Journal article
On transfer learning for building damage assessment from satellite imagery in emergency contexts
- Abstract:
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When a natural disaster occurs, humanitarian organizations need to be prompt, effective, and efficient to support people whose security is threatened. Satellite imagery offers rich and reliable information to support expert decision-making, yet its annotation remains labour-intensive and tedious. In this work, we evaluate the applicability of convolutional neural networks (CNN) in supporting building damage assessment in an emergency context. Despite data scarcity, we develop a deep learning ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, 2.4MB, Terms of use)
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- Publisher copy:
- 10.3390/rs14112532
Authors
Bibliographic Details
- Publisher:
- MDPI
- Journal:
- Remote Sensing More from this journal
- Volume:
- 14
- Issue:
- 11
- Article number:
- 2532
- Publication date:
- 2022-05-25
- Acceptance date:
- 2022-05-20
- DOI:
- EISSN:
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2072-4292
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1266066
- Local pid:
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pubs:1266066
- Deposit date:
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2022-06-30
Terms of use
- Copyright holder:
- Bouchard et al.
- Copyright date:
- 2022
- Rights statement:
- Copyright: © 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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