Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1161305
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dc.contributor.authorGREENSTREET, L.
dc.contributor.authorFAN, J.
dc.contributor.authorPACHECO, F. S.
dc.contributor.authorBAI, Y.
dc.contributor.authorUMMUS, M. E.
dc.contributor.authorDORIA, C.
dc.contributor.authorBARROS, N. O.
dc.contributor.authorFORSBERG, B. R.
dc.contributor.authorXU, X.
dc.contributor.authorFLECKER, A.
dc.contributor.authorGOMES, C.
dc.date.accessioned2024-01-25T14:32:15Z-
dc.date.available2024-01-25T14:32:15Z-
dc.date.created2024-01-25
dc.date.issued2023
dc.identifier.citationIn: SIGKDD FRAGILE EARTH WORKSHOP, 2023, Long Beach.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1161305-
dc.descriptionAquaculture 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectImage segmentation
dc.subjectImage classification
dc.subjectAttention
dc.subjectContrastive learning
dc.subjectRepresentation learning
dc.subjectConvolutinal neural networks
dc.titleDetecting aquaculture with deep learning in a low-data setting.
dc.typeArtigo em anais e proceedings
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroAquicultura
dc.subject.nalthesaurusAquaculture
dc.subject.nalthesaurusRemote sensing
dc.subject.nalthesaurusDigital images
dc.subject.nalthesaurusNeural networks
riaa.ainfo.id1161305
riaa.ainfo.lastupdate2024-01-25
dc.contributor.institutionLAURA 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.
Aparece nas coleções:Artigo em anais de congresso (CNPASA)

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