Projects per year
Abstract
Resilient road infrastructures are vital to a society's welfare and economy; however, monitoring and maintaining them is becoming increasingly complex due to climate change, ageing infrastructure assets and increased traffic loads. Increasing maintenance expenditure is required to refurbish assets, of which the majority are already reaching the end of their predicted service life, and to make them resilient against (future) stressors that lead to accelerated degradation and damage. Monitoring is crucial in assessing the state and performance level of infrastructures. However, most Western countries have deferred essential maintenance and monitoring. Catastrophic events in recent years have highlighted the state of today's crumbling infrastructure and made it evident that most countries face the critical challenge of upkeeping these vital “lifelines”. There is a need for improved, automated, accurate and synoptic monitoring technologies to support road operators in keeping road infrastructures safe and functional in routine and emergency scenarios. Modern inspection technology increasingly supports human-based inspections and is extremely valuable in alleviating the limitations that exist in operational or traditional monitoring methods. There are various state-of-the-art technologies and perspectives from which infrastructure monitoring can be regarded. This dissertation considers it in a non-destructive and remote sensing manner while simultaneously meeting the demand of road operators to achieve monitoring in a real-time, multi-objective and automatic manner. To this end, research was conducted to investigate and propose novel monitoring technology to aid road infrastructure monitoring in routine and emergency scenarios using earth observation platforms and Artificial Intelligence (AI).
The following objectives were investigated:
-To determine the applicability of anomaly-detecting generative adversarial networks (ADGANs) for routine and post-disaster damage assessments. (Chapters 2 and 3)
-To develop a framework for real-time infrastructure monitoring using (hybrid) UAVs. (Chapter 4)
-To provide road operators with qualitative high-level information products to detect anomalous infrastructure scenarios from a UAV platform. (Chapter 5)
The following objectives were investigated:
-To determine the applicability of anomaly-detecting generative adversarial networks (ADGANs) for routine and post-disaster damage assessments. (Chapters 2 and 3)
-To develop a framework for real-time infrastructure monitoring using (hybrid) UAVs. (Chapter 4)
-To provide road operators with qualitative high-level information products to detect anomalous infrastructure scenarios from a UAV platform. (Chapter 5)
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 13 Sept 2023 |
Publisher | |
Print ISBNs | 978-90-365-5710-8 |
DOIs | |
Publication status | Published - 13 Sept 2023 |
Keywords
- Edge computing
- Artificial Intelligence (AI)
- Road infrastructure
- Generative adversarial networks
- Anomaly detection
- Degradation
- Damage
- Infrastructure monitoring
- Post-disaster
- xBD
- Building damage detection
- Satellite
- Unmanned Aerial Vehicle (UAV)
- Real-time
- Remote processing
- Fixed-wing VTOL
- Hybrid UAV
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Dive into the research topics of 'A Routine and Post-disaster Road Corridor Monitoring Framework for the Increased Resilience of Road Infrastructures'. Together they form a unique fingerprint.Projects
- 1 Finished
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Panoptis: H2020 - Panoptis
Kerle, N. (PI), Nex, F. (CoI), Tilon, S. M. (CoI) & Mavrouli, O. (CoI)
1/06/18 → 31/12/22
Project: Research
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Towards improved unmanned aerial vehicle edge intelligence: a road infrastructure monitoring case study
Tilon, S., Nex, F., Vosselman, G., Llave, I. S. D. L. & Kerle, N., 18 Aug 2022, In: Remote sensing. 14, 16, 4008.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile10 Citations (Scopus)162 Downloads (Pure) -
Infrastructure degradation and post-disaster damage detection using anomaly detecting Generative Adversarial Networks
Tilon, S. M., Nex, F., Duarte, D., Kerle, N. & Vosselman, G., 3 Aug 2020, XXIV ISPRS Congress, Commission II 2020. Paparoditis, N., Mallet, C., Lafarge, F., Remondino, F., Toschi, I. & Fuse, T. (eds.). International Society for Photogrammetry and Remote Sensing (ISPRS), p. 573-582 10 p. (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences; vol. V-2-2020).Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review
Open AccessFile7 Citations (Scopus)307 Downloads (Pure) -
Post-Disaster Building Damage Detection from Earth Observation Imagery using Unsupervised and Transferable Anomaly Detecting Generative Adversarial Networks
Tilon, S., Nex, F., Kerle, N. & Vosselman, G., 21 Dec 2020, In: Remote sensing. 12, 24, p. 1-27 27 p., 4193.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile41 Citations (Scopus)334 Downloads (Pure)