Continental-scale land cover mapping at 10 m resolution over Europe (ELC10)
Zander S. Venter, Markus A.K. Sydenham

TL;DR
This paper introduces ELC10, a high-resolution (10 m) land cover map of Europe created using satellite data and machine learning, achieving high accuracy and rapid update capability, surpassing existing maps like CORINE.
Contribution
The study presents a scalable, accurate, and annually updatable land cover mapping workflow at 10 m resolution for Europe using Sentinel data and Random Forests.
Findings
Achieved 90% overall accuracy across 8 land cover classes.
Combining optical and radar data improved accuracy by 10%.
The model requires minimal training data to reach high accuracy.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
