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

Datos Bibliográficos

ID15545602
AutoresLaurence Hawker (0000-0002-8317-7084, University of Bristol, autor de correspondencia), Peter Uhe (0000-0003-4644-8559, At Bristol), Luntadila Paulo (At Bristol), Jeison Sosa (0000-0002-4808-2063, At Bristol), James Savage (0000-0003-4807-0916, At Bristol), Christopher C Sampson (0000-0003-4094-2724, At Bristol), Christopher Sampson, Jeffrey Neal (0000-0001-5793-9594, At Bristol)
Año2022
Volumen17
Número2
Páginas024016-024016
Fecha de publicación2022-01-20
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironmental Research Letters (JOURNAL)
Identificadores de la revistaISSN: 1748-9326 • E-ISSN: 1748-9326
EditorialIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac4d4f
OpenAlexW4205693143
IdiomaEN
Citas recibidas24
Referencias citadas64

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

Cartography · Digital elevation model · Elevation (ballistics · Geography · Meteorology · Range (aeronautics · Remote sensing · Terrain · Computer Science · Cryospheric studies and observations · Environmental Science · Flood Risk Assessment and Management · Hydrology and Watershed Management Studies · Mathematics · Geology