Journal article
Uncertainty-aware interpretable deep learning for slum mapping and monitoring
- Abstract:
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Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having a direct impact on current residents and future generations. Slum mapping is one of the key problems concerning slums. Policymakers need to delineate slum settlements to make informed decisions about infrastructure development and allocation of aid. A wide variety of machine learning and deep learning methods have been applied to multispectral satellite images to map slums with ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 6.3MB, Terms of use)
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(Preview, Supplementary materials, pdf, 59.3KB, Terms of use)
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- Publisher copy:
- 10.3390/rs14133072
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Bibliographic Details
- Publisher:
- MDPI
- Journal:
- Remote Sensing More from this journal
- Volume:
- 14
- Issue:
- 13
- Article number:
- 3072
- Publication date:
- 2022-06-26
- Acceptance date:
- 2022-06-17
- DOI:
- EISSN:
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2072-4292
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1267930
- Local pid:
-
pubs:1267930
- Deposit date:
-
2022-08-05
Terms of use
- Copyright holder:
- Fisher et al.
- Copyright date:
- 2022
- Rights statement:
- ©2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
- Licence:
- CC Attribution (CC BY)
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