| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Pesca e Aquicultura. |
|
Data corrente: |
25/01/2024 |
|
Data da última atualização: |
25/01/2024 |
|
Tipo da produção científica: |
Artigo em Anais de Congresso |
|
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. |
|
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. |
|
Título: |
Detecting aquaculture with deep learning in a low-data setting. |
|
Ano de publicação: |
2023 |
|
Fonte/Imprenta: |
In: SIGKDD FRAGILE EARTH WORKSHOP, 2023, Long Beach. |
|
Idioma: |
Inglês |
|
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. |
|
Palavras-Chave: |
Attention; Contrastive learning; Convolutinal neural networks; Image classification; Image segmentation; Representation learning. |
|
Thesagro: |
Aquicultura; Sensoriamento Remoto. |
|
Thesaurus Nal: |
Aquaculture; Digital images; Neural networks; Remote sensing. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1161305/1/detecting-aquaculture-with-dee.pdf
|
|
Marc: |
LEADER 01980nam a2200373 a 4500
001 2161305
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Pesca e Aquicultura (CNPASA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 6 | |
| 3. |  | 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. Detecting aquaculture with deep learning in a low-data setting. In: SIGKDD FRAGILE EARTH WORKSHOP, 2023, Long Beach.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Pesca e Aquicultura. |
|    |
| 4. | | WU, J.; GUAN, K.; HAYEK, M.; RESTREPO-COUPE, N.; WIEDEMANN, K. T.; XU, X.; WEHR, R.; CHRISTOFFERSEN, B. O.; MIAO, G.; SILVA, R. da; ARAUJO, A. C. de; OLIVEIRA JUNIOR, R. C. de; CAMARGO, P. B.; MONSON, R. K.; HUETE, A. R.; SALESKA, S. R. Partitioning controls on Amazon forest photosynthesis between environmental and biotic factors at hourly to interannual timescales. Global Change Biology, v. 23, n. 3, p. 1240-1257, Mar. 2017.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Amazônia Oriental. |
|  |
| 5. | | YOUNG, N. D.; JEX, A. R.; LI, B.; LIU, S.; YANG, L.; XIONG, Z.; LI, Y.; CANTACESSI, C.; HALL, R. S.; XU, X.; CHEN, F.; WU, X.; ZERLOTINI, A.; OLIVEIRA, G.; HOFMANN, A.; ZHANG, G.; FANG, X.; KANG, Y.; CAMPBELL, B. E.; LOUKAS, A.; RANGANATHAN, S.; ROLLINSON, D.; RINALDI, G.; BRINDLEY, P. J.; YANG, H.; WANG, J.; WANG, J.; GASSER, R. B. Whole-genome sequence of Schistosoma haematobium. Nature Genetics, v. 44, n. 2, p. 221-225, Feb. 2012.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agricultura Digital. |
| |
| 6. | | MONTANARELLA, L.; MAY, W. B.; TONG, Y.; FONTANA, A.; KLIMANOV, A.; KONTOBOYTSEVA, A.; LOSS, A.; NOOV, B.; LABAZ, B.; SMRECZAK, B.; HONGGUANG, C.; CLERICI, C.; SANCHEZ, C. O.; PENG, D.; TIMOFEEVA, E.; GIASSON, E.; BAZARRADNAA, E.; WEI, F.; FONTES, F.; PEREIRA, G.; ERDOGAN, H. E.; HUSEIN, H. H.; SKRYLNYK, I.; SOBOCKÁ, J.; QU, J.; CLARKE, J. L.; WANG, J.; STUCHI, J. F.; KONYUSHKOVA, M.; SILVA, L. S. da; YAO, L.; VOROTYNTSEVA, L.; SOVETBEK, M.; BOLAÑOS-BENAVIDES, M. M.; ENTZ, M.; ZAKHAROVA, M.; ST. LUCE, M.; SCHELLENBERG, M. P.; SCHELLENBERG, M.; MIROSHNICHENKO, M.; CLARKE, N.; NONGHARNPITAK, N.; NYAMSAMBUU, N.; TURSUNOVNA, O. R.; CARFAGNO, P.; SCHENATO, R. B.; KAPUR, S.; WAN, S.; DYBDAL, S.; BALIUK, S.; KUYPER, T. W.; SHISHKOV, T. A.; BARON, V.; HETMANENKO, V.; CARDONA, W. A.; XU, X.; LIU, X.; GENG, X.; MA, X.; CHEN, X.; ZHANG, Y. Sustainable management of black soils: from practices to policies. In: FAO. Global status of black soils. Rome, 2022. cap. 4, p. 107-144.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Solos. |
| |
| Registros recuperados : 6 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
