  Widely used European land cover maps such as CORINE are produced at medium
spatial resolutions (100 m) and rely on diverse data with complex workflows
requiring significant institutional capacity. We present a high resolution (10
m) land cover map (ELC10) of Europe based on a satellite-driven machine
learning workflow that is annually updatable. A Random Forest classification
model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover
Area frame Survey) dataset. Within the Google Earth Engine cloud computing
environment, the ELC10 map can be generated from approx. 700 TB of Sentinel
imagery within approx. 4 days from a single research user account. The map
achieved an overall accuracy of 90% across 8 land cover classes and could
account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of
the actual value. These accuracies are higher than that of CORINE (100 m) and
other 10-m land cover maps including S2GLC and FROM-GLC10. We found that
atmospheric correction of Sentinel-2 and speckle filtering of Sentinel-1
imagery had minimal effect on enhancing classification accuracy (< 1%).
However, combining optical and radar imagery increased accuracy by 3% compared
to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The conversion of
LUCAS points into homogenous polygons under the Copernicus module increased
accuracy by <1%, revealing that Random Forests are robust against contaminated
training data. Furthermore, the model requires very little training data to
achieve moderate accuracies - the difference between 5K and 50K LUCAS points is
only 3% (86 vs 89%). At 10-m resolution, the ELC10 map can distinguish detailed
landscape features like hedgerows and gardens, and therefore holds potential
for aerial statistics at the city borough level and monitoring property-level
environmental interventions (e.g. tree planting).
