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Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys

Author

Listed:
  • Thegeya, Aaron

    (World Data Lab)

  • Mitterling, Thomas

    (World Data Lab)

  • Martinez Jr., Arturo

    (Asian Development Bank)

  • Bulan, Joseph

    (Asian Development Bank)

  • Durante, Ron Lester

    (Asian Development Bank)

  • Mag-atas, Jayzon

    (Asian Development Bank)

Abstract

Roads are vital to support the transportation of people, goods, and services, among others. To yield their optimal socioeconomic impact, proper maintenance of existing roads is required; however, this is typically underfunded. Since detecting road quality is both labor and capital intensive, information on it is usually scarce, especially in resource-constrained countries. Accordingly, the study examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads. With this goal, a preliminary algorithm was created and validated using medium-resolution satellite imagery and existing road roughness data from the Philippines. After analysis, it was determined that the algorithm had an accuracy rate up to 75% and can be used for the preliminary identification of poor to bad roads. This provides an alternative for compiling road quality data, especially for areas where conventional methods can be difficult to implement. Nonetheless, additional technical enhancements need to be explored to further increase the algorithm’s prediction accuracy and enhance its robustness.

Suggested Citation

  • Thegeya, Aaron & Mitterling, Thomas & Martinez Jr., Arturo & Bulan, Joseph & Durante, Ron Lester & Mag-atas, Jayzon, 2022. "Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys," ADB Economics Working Paper Series 675, Asian Development Bank.
    • Handle: RePEc:ris:adbewp:0675
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    File URL: https://www.adb.org/publications/machine-learning-satellite-imagery-road-quality-monitoring
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    References listed on IDEAS

    as
    1. Andrea E Gaughan & Forrest R Stevens & Catherine Linard & Peng Jia & Andrew J Tatem, 2013. "High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    2. Sally Burningham & Natalya Stankevich, 2005. "Why Road Maintenance is Important and How to Get it Done," World Bank Publications - Reports 11779, The World Bank Group.
    3. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    4. Christopher R. Bennett & Hernán De Solminihac & Alondra Chamorro, 2006. "Data Collection Technologies for Road Management," World Bank Publications - Reports 11776, The World Bank Group.
    5. Asian Development Bank Institute, 2017. "Meeting Asia's Infrastructure Needs," Working Papers id:11711, eSocialSciences.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    road quality; road maintenance; Sustainable Development Goals; remote sensing; deep learning;
    All these keywords.

    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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