT Core, 41071, Seville, Spain. yassir.benhammou@lifewatch.eu., Alcaraz-Segura D; Department of Botany, Faculty of Science, University of Granada, 18071, Granada, Spain. dalcaraz@ugr.es.; iEcolab, Inter-University Institute for Earth System Research, University of Granada, 18006, Granada, Spain. dalcaraz@ugr.es.; Andalusian Center for Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04120, Almería, Spain. dalcaraz@ugr.es., Guirado E; Andalusian Center for Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04120, Almería, Spain. e.guirado@ual.es.; Multidisciplinary Institute for Environment Studies 'Ramon Margalef', University of Alicante, San Vicente del Raspeig, 03690, Alicante, Spain. e.guirado@ual.es., Khaldi R; Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071, Granada, Spain.; LifeWatch-ERIC ICT Core, 41071, Seville, Spain., Achchab B; Systems Analysis and Modeling for Decision Support Laboratory, Higher National School of Applied Sciences of Berrechid, Hassan 1st University, Berrechid, 218, Morocco., Herrera F; Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071, Granada, Spain., Tabik S; Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI, University of Granada, 18071, Granada, Spain. siham@ugr.es.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2022 Nov 09; Vol. 9 (1), pp. 681. Date of Electronic Publication: 2022 Nov 09.
DOI: 10.1038/s41597-022-01775-8
Abstrakt: Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.
(© 2022. The Author(s).)
Databáze: MEDLINE
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