Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning
Metadatos
Afficher la notice complèteAuteur
Benhammou, Yassir; Alcaraz Segura, Domingo; Khaldi, Rohaifa; Herrera Triguero, Francisco; Tabik, SihamEditorial
Nature
Date
2022-11-09Referencia bibliográfica
Benhammou, Y... [et al.]. Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning. Sci Data 9, 681 (2022). [https://doi.org/10.1038/s41597-022-01775-8]
Patrocinador
Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program LifeWatch-2019-10-UGR-01; LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8 LifeWatch-2019-10-UGR-01 WP-7 LifeWatch-2019-10-UGR-01 WP-4; European Research Council (ERC); European Commission 647038; Center for Forestry Research & Experimentation (CIEF) APOSTD/2021/188 A-RNM-256-UGR18 A-TIC-458-UGR18 PID2020-119478GB-I00 P18-FR-4961Résumé
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.