Data/hora: 08/06/2024 14:45:23

Biblioteca(s): | Embrapa Pesca e Aquicultura.
Data corrente: | 25/01/2024
Data da última atualização: | 25/01/2024
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: | http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1161305/1/detecting-aquaculture-with-dee.pdf
Registro original: | Embrapa Pesca e Aquicultura (CNPASA)
Exemplares: | CNPASA - (1329-1) - UPC - AA - DD - 2023 -  - 2023 - DIS