Application of remote sensing for automated litter detection and management

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Clean Europe Network (CEN) estimates that cleaning litter in the EU accounts for€10-13 billion of public expenditure every year.The annual budget for managing roadside litter alone,is approximately €1 billion.While local authorities in Northern Ireland and elsewhere have legal requirements to monitor and control litter levels, requirements for compliance are unclear and frequently ignored. Against this background, the overall objective of this research is to develop an integrated management system allowing remote discrimination and quantification of roadside litter. As such, the intention is that local authorities can more effectively meet their statutory requirements with regards to litter management. The research aligns with objectives outlined by the UK Government and CEN in terms of improving litter-related data levels. As plastic containers of type RIC1, Polyethylene terephthalate (PETE),represent one of the most common components of roadside litter, its identification in the natural environment via remote sensing is a key objective. By combining published US Hyperspectral library data and experimental field study results, the initial findings of this research indicate that it is possible to discriminate PETE plastic samples in a grass background using a low-cost multispectral sensor primarily designed for agricultural use. While at an initial phase, the research presented has the potential to have a significant impact on the economic, environmental and statutory implications of roadside litter management.Future work will employ image processing and machine learning techniques to deliver a methodology for automatic identification and quantification of multiple roadside litter types.
LanguageEnglish
Title of host publicationAdvances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC
Subtitle of host publicationProceedings of the 2019 Computer Vision Conference (CVC), Volume 2
EditorsSupriya Kapoor, Kohei Arai
Pages157-168
Number of pages12
Volume944
ISBN (Electronic)978-3-030-17798-0
DOIs
Publication statusE-pub ahead of print - 24 Apr 2019
EventComputer Vision Conference 2019 - Vdara Hotel & Spa , Las Vegas, United States
Duration: 25 Apr 201926 Apr 2019
https://saiconference.com/CVC

Publication series

NameAdvances in Intelligent Systems and Computing

Conference

ConferenceComputer Vision Conference 2019
Abbreviated titleCVC 2019
CountryUnited States
CityLas Vegas
Period25/04/1926/04/19
Internet address

Fingerprint

litter
remote sensing
plastic
detection
environmental economics
image processing
compliance
grass
sensor
methodology
cost

Keywords

  • Image analysis
  • Multispectral
  • Litter
  • Remote sensing
  • Hyperspectral signatures

Cite this

Hamill, M., Magee, B., & Millar, P. (2019). Application of remote sensing for automated litter detection and management. In S. Kapoor, & K. Arai (Eds.), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2 (Vol. 944, pp. 157-168). (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-17798-0_15
Hamill, Mark ; Magee, Bryan ; Millar, Phillip. / Application of remote sensing for automated litter detection and management. Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2. editor / Supriya Kapoor ; Kohei Arai. Vol. 944 2019. pp. 157-168 (Advances in Intelligent Systems and Computing).
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title = "Application of remote sensing for automated litter detection and management",
abstract = "The Clean Europe Network (CEN) estimates that cleaning litter in the EU accounts for€10-13 billion of public expenditure every year.The annual budget for managing roadside litter alone,is approximately €1 billion.While local authorities in Northern Ireland and elsewhere have legal requirements to monitor and control litter levels, requirements for compliance are unclear and frequently ignored. Against this background, the overall objective of this research is to develop an integrated management system allowing remote discrimination and quantification of roadside litter. As such, the intention is that local authorities can more effectively meet their statutory requirements with regards to litter management. The research aligns with objectives outlined by the UK Government and CEN in terms of improving litter-related data levels. As plastic containers of type RIC1, Polyethylene terephthalate (PETE),represent one of the most common components of roadside litter, its identification in the natural environment via remote sensing is a key objective. By combining published US Hyperspectral library data and experimental field study results, the initial findings of this research indicate that it is possible to discriminate PETE plastic samples in a grass background using a low-cost multispectral sensor primarily designed for agricultural use. While at an initial phase, the research presented has the potential to have a significant impact on the economic, environmental and statutory implications of roadside litter management.Future work will employ image processing and machine learning techniques to deliver a methodology for automatic identification and quantification of multiple roadside litter types.",
keywords = "Image analysis, Multispectral, Litter, Remote sensing, Hyperspectral signatures",
author = "Mark Hamill and Bryan Magee and Phillip Millar",
note = "Hamill M., Magee B., Millar P. (2020) Application of Remote Sensing for Automated Litter Detection and Management. In: Arai K., Kapoor S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham",
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Hamill, M, Magee, B & Millar, P 2019, Application of remote sensing for automated litter detection and management. in S Kapoor & K Arai (eds), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2. vol. 944, Advances in Intelligent Systems and Computing, pp. 157-168, Computer Vision Conference 2019, Las Vegas, United States, 25/04/19. https://doi.org/10.1007/978-3-030-17798-0_15

Application of remote sensing for automated litter detection and management. / Hamill, Mark; Magee, Bryan; Millar, Phillip.

Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2. ed. / Supriya Kapoor; Kohei Arai. Vol. 944 2019. p. 157-168 (Advances in Intelligent Systems and Computing).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Magee, Bryan

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Y1 - 2019/4/24

N2 - The Clean Europe Network (CEN) estimates that cleaning litter in the EU accounts for€10-13 billion of public expenditure every year.The annual budget for managing roadside litter alone,is approximately €1 billion.While local authorities in Northern Ireland and elsewhere have legal requirements to monitor and control litter levels, requirements for compliance are unclear and frequently ignored. Against this background, the overall objective of this research is to develop an integrated management system allowing remote discrimination and quantification of roadside litter. As such, the intention is that local authorities can more effectively meet their statutory requirements with regards to litter management. The research aligns with objectives outlined by the UK Government and CEN in terms of improving litter-related data levels. As plastic containers of type RIC1, Polyethylene terephthalate (PETE),represent one of the most common components of roadside litter, its identification in the natural environment via remote sensing is a key objective. By combining published US Hyperspectral library data and experimental field study results, the initial findings of this research indicate that it is possible to discriminate PETE plastic samples in a grass background using a low-cost multispectral sensor primarily designed for agricultural use. While at an initial phase, the research presented has the potential to have a significant impact on the economic, environmental and statutory implications of roadside litter management.Future work will employ image processing and machine learning techniques to deliver a methodology for automatic identification and quantification of multiple roadside litter types.

AB - The Clean Europe Network (CEN) estimates that cleaning litter in the EU accounts for€10-13 billion of public expenditure every year.The annual budget for managing roadside litter alone,is approximately €1 billion.While local authorities in Northern Ireland and elsewhere have legal requirements to monitor and control litter levels, requirements for compliance are unclear and frequently ignored. Against this background, the overall objective of this research is to develop an integrated management system allowing remote discrimination and quantification of roadside litter. As such, the intention is that local authorities can more effectively meet their statutory requirements with regards to litter management. The research aligns with objectives outlined by the UK Government and CEN in terms of improving litter-related data levels. As plastic containers of type RIC1, Polyethylene terephthalate (PETE),represent one of the most common components of roadside litter, its identification in the natural environment via remote sensing is a key objective. By combining published US Hyperspectral library data and experimental field study results, the initial findings of this research indicate that it is possible to discriminate PETE plastic samples in a grass background using a low-cost multispectral sensor primarily designed for agricultural use. While at an initial phase, the research presented has the potential to have a significant impact on the economic, environmental and statutory implications of roadside litter management.Future work will employ image processing and machine learning techniques to deliver a methodology for automatic identification and quantification of multiple roadside litter types.

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KW - Litter

KW - Remote sensing

KW - Hyperspectral signatures

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Hamill M, Magee B, Millar P. Application of remote sensing for automated litter detection and management. In Kapoor S, Arai K, editors, Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2. Vol. 944. 2019. p. 157-168. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-17798-0_15