Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks

Detalhes bibliográficos
Autor(a) principal: Vestena, Kauê de Moraes
Data de Publicação: 2022
Outros Autores: Santos, Daniel Rodrigues dos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anuário do Instituto de Geociências (Online)
Texto Completo: https://revistas.ufrj.br/index.php/aigeo/article/view/46008
Resumo: Urbanization brought a lot of pollution-related issues that are mitigable by the presence of urban vegetation. Therefore, it is necessary to map vegetation in urban areas, to assist the planning and implementation of public policies. As a technology presented in the last decades, the so-called Terrestrial Mobile Mapping Systems - TMMS, are capable of providing cost and time effective data acquisition, they are composed primarily by a Navigation System and an Imaging System, both mounted on a rigid platform, attachable to the top ofa ground vehicle. In this context, it is proposed the creation of a low-cost TMMS, which has the feature of imaging in the near-infrared (NIR) where the vegetation is highly discriminable. After the image acquisition step, it becomes necessary for the semantic segmentation of vegetation and non-vegetation. The current state of the art algorithms in semantic segmentation scope are the Convolutional Neural Networks - CNNs. In this study, CNNs were trained and tested, reaching a mean value of 83% for the Intersection Over Union (IoU) indicator. From the results obtained, which demonstrated good performance for the trained neural network, it is possible to concludethat the developed TMMS is suitable to capture data regarding urban vegetation.
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spelling Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural NetworksMobile geospatial data acquisition systems; NIR Imaging; Semantic segmentationUrbanization brought a lot of pollution-related issues that are mitigable by the presence of urban vegetation. Therefore, it is necessary to map vegetation in urban areas, to assist the planning and implementation of public policies. As a technology presented in the last decades, the so-called Terrestrial Mobile Mapping Systems - TMMS, are capable of providing cost and time effective data acquisition, they are composed primarily by a Navigation System and an Imaging System, both mounted on a rigid platform, attachable to the top ofa ground vehicle. In this context, it is proposed the creation of a low-cost TMMS, which has the feature of imaging in the near-infrared (NIR) where the vegetation is highly discriminable. After the image acquisition step, it becomes necessary for the semantic segmentation of vegetation and non-vegetation. The current state of the art algorithms in semantic segmentation scope are the Convolutional Neural Networks - CNNs. In this study, CNNs were trained and tested, reaching a mean value of 83% for the Intersection Over Union (IoU) indicator. From the results obtained, which demonstrated good performance for the trained neural network, it is possible to concludethat the developed TMMS is suitable to capture data regarding urban vegetation.Universidade Federal do Rio de JaneiroCoordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESVestena, Kauê de MoraesSantos, Daniel Rodrigues dos2022-05-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/4600810.11137/1982-3908_2022_45_46008Anuário do Instituto de Geociências; Vol 45 (2022)Anuário do Instituto de Geociências; Vol 45 (2022)1982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/46008/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/46008/16481/*ref*/Badrinarayanan, V., Kendall, A. & Cipolla, R. 2017, ‘Segnet: A deep convolutional encoder-decoder architecture for image segmentation’, IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 12, pp. 2481-95. https://doi.org/10.1109/TPAMI.2016.2644615 Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A.L. 2018, ‘Deep Lab: Semantic image segmentation with deep convolutional nets and fully connected CRFS’, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834-48. https://doi.org/10.1109/TPAMI.2017.2699184 El-Sheimy, N. 2005, ‘An overview of mobile mapping systems’, Proceedings of the FIG Working Week, pp. 16-21. Fawcett, T. 2006, ‘An introduction to ROC analysis’, Pattern recognition letters, vol. 27, no. 8, pp. 861-74. https://doi.org/10.1016/j.patrec.2005.10.010 Kannojia, S.P. & Jaiswal, G. 2018, ‘Effects of varying resolution on performance of CNN based image classification: An experimental study’, International Journal of Computer Sciences and Engineering, vol. 6, no. 9, pp. 451-6. http://dx.doi.org/10.26438/ijcse/v6i9.451456 Mikołajczyk, A. & Grochowski, M. 2018, ‘Data augmentation for improving deep learning in image classification problem’, 2018 International Interdisciplinary PhD workshop (IIPhDW), pp. 117-22, IEEE. https://doi.org/10.1109/IIPHDW.2018.8388338 Mostajabi, M., Yadollahpour, P. & Shakhnarovich, G. 2015, ‘Feedforward semantic segmentation with zoom-out features’, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3376-85. https://doi.org/10.48550/arXiv.1412.0774 Myneni, R.B., Hall, F.G., Sellers, P.J. & Marshak, A.L. 1995, ‘The interpretation of spectral vegetation indexes’, IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 2, pp. 481-6. https://doi.org/10.1109/TGRS.1995.8746029 Nicodemo, M.L.F. & Primavesi, O. 2009, ‘Por que manter árvores na área urbana?’, Embrapa Pecuária Sudeste-Documentos (INFOTECA-E). Pohlen, T., Hermans, A., Mathias, M. & Leibe, B. 2017, ‘Full-resolution residual networks for semantic segmentation in street scenes’, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4151-60. https://doi.org/10.48550/arXiv.1611.08323 Sabottke, C.F. & Spieler, B.M. 2020, ‘The effect of image resolution on deep learning in radiography’, Radiology: Artificial Intelligence, vol. 2, no. 1, pp. e190015. https://doi.org/10.1148/ryai.2019190015Copyright (c) 2022 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2022-12-28T20:46:28Zoai:www.revistas.ufrj.br:article/46008Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2022-12-28T20:46:28Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks
title Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks
spellingShingle Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks
Vestena, Kauê de Moraes
Mobile geospatial data acquisition systems; NIR Imaging; Semantic segmentation
title_short Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks
title_full Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks
title_fullStr Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks
title_full_unstemmed Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks
title_sort Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks
author Vestena, Kauê de Moraes
author_facet Vestena, Kauê de Moraes
Santos, Daniel Rodrigues dos
author_role author
author2 Santos, Daniel Rodrigues dos
author2_role author
dc.contributor.none.fl_str_mv Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
dc.contributor.author.fl_str_mv Vestena, Kauê de Moraes
Santos, Daniel Rodrigues dos
dc.subject.por.fl_str_mv Mobile geospatial data acquisition systems; NIR Imaging; Semantic segmentation
topic Mobile geospatial data acquisition systems; NIR Imaging; Semantic segmentation
description Urbanization brought a lot of pollution-related issues that are mitigable by the presence of urban vegetation. Therefore, it is necessary to map vegetation in urban areas, to assist the planning and implementation of public policies. As a technology presented in the last decades, the so-called Terrestrial Mobile Mapping Systems - TMMS, are capable of providing cost and time effective data acquisition, they are composed primarily by a Navigation System and an Imaging System, both mounted on a rigid platform, attachable to the top ofa ground vehicle. In this context, it is proposed the creation of a low-cost TMMS, which has the feature of imaging in the near-infrared (NIR) where the vegetation is highly discriminable. After the image acquisition step, it becomes necessary for the semantic segmentation of vegetation and non-vegetation. The current state of the art algorithms in semantic segmentation scope are the Convolutional Neural Networks - CNNs. In this study, CNNs were trained and tested, reaching a mean value of 83% for the Intersection Over Union (IoU) indicator. From the results obtained, which demonstrated good performance for the trained neural network, it is possible to concludethat the developed TMMS is suitable to capture data regarding urban vegetation.
publishDate 2022
dc.date.none.fl_str_mv 2022-05-18
dc.type.none.fl_str_mv

dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/46008
10.11137/1982-3908_2022_45_46008
url https://revistas.ufrj.br/index.php/aigeo/article/view/46008
identifier_str_mv 10.11137/1982-3908_2022_45_46008
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/46008/pdf
https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/46008/16481
/*ref*/Badrinarayanan, V., Kendall, A. & Cipolla, R. 2017, ‘Segnet: A deep convolutional encoder-decoder architecture for image segmentation’, IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 12, pp. 2481-95. https://doi.org/10.1109/TPAMI.2016.2644615 Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A.L. 2018, ‘Deep Lab: Semantic image segmentation with deep convolutional nets and fully connected CRFS’, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834-48. https://doi.org/10.1109/TPAMI.2017.2699184 El-Sheimy, N. 2005, ‘An overview of mobile mapping systems’, Proceedings of the FIG Working Week, pp. 16-21. Fawcett, T. 2006, ‘An introduction to ROC analysis’, Pattern recognition letters, vol. 27, no. 8, pp. 861-74. https://doi.org/10.1016/j.patrec.2005.10.010 Kannojia, S.P. & Jaiswal, G. 2018, ‘Effects of varying resolution on performance of CNN based image classification: An experimental study’, International Journal of Computer Sciences and Engineering, vol. 6, no. 9, pp. 451-6. http://dx.doi.org/10.26438/ijcse/v6i9.451456 Mikołajczyk, A. & Grochowski, M. 2018, ‘Data augmentation for improving deep learning in image classification problem’, 2018 International Interdisciplinary PhD workshop (IIPhDW), pp. 117-22, IEEE. https://doi.org/10.1109/IIPHDW.2018.8388338 Mostajabi, M., Yadollahpour, P. & Shakhnarovich, G. 2015, ‘Feedforward semantic segmentation with zoom-out features’, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3376-85. https://doi.org/10.48550/arXiv.1412.0774 Myneni, R.B., Hall, F.G., Sellers, P.J. & Marshak, A.L. 1995, ‘The interpretation of spectral vegetation indexes’, IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 2, pp. 481-6. https://doi.org/10.1109/TGRS.1995.8746029 Nicodemo, M.L.F. & Primavesi, O. 2009, ‘Por que manter árvores na área urbana?’, Embrapa Pecuária Sudeste-Documentos (INFOTECA-E). Pohlen, T., Hermans, A., Mathias, M. & Leibe, B. 2017, ‘Full-resolution residual networks for semantic segmentation in street scenes’, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4151-60. https://doi.org/10.48550/arXiv.1611.08323 Sabottke, C.F. & Spieler, B.M. 2020, ‘The effect of image resolution on deep learning in radiography’, Radiology: Artificial Intelligence, vol. 2, no. 1, pp. e190015. https://doi.org/10.1148/ryai.2019190015
dc.rights.driver.fl_str_mv Copyright (c) 2022 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2022 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
dc.source.none.fl_str_mv Anuário do Instituto de Geociências; Vol 45 (2022)
Anuário do Instituto de Geociências; Vol 45 (2022)
1982-3908
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reponame_str Anuário do Instituto de Geociências (Online)
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