Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation

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 Brazi...

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Main Authors: Costa, Marcus Vinícius Coelho Vieira da, Carvalho, Osmar Luiz Ferreira de, Orlandi, Alex Gois, Hirata, Issao, Albuquerque, Anesmar Olino de, Silva, Felipe Vilarinho e, Guimarães, Renato Fontes, Gomes, Roberto Arnaldo Trancoso, Carvalho Júnior, Osmar Abílio de
Format: Artigo
Language: Inglês
Published: MDPI 2021
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Online Access: https://repositorio.unb.br/handle/10482/41506
https://doi.org/10.3390/en14102960
https://orcid.org/0000-0002-5619-8525
https://orcid.org/0000-0003-1561-7583
https://orcid.org/0000-0002-9555-043X
https://orcid.org/0000-0003-4724-4064
https://orcid.org/0000-0002-0346-1684
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spelling ir-10482-415062021-07-29T13:46:43Z Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation Costa, Marcus Vinícius Coelho Vieira da Carvalho, Osmar Luiz Ferreira de Orlandi, Alex Gois Hirata, Issao Albuquerque, Anesmar Olino de Silva, Felipe Vilarinho e Guimarães, Renato Fontes Gomes, Roberto Arnaldo Trancoso Carvalho Júnior, Osmar Abílio de Energia solar Sensoriamento remoto 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. 2021-07-27T14:12:07Z 2021-07-27T14:12:07Z 2021 Artigo 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. https://repositorio.unb.br/handle/10482/41506 https://doi.org/10.3390/en14102960 https://orcid.org/0000-0002-5619-8525 https://orcid.org/0000-0003-1561-7583 https://orcid.org/0000-0002-9555-043X https://orcid.org/0000-0003-4724-4064 https://orcid.org/0000-0002-0346-1684 Inglês Acesso Aberto 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/). application/pdf MDPI
institution REPOSITORIO UNB
collection REPOSITORIO UNB
language Inglês
topic Energia solar
Sensoriamento remoto
spellingShingle Energia solar
Sensoriamento remoto
Costa, Marcus Vinícius Coelho Vieira da
Carvalho, Osmar Luiz Ferreira de
Orlandi, Alex Gois
Hirata, Issao
Albuquerque, Anesmar Olino de
Silva, Felipe Vilarinho e
Guimarães, Renato Fontes
Gomes, Roberto Arnaldo Trancoso
Carvalho Júnior, Osmar Abílio de
Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation
description 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.
format Artigo
author Costa, Marcus Vinícius Coelho Vieira da
Carvalho, Osmar Luiz Ferreira de
Orlandi, Alex Gois
Hirata, Issao
Albuquerque, Anesmar Olino de
Silva, Felipe Vilarinho e
Guimarães, Renato Fontes
Gomes, Roberto Arnaldo Trancoso
Carvalho Júnior, Osmar Abílio de
author_sort Costa, Marcus Vinícius Coelho Vieira da
title Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation
title_short Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation
title_full Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation
title_fullStr Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation
title_full_unstemmed Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation
title_sort remote sensing for monitoring photovoltaic solar plants in brazil using deep semantic segmentation
publisher MDPI
publishDate 2021
url https://repositorio.unb.br/handle/10482/41506
https://doi.org/10.3390/en14102960
https://orcid.org/0000-0002-5619-8525
https://orcid.org/0000-0003-1561-7583
https://orcid.org/0000-0002-9555-043X
https://orcid.org/0000-0003-4724-4064
https://orcid.org/0000-0002-0346-1684
_version_ 1710449470108336128
score 13.657419