Binary classification of the Kinshasa and Bandundu provinces in the Democratic Republic of the Congo — settled versus non settled
Binary classification of the Kinshasa and Bandundu provinces in the Democratic Republic of the Congo — settled versus non settled
This dataset was created based on a settlement layer produced by the Oak Ridge National Laboratory using feature extraction from high-resolution imagery for population modelling work undertaken in the Kinshasa and Kongo-Central provinces in the Democratic Republic of the Congo. The settlement layer consists of settlement polygons of approximately 7 meters resolution. The polygons were rasterized based on a reference grid with a resolution of 3 arc-seconds, approximately 90 meters. The presence of at least one settlement polygon designated a settled cell. We thank the Oak Ridge National Laboratory and the Bill and Melinda Gates Foundation for the support.
We would also like to extend our gratitude to Eric M. Webber and Amy N. Rose at the Oak Ridge National Laboratory and Io Blair-Freese at the Bill and Melinda Gates Foundation.
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
(2019)
Binary classification of the Kinshasa and Bandundu provinces in the Democratic Republic of the Congo — settled versus non settled.
Zenodo
doi:10.5281/zenodo.3562191
[Dataset]
Abstract
This dataset was created based on a settlement layer produced by the Oak Ridge National Laboratory using feature extraction from high-resolution imagery for population modelling work undertaken in the Kinshasa and Kongo-Central provinces in the Democratic Republic of the Congo. The settlement layer consists of settlement polygons of approximately 7 meters resolution. The polygons were rasterized based on a reference grid with a resolution of 3 arc-seconds, approximately 90 meters. The presence of at least one settlement polygon designated a settled cell. We thank the Oak Ridge National Laboratory and the Bill and Melinda Gates Foundation for the support.
We would also like to extend our gratitude to Eric M. Webber and Amy N. Rose at the Oak Ridge National Laboratory and Io Blair-Freese at the Bill and Melinda Gates Foundation.
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More information
Published date: 4 December 2019
Identifiers
Local EPrints ID: 472305
URI: http://eprints.soton.ac.uk/id/eprint/472305
PURE UUID: 4949905e-6d98-422d-9305-00ce4be31f46
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Date deposited: 30 Nov 2022 18:04
Last modified: 01 Aug 2024 01:56
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Contributors
Contributor:
Gianluca Boo
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