ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine
Por:
Mirmazloumi, S.M., Kakooei, M., Mohseni, F., Ghorbanian, A., Amani, M., Crosetto, M., Monserrat, O.
Publicada:
1 ene 2022
Categoría:
Earth and planetary sciences (miscellaneous)
Resumen:
Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over largescale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based postprocessing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Filiaciones:
Mirmazloumi, S.M.:
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Av. Gauss, Barcelona, Castelldefels, 7E-08860, Spain
Kakooei, M.:
Department of Computer Science, Chalmers University of Technology, Rännvägen 6, Göteborg, 41258, Sweden
Mohseni, F.:
Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, 19967 15433, Iran
Department of Technology and Society, Faculty of Engineering, Lund University, Lund, 221 00, Sweden
Ghorbanian, A.:
Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, 19967 15433, Iran
Department of Technology and Society, Faculty of Engineering, Lund University, Lund, 221 00, Sweden
Amani, M.:
Wood Environment & Infrastructure Solutions, Ottawa, ON CA K2E 7L5, Canada
Crosetto, M.:
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Av. Gauss, Barcelona, Castelldefels, 7E-08860, Spain
Monserrat, O.:
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Av. Gauss, Barcelona, Castelldefels, 7E-08860, Spain
Green Published, gold, All Open Access; Gold Open Access; Green Open Access
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