| Texto completo | |
| Autor(es): |
Benvenuto, Giovana A.
;
Negri, Rogerio G.
;
Colnago, Marilaine
;
Frery, Alejandro C.
;
Casaca, Wallace
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE Geoscience and Remote Sensing Letters; v. 22, p. 5-pg., 2025-01-01. |
| Resumo | |
Deforestation remains a critical global environmental concern, requiring effective monitoring approaches. This letter presents a novel attention-powered encoder-decoder neural network designed to address the key challenges in deforestation mapping, including scale heterogeneity, temporal dynamics, and computational efficiency. The proposed framework integrates a modified YOLOv8 backbone, spatial attention (SA) mechanisms, and a conjugated Dice-Focal loss function to enhance sensitivity to small- and large-scale deforestation patterns in temporal remote sensing (RS) data. An extensive battery of tests was conducted using two datasets from the Amazon region, exploring both single-image and image-pair inputs under varying contextual and class balance conditions. The results attest to substantial improvements in accuracy and computational efficiency compared to 13 deep learning (DL) methods, establishing the proposed model as effective in deforestation monitoring scenarios, where accuracy, scalability, and computational cost are simultaneously critical. (AU) | |
| Processo FAPESP: | 24/04718-8 - Aprimorando a Eficiência na Previsão Sucroalcooleira via Técnicas de Inteligência Artificial e Análise de Dados Integrada |
| Beneficiário: | Marilaine Colnago |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 24/01610-1 - Abordagens baseadas em redes neurais profundas para detecção de mudanças via séries de imagens de sensoriamento remoto |
| Beneficiário: | Rogério Galante Negri |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 23/14427-8 - Ciência de Dados para a Indústria Inteligente (CDII) |
| Beneficiário: | José Alberto Cuminato |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada |