Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh
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 water...
| Autores principales: | , , , , , , |
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| Formato: | Resumen |
| Lenguaje: | Inglés |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/127166 |
| _version_ | 1855533380001595392 |
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| author | Belton, Ben Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernández, Ricardo Khondker, Murshed-E-Jahan Ferriby, Hannah |
| author_browse | Ali, Hazrat Belton, Ben Ferriby, Hannah Haque, Mohammad Mahfujul Hernández, Ricardo Khondker, Murshed-E-Jahan Nejadhashemi, Amir Pouyan |
| author_facet | Belton, Ben Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernández, Ricardo Khondker, Murshed-E-Jahan Ferriby, Hannah |
| author_sort | Belton, Ben |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Abstract |
| id | CGSpace127166 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| record_format | dspace |
| spelling | CGSpace1271662025-01-09T16:57:51Z Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh Belton, Ben Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernández, Ricardo Khondker, Murshed-E-Jahan Ferriby, Hannah climate change food systems deltas 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. 2022-12-01 2023-01-16T09:09:27Z 2023-01-16T09:09:27Z Abstract https://hdl.handle.net/10568/127166 en Open Access application/pdf Belton, B., Haque, M. M., Ali, H., Nejadhashemi, A. P., Hernandez, R. A., Khondker, M. and Ferriby, H. 2022. Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh. Paper abstract submitted to the World Aquaculture Society, Singapore, 1 December 2022 |
| spellingShingle | climate change food systems deltas Belton, Ben Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernández, Ricardo Khondker, Murshed-E-Jahan Ferriby, Hannah Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh |
| title | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh |
| title_full | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh |
| title_fullStr | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh |
| title_full_unstemmed | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh |
| title_short | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh |
| title_sort | harnessing machine learning to estimate aquaculture s contributions to the economy of southwest bangladesh |
| topic | climate change food systems deltas |
| url | https://hdl.handle.net/10568/127166 |
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