A 30m global map of elevation with forests and buildings removed

Laurence Hawker, Peter Uhe, Luntadila Paulo, Jeison Sosa, James Savage, Christopher Sampson, Jeffrey Neal

Research output: Contribution to journalArticle (Academic Journal)peer-review

79 Citations (Scopus)
286 Downloads (Pure)


Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation data contains forest and building artifacts that limit its usefulness for applications that require precise terrain heights, in particular flood simulation. Here, we use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (∼30 m) grid spacing. We train our correction algorithm on a unique set of reference elevation data from 12 countries, covering a wide range of climate zones and urban extents. Hence, this approach has much wider applicability compared to previous DEMs trained on data from a single country. Our method reduces mean absolute vertical error in built-up areas from 1.61 to 1.12 m, and in forests
from 5.15 to 2.88 m. The new elevation map is more accurate than existing global elevation maps and will strengthen applications and models where high quality global terrain information is required.
Original languageEnglish
Article number024016
JournalEnvironmental Research Letters
Issue number2
Publication statusPublished - 3 Feb 2022

Bibliographical note

Funding Information:
L H and J N were funded by a joint Natural Environment Research Council (NERC) and Vietnam National Foundation for Science and Technology Development (NAFOSTED) project under Grant No. NE/S3003061/1.

Publisher Copyright:
© 2022 The Author(s). Published by IOP Publishing Ltd.


  • digital elevation model
  • bare-earth
  • terrain
  • remote sensing
  • machine learning


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