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| 8. |  | VICTORIA, R. L.; MARTINELLI, L. A.; TRIVELIN, P. C. O.; FORSBERG, B. R.; RICHEY, J. E.; DEVOL, A. H. The use of stable isotopes in studies of nutrient cycling: carbon isotope composition of Amazon varzea sediments. Biotropica, v.24, n.2b, p.240-249, 1992.| Biblioteca(s): Embrapa Pantanal. |
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| 16. | | ASPEREN, H. V.; WARNEKE, T.; ARAUJO, A. C. de; FORSBERG, B.; FERREIRA, S.; ALVES-OLIVEIRA, J.; OLIVEIRA, L. R. de; XAVIER, T. de L.; SÁ, M.; TEIXEIRA, P.; PIRES, E.; MOURA, V.; KOMIYA, S.; BOTIA, S.; JONES, S.; LAVRIČ, J.; TRUMBORE, S.; NOTHOLT, J. Tropical forest CH4 budget: the importance of local hotspots In: EGU GENERAL ASSEMBLY, 2022, Viena. Programme. [S.l.]: EGU, 2022.| Biblioteca(s): Embrapa Amazônia Oriental. |
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| Registros recuperados : 16 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Pesca e Aquicultura. |
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Data corrente: |
25/01/2024 |
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Data da última atualização: |
25/01/2024 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
GREENSTREET, L.; FAN, J.; PACHECO, F. S.; BAI, Y.; UMMUS, M. E.; DORIA, C.; BARROS, N. O.; FORSBERG, B. R.; XU, X.; FLECKER, A.; GOMES, C. |
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Afiliação: |
LAURA GREENSTREET, CORNELL UNIVERSITY; JOSHUA FAN, CORNELL UNIVERSITY; FELIPE SIQUEIRA PACHECO, CORNELL UNIVERSITY; YIWEI BAI, CORNELL UNIVERSITY; MARTA EICHEMBERGER UMMUS, CNPASA; CAROLINA DORIA, UNIVERSIDADE FEDERAL DE RONDÔNIA; NATHAN OLIVEIRA BARROS, UNIVERSIDADE FEDERAL DE JUIZ DE FORA; BRUCE R. FORSBERG, INPA; XIANGTAO XU, CORNELL UNIVERSITY; ALEXANDER FLECKER, CORNELL UNIVERSITY; CARLA GOMES, CORNELL UNIVERSITY. |
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Título: |
Detecting aquaculture with deep learning in a low-data setting. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: SIGKDD FRAGILE EARTH WORKSHOP, 2023, Long Beach. |
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Idioma: |
Inglês |
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Conteúdo: |
Aquaculture is growing rapidly in the Amazon basin and detailed spatial information is needed to understand the trade-offs between food production, economic development, and environmental impacts. Large open-source datasets of medium resolution satellite imagery offer the potential for mapping a variety of infrastructure, including aquaculture ponds. However, there are many challenges utilizing this data, including few labelled examples, class imbalance, and spatial bias. We find previous rule-based methods for mapping aquaculture perform poorly in the Amazon. By incorporating temporal information through percentile data, we show deep learning models can outperform previous methods by as much as 15% with as few as 300 labelled examples. Further, generalization to unseen regions can be improved by incorporating segmentation information through masked pooling and using contrastive pretraining to harness large quantities of unlabelled data. |
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Palavras-Chave: |
Attention; Contrastive learning; Convolutinal neural networks; Image classification; Image segmentation; Representation learning. |
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Thesagro: |
Aquicultura; Sensoriamento Remoto. |
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Thesaurus NAL: |
Aquaculture; Digital images; Neural networks; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1161305/1/detecting-aquaculture-with-dee.pdf
|
|
Marc: |
LEADER 01980nam a2200373 a 4500
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005 2024-01-25
008 2023 bl uuuu u00u1 u #d
100 1 $aGREENSTREET, L.
245 $aDetecting aquaculture with deep learning in a low-data setting.$h[electronic resource]
260 $aIn: SIGKDD FRAGILE EARTH WORKSHOP, 2023, Long Beach.$c2023
520 $aAquaculture is growing rapidly in the Amazon basin and detailed spatial information is needed to understand the trade-offs between food production, economic development, and environmental impacts. Large open-source datasets of medium resolution satellite imagery offer the potential for mapping a variety of infrastructure, including aquaculture ponds. However, there are many challenges utilizing this data, including few labelled examples, class imbalance, and spatial bias. We find previous rule-based methods for mapping aquaculture perform poorly in the Amazon. By incorporating temporal information through percentile data, we show deep learning models can outperform previous methods by as much as 15% with as few as 300 labelled examples. Further, generalization to unseen regions can be improved by incorporating segmentation information through masked pooling and using contrastive pretraining to harness large quantities of unlabelled data.
650 $aAquaculture
650 $aDigital images
650 $aNeural networks
650 $aRemote sensing
650 $aAquicultura
650 $aSensoriamento Remoto
653 $aAttention
653 $aContrastive learning
653 $aConvolutinal neural networks
653 $aImage classification
653 $aImage segmentation
653 $aRepresentation learning
700 1 $aFAN, J.
700 1 $aPACHECO, F. S.
700 1 $aBAI, Y.
700 1 $aUMMUS, M. E.
700 1 $aDORIA, C.
700 1 $aBARROS, N. O.
700 1 $aFORSBERG, B. R.
700 1 $aXU, X.
700 1 $aFLECKER, A.
700 1 $aGOMES, C.
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Embrapa Pesca e Aquicultura (CNPASA) |
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