| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Costa, Marcus Vinícius Coelho Vieira da | - |
| dc.contributor.author | Carvalho, Osmar Luiz Ferreira de | - |
| dc.contributor.author | Orlandi, Alex Gois | - |
| dc.contributor.author | Hirata, Issao | - |
| dc.contributor.author | Albuquerque, Anesmar Olino de | - |
| dc.contributor.author | Silva, Felipe Vilarinho e | - |
| dc.contributor.author | Guimarães, Renato Fontes | - |
| dc.contributor.author | Gomes, Roberto Arnaldo Trancoso | - |
| dc.contributor.author | Carvalho Júnior, Osmar Abílio de | - |
| dc.date.accessioned | 2021-07-27T14:12:07Z | - |
| dc.date.available | 2021-07-27T14:12:07Z | - |
| dc.date.issued | 2021 | - |
| dc.identifier.citation | COSTA, Marcus Vinícius Coelho Vieira da et al. Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation. Energies, v. 14, n. 10, 2960, 2021. DOI: https://doi.org/10.3390/en14102960. Disponível em: https://www.mdpi.com/1996-1073/14/10/2960. Acesso em: 26 jul. 2021. | pt_BR |
| dc.identifier.uri | https://repositorio.unb.br/handle/10482/41506 | - |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | MDPI | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Energia solar | pt_BR |
| dc.subject.keyword | Sensoriamento remoto | pt_BR |
| dc.rights.license | Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | pt_BR |
| dc.identifier.doi | https://doi.org/10.3390/en14102960 | pt_BR |
| dc.description.abstract1 | Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach. | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-5619-8525 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0003-1561-7583 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-9555-043X | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0003-4724-4064 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-0346-1684 | pt_BR |
| Aparece nas coleções: | Artigos publicados em periódicos e afins
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