GOODD, a Global Dataset of More than 38,000 Georeferenced Dams. Mulligan, M.; van Soesbergen , A.; and Sáenz, L. Scientific Data, 7(1):31, January, 2020.
doi  abstract   bibtex   
By presenting the most comprehensive GlObal geOreferenced Database of Dams to date containing more than 38,000 dams as well as their associated catchments, we enable new and improved global analyses of the impact of dams on society and environment and the impact of environmental change (for example land use and climate change) on the catchments of dams. This paper presents the development of the global database through systematic digitisation of satellite imagery globally by a small team and highlights the various approaches to bias estimation and to validation of the data. The following datasets are provided (a) raw digitised coordinates for the location of dam walls (that may be useful for example in machine learning approaches to dam identification from imagery), (b) a global vector file of the watershed for each dam.
@article{mulliganGOODDGlobalDataset2020,
  title = {{{GOODD}}, a Global Dataset of More than 38,000 Georeferenced Dams},
  author = {Mulligan, Mark and {van Soesbergen}, Arnout and S{\'a}enz, Leonardo},
  year = {2020},
  month = jan,
  volume = {7},
  pages = {31},
  issn = {2052-4463},
  doi = {10.1038/s41597-020-0362-5},
  abstract = {By presenting the most comprehensive GlObal geOreferenced Database of Dams to date containing more than 38,000 dams as well as their associated catchments, we enable new and improved global analyses of the impact of dams on society and environment and the impact of environmental change (for example land use and climate change) on the catchments of dams. This paper presents the development of the global database through systematic digitisation of satellite imagery globally by a small team and highlights the various approaches to bias estimation and to validation of the data. The following datasets are provided (a) raw digitised coordinates for the location of dam walls (that may be useful for example in machine learning approaches to dam identification from imagery), (b) a global vector file of the watershed for each dam.},
  copyright = {2020 The Author(s)},
  journal = {Scientific Data},
  keywords = {~INRMM-MiD:z-QW7L23WY,geospatial,global-scale,information-systems,open-data,water-reservoir-management,water-resources},
  language = {en},
  lccn = {INRMM-MiD:z-QW7L23WY},
  number = {1}
}
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