ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

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 large-scale regions using big geodata. This study proposes workflow to generate 10 m map Europe with nine classes, ELULC-10, European Sentinel-1/-2 Landsat-8 images, well LUCAS reference samples. More than 200 K 300 situ surveys respectively, were employed inputs Google Earth Engine (GEE) platform perform classification an object-based segmentation algorithm Artificial Neural Network (ANN). A novel ANN-based preparation was also presented remove noisy samples from dataset. Additionally, improved several rule-based post-processing steps. The overall accuracy kappa coefficient 2021 ELULC-10 95.38% 0.94, respectively. detailed report accuracies provided, demonstrating accurate different such Woodland Cropland. Furthermore, post processing class identifications when compared current studies. could supply seasonal, yearly, change considering proposed integration complex machine learning algorithms large survey data.