There is a newer version of the record available.

Published September 28, 2020 | Version v0.1
Dataset Open

Continental Europe Digital Terrain Model at 30 m resolution based on GEDI and background layers

  • 1. OpenGeoHub foundation
  • 2. MultiOne


Digital Terrain Model for Continental Europe based on the three publicly available Digital Surface Models and predicted using an Ensemble Machine Learning (EML). EML was trainined using GEDI level 2B points (column "elev.lowestmode"): about 7 million GEDI points were overlaid vs NASADEM, AW3D, EU DEM, canopy height, tree cover and surface water cover maps, then an ensemble prediction model was fitted using random forest, GLM with Lasso, Cubist and GLMnet, and used to predict most probable terrain height (bare earth). Input layers used to train the EML include:

Detailed processing steps can be found here. Read more about the processing steps here. Training data set can be obtained in the file "gedi_elev.lowestmode_2019_eumap.RDS". Summary results of the model training (mlr::makeStackedLearner) report:

stats::lm(formula = f, data = d)

    Min      1Q  Median      3Q     Max 
-65.580  -2.630   0.648   3.120 181.769 

                Estimate Std. Error t value Pr(>|t|)    
(Intercept)   -4.1448129  0.4663283  -8.888  < 2e-16 ***
regr.ranger    0.2667469  0.0009676 275.677  < 2e-16 ***
regr.glmnet   -4.7183974  0.6038334  -7.814 5.54e-15 ***
regr.cvglmnet  4.6966219  0.6042481   7.773 7.69e-15 ***
regr.cubist    0.7643997  0.0012860 594.378  < 2e-16 ***
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 6.729 on 6757726 degrees of freedom
Multiple R-squared:  0.9996,	Adjusted R-squared:  0.9996 
F-statistic: 4.644e+09 on 4 and 6757726 DF,  p-value: < 2.2e-16

Which indicates that the elevation errors are in average (2/3rd of pixels) between +2-3 m. The output predicted terrain model includes the following two layers:

  • "dtm_elev.lowestmode_gedi.eml_m": mean estimate of the terrain elevation,
  • "dtm_elev.lowestmode_gedi.eml_md": standard deviation of the independently fitted stacked predictors quantifying the prediction uncertainty,

The predicted elevations are based on the GEDI data hence the reference water surface (WGS84 ellipsoid) is about 43 m higher than the sea water surface for a specific EU country. All GeoTIFFs were prepared using Integer format (elevations rounded to 1 m) and have been converted to Cloud Optimized GeoTIFFs using GDAL.

Disclaimer: The output DTM still shows forest canopy (overestimation of the terrain elevation) and has not been hydrologically corrected for spurious sinks and similar. This data set is continuously updated. To report a bug or suggest an improvement, please visit here. To register for updates please subscribe to:


This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (



Files (48.7 GB)

Name Size Download all
1.4 MB Preview Download
3.7 GB Preview Download
13.9 GB Preview Download
8.0 GB Preview Download
623.9 MB Preview Download
8.0 GB Preview Download
2.7 GB Preview Download
1.5 GB Download
998.2 MB Preview Download
3.5 GB Preview Download
1.3 GB Preview Download
4.3 GB Preview Download

Additional details

Related works

Is supplemented by
Dataset: 10.5281/zenodo.4058447 (DOI)


  • Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., ... & Jones, Z. M. (2016). mlr: Machine Learning in R. The Journal of Machine Learning Research, 17(1), 5938-5942.
  • Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., ... & Kommareddy, A. (2013). High-resolution global maps of 21st-century forest cover change. science, 342(6160), 850-853.
  • Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418-422.
  • Pflugmacher, D., Rabe, A., Peters, M., & Hostert, P. (2019). Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote sensing of environment, 221, 583-595.
  • Potapov, P., X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2020) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. In review
  • Takaku, J., & Tadono, T. (2017). Quality updates of 'AW3D'global DSM generated from ALOS PRISM. In 2017 IEEE International Geoscience and Remote Sensing Symposium (Igarss) (pp. 5666-5669). IEEE.