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

Abstract 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…

• NF
  National Foundation for Science and Technology Development
• SR
  Sight Research UK

  Awards: NE/S006079/1, NE/S003061/1
• NE
  Natural Environment Research Council

  Awards: NE/S006079/1, NE/S003061/1