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Global Mapping of Rivers and Gravel Bars [dataset] Open Access

This dataset comprises 469 tiles of 6x8 degrees which covers the non-polar world, ~89% of the globe. They give land-cover classes at a spatial resolution of 10 meters for rivers (class 1), lakes (class 2) and bars (class 3). These classes were determined using deep learning methods. Further, the tiles also have additional land cover classes for oceans (class 4), glaciated terrain (class 5), snow cover (class 6), cloud (class 7) and data gaps (class 8). These classes were derived from external metadata. The data is for the month of July 2021.

Descriptions

Resource type

Dataset

Contributors

Creator: Patrice Carbonneau ¹

¹ Durham University

Funder

Research methods

Deep learning and image processing

Other description

Full details and code can be found at:  https://github.com/PCdurham/Semantic_Classification_of_Rivers_and_Bars

Keyword

Deep learning
Image Processing
Global Land Cover

Subject

Location

Language

Cited in

Identifier

ark:/32150/r17w62f824x
doi:10.15128/r17w62f824x

Rights

Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC)

Publisher

Durham University

Date Created

17 October 2023

File Details

Depositor

N. Syrotiuk

Date Uploaded

16 October 2023, 18:10:26

Date Modified

17 October 2023, 15:10:23

Audit Status

Audits have not yet been run on this file.

Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 7655218565

Last modified: 2023:10:16 19:45:43+01:00

Filename: CarbonneauResearchData.zip

Original checksum: fd4685db19361b7cbc63132727e1a8b7