| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Carvalho, Osmar Luiz Ferreira de | - |
| dc.contributor.author | Carvalho Júnior, Osmar Abílio de | - |
| dc.contributor.author | Albuquerque, Anesmar Olino de | - |
| dc.contributor.author | Orlandi, Alex Gois | - |
| dc.contributor.author | Hirata, Issao | - |
| dc.contributor.author | Borges, Díbio Leandro | - |
| dc.contributor.author | Gomes, Roberto Arnaldo Trancoso | - |
| dc.contributor.author | Guimarães, Renato Fontes | - |
| dc.date.accessioned | 2023-10-16T15:47:25Z | - |
| dc.date.available | 2023-10-16T15:47:25Z | - |
| dc.date.issued | 2023-02-23 | - |
| dc.identifier.citation | CARVALHO, Osmar Luiz Ferreira deet al. A data-centric approach for wind plant instance-level segmentation using semantic segmentation and GIS. Remote Sensing, [S.l.], v. 15, n. 5, 2023. | pt_BR |
| dc.identifier.uri | http://repositorio2.unb.br/jspui/handle/10482/46680 | - |
| dc.language.iso | eng | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | A data-centric approach for wind plant instance-level segmentation using semantic segmentation and GIS | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.rights.license | (CC BY) Copyright: © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/license s/by/4.0/). | pt_BR |
| dc.identifier.doi | https://doi.org/10.3390/rs15051240 | pt_BR |
| dc.description.abstract1 | Wind energy is one of Brazil’s most promising energy sources, and the rapid growth of
wind plants has increased the need for accurate and efficient inspection methods. The current onsite
visits, which are laborious and costly, have become unsustainable due to the sheer scale of wind
plants across the country. This study proposes a novel data-centric approach integrating semantic
segmentation and GIS to obtain instance-level predictions of wind plants by using free orbital
satellite images. Additionally, we introduce a new annotation pattern, which includes wind turbines
and their shadows, leading to a larger object size. The elaboration of data collection used the
panchromatic band of the China–Brazil Earth Resources Satellite (CBERS) 4A, with a 2-m spatial
resolution, comprising 21 CBERS 4A scenes and more than 5000 wind plants annotated manually.
This database has 5021 patches, each with 128 × 128 spatial dimensions. The deep learning model
comparison involved evaluating six architectures and three backbones, totaling 15 models. The
sliding windows approach allowed us to classify large areas, considering different pass values to
obtain a balance between performance and computational time. The main results from this study
include: (1) the LinkNet architecture with the Efficient-Net-B7 backbone was the best model,
achieving an intersection over union score of 71%; (2) the use of smaller stride values improves the
recognition process of large areas but increases computational power, and (3) the conversion of
raster to polygon in GIS platforms leads to highly accurate instance-level predictions. This entire
pipeline can be easily applied for mapping wind plants in Brazil and be expanded to other regions
worldwide. With this approach, we aim to provide a cost-effective and efficient solution for
inspecting and monitoring wind plants, contributing to the sustainability of the wind energy sector
in Brazil and beyond. | pt_BR |
| dc.contributor.affiliation | Universidade de Brasília, Departamento de Engenharia Elétrica | pt_BR |
| dc.contributor.affiliation | Universidade de Brasília, Departamento de Geografia | pt_BR |
| dc.contributor.affiliation | Universidade de Brasília, Departamento de Geografia | pt_BR |
| dc.contributor.affiliation | Universidade de Brasília, Departamento de Geografia | pt_BR |
| dc.contributor.affiliation | Agência Nacional de Energia Elétrica, Superintendencia da Gestão da Informação | pt_BR |
| dc.contributor.affiliation | Agência Nacional de Energia Elétrica, Superintendencia da Gestão da Informação | pt_BR |
| dc.contributor.affiliation | Universidade de Brasília, Departamento de Ciência da Computação | pt_BR |
| dc.contributor.affiliation | Universidade de Brasília, Departamento de Geografia | pt_BR |
| dc.contributor.affiliation | Universidade de Brasília, Departamento de Geografia | pt_BR |
| dc.description.unidade | Faculdade de Tecnologia (FT) | pt_BR |
| dc.description.unidade | Departamento de Engenharia Elétrica (FT ENE) | pt_BR |
| dc.description.unidade | Instituto de Ciências Humanas (ICH) | pt_BR |
| dc.description.unidade | Departamento de Geografia (ICH GEA) | pt_BR |
| dc.description.unidade | Instituto de Ciências Exatas (IE) | pt_BR |
| dc.description.unidade | Departamento de Ciência da Computação (IE CIC) | pt_BR |
| Aparece nas coleções: | Artigos publicados em periódicos e afins
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