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Monitoring vegetation on railway embankments: supporting maintenance decisions
Dalarna University, School of Technology and Business Studies, Computer Engineering. School of Engineering and the Built Environment, Edinburgh Napier University, EH10 5DT Edinburgh, UK.ORCID iD: 0000-0003-4812-4988
School of Engineering and the Built Environment, Edinburgh Napier University, EH10 5DT Edinburgh, UK.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2013 (English)In: Proceedings of the 2013 International Conference on Ecology and Transportation, 2013, 1-18 p.Conference paper, Published paper (Refereed)
Abstract [en]

The national railway administrations in Scandinavia, Germany, and Austria mainly resort to manual inspections to control vegetation growth along railway embankments. Manually inspecting railways is slow and time consuming. A more worrying aspect concerns the fact that human observers are often unable to estimate the true cover of vegetation on railway embankments. Further human observers often tend to disagree with each other when more than one observer is engaged for inspection. Lack of proper techniques to identify the true cover of vegetation even result in the excess usage of herbicides; seriously harming the environment and threating the ecology. Hence work in this study has investigated aspects relevant to human variationand agreement to be able to report better inspection routines. This was studied by mainly carrying out two separate yet relevant investigations.First, thirteen observers were separately asked to estimate the vegetation cover in nine imagesacquired (in nadir view) over the railway tracks. All such estimates were compared relatively and an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05). Bearing in difference between the observers, a second follow-up field-study on the railway tracks was initiated and properly investigated. Two railway segments (strata) representingdifferent levels of vegetationwere carefully selected. Five sample plots (each covering an area of one-by-one meter) were randomizedfrom each stratumalong the rails from the aforementioned segments and ten images were acquired in nadir view. Further three observers (with knowledge in the railway maintenance domain) were separately asked to estimate the plant cover by visually examining theplots. Again an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05) confirming the result from the first investigation.The differences in observations are compared against a computer vision algorithm which detects the "true" cover of vegetation in a given image. The true cover is defined as the amount of greenish pixels in each image as detected by the computer vision algorithm. Results achieved through comparison strongly indicate that inconsistency is prevalent among the estimates reported by the observers. Hence, an automated approach reporting the use of computer vision is suggested, thus transferring the manual inspections into objective monitored inspections

Place, publisher, year, edition, pages
2013. 1-18 p.
National Category
Engineering and Technology Computer Science Ecology Computer Vision and Robotics (Autonomous Systems)
Research subject
Complex Systems – Microdata Analysis, Automatisk detektering och karakterisering av vegetation längs järnvägen; Complex Systems – Microdata Analysis, Utveckling av metod för att bedöma behovet av ogräsbekämpning i spår
Identifiers
URN: urn:nbn:se:du-13314OAI: oai:DiVA.org:du-13314DiVA: diva2:667203
Conference
International Conference on Ecology and Transportation, June 23-27 Arizona US
Available from: 2013-11-25 Created: 2013-11-25 Last updated: 2016-05-20Bibliographically approved

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ICOET2013_Paper103C_Nyberg_at_al.pdf(771 kB)139 downloads
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CiteExportLink to record
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Citation style
  • apa
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