Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh
| cg.contribution.worldfishauthor | Belton, B. | en_US |
| cg.contribution.worldfishauthor | Ali, H. | en_US |
| cg.contribution.worldfishauthor | Khondker, M. | en_US |
| cg.contributor.affiliation | Michigan State University | en_US |
| cg.contributor.affiliation | WorldFish | en_US |
| cg.contributor.affiliation | Bangladesh Agricultural University | en_US |
| cg.contributor.affiliation | International Center for Tropical Agriculture | en_US |
| cg.contributor.initiative | Asian Mega-Deltas | en_US |
| cg.coverage.country | Bangladesh | en_US |
| cg.coverage.region | Southern Asia | en_US |
| cg.creator.id | Hazrat Ali: 0000-0002-6303-1336 | en_US |
| cg.creator.id | Murshed-E-Jahan Khondker: 0000-0001-9933-8631 | en_US |
| cg.description.theme | Aquaculture | en_US |
| cg.identifier.status | Open access | en_US |
| cg.identifier.url | https://cgspace.cgiar.org/handle/10568/127166 | en_US |
| cg.subject.actionArea | Resilient Agrifood Systems | en_US |
| cg.subject.agrovoc | climate change | en_US |
| cg.subject.agrovoc | deltas | en_US |
| cg.subject.agrovoc | food systems | en_US |
| cg.subject.agrovoc | goal 1 no poverty | en_US |
| cg.subject.agrovoc | goal 2 zero hunger | en_US |
| cg.subject.agrovoc | goal 13 climate action | en_US |
| cg.subject.agrovoc | goal 15 life on land | en_US |
| cg.subject.agrovoc | goal 8 decent work and economic growth | en_US |
| cg.subject.sdg | SDG 1 - No poverty | en_US |
| cg.subject.sdg | SDG 2 - Zero hunger | en_US |
| cg.subject.sdg | SDG 8 - Decent work and economic growth | en_US |
| cg.subject.sdg | SDG 13 - Climate action | en_US |
| cg.subject.sdg | SDG 15 - Life on land | en_US |
| dc.creator | Belton, B. | en_US |
| dc.creator | Haque, M. | en_US |
| dc.creator | Ali, H. | en_US |
| dc.creator | Nejadhashemi, A. | en_US |
| dc.creator | Hernandez, R. | en_US |
| dc.creator | Khondker, M. | en_US |
| dc.creator | Ferriby, H. | en_US |
| dc.date.accessioned | 2023-06-10T11:01:13Z | |
| dc.date.available | 2023-06-10T11:01:13Z | |
| dc.date.issued | 2022 | en_US |
| dc.description.abstract | Abstract accepted for presentation at the Annual Meeting of the World Aquaculture Society held in Singapore on 29 November to 2 December 2022. The presentation detailed the use of machine learning techniques to extract information from freely available satellite images and estimate the area of waterbodies used for aquaculture in seven districts in southern Bangladesh, one of country’s most important aquaculture zones producing fish for domestic markets and crustaceans for export. | en_US |
| dc.format | en_US | |
| dc.identifier.citation | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12348/5521 | |
| dc.language | en | en_US |
| dc.publisher | WorldFish (WF) | en_US |
| dc.rights | No known copyright restrictions | en_US |
| dc.title | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh | en_US |
| dc.type | Other (Abstract) | en_US |
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