Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/106230 https://doi.org/10.5194/isprs-annals-V-2-2020-573-2020 |
Resumo: | Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads. |
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Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networksGenerative Adversarial Networksanomaly detectiondegradationdamageinfrastructure monitoringpost-disasterDegradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.Financial support has been provided by the Innovation and Networks Executive Agency (INEA) under the powers delegated by the European Commission through the Horizon 2020 program “PANOPTIS–Development of a decision support system for increasing the resilience of transportation infrastructure based on combined use of terrestrial and airborne sensors and advanced modelling tools”, Grant Agreement number 769129ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/106230http://hdl.handle.net/10316/106230https://doi.org/10.5194/isprs-annals-V-2-2020-573-2020eng2194-9050Tilon, S. M.Nex, F.Duarte, D.Kerle, N.Vosselman, G.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-04-06T10:20:22Zoai:estudogeral.uc.pt:10316/106230Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:22:43.075236Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks |
title |
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks |
spellingShingle |
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks Tilon, S. M. Generative Adversarial Networks anomaly detection degradation damage infrastructure monitoring post-disaster |
title_short |
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks |
title_full |
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks |
title_fullStr |
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks |
title_full_unstemmed |
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks |
title_sort |
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks |
author |
Tilon, S. M. |
author_facet |
Tilon, S. M. Nex, F. Duarte, D. Kerle, N. Vosselman, G. |
author_role |
author |
author2 |
Nex, F. Duarte, D. Kerle, N. Vosselman, G. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Tilon, S. M. Nex, F. Duarte, D. Kerle, N. Vosselman, G. |
dc.subject.por.fl_str_mv |
Generative Adversarial Networks anomaly detection degradation damage infrastructure monitoring post-disaster |
topic |
Generative Adversarial Networks anomaly detection degradation damage infrastructure monitoring post-disaster |
description |
Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/106230 http://hdl.handle.net/10316/106230 https://doi.org/10.5194/isprs-annals-V-2-2020-573-2020 |
url |
http://hdl.handle.net/10316/106230 https://doi.org/10.5194/isprs-annals-V-2-2020-573-2020 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2194-9050 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
publisher.none.fl_str_mv |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134115651387392 |