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

Aaron Thegeya (), Thomas Mitterling (), Arturo Martinez Jr. (), Joseph Bulan (), Ron Lester Durante () and Jayzon Mag-atas ()
Additional contact information
Aaron Thegeya: World Data Lab
Thomas Mitterling: World Data Lab
Arturo Martinez Jr.: Asian Development Bank
Joseph Bulan: Asian Development Bank
Ron Lester Durante: Asian Development Bank
Jayzon Mag-atas: Asian Development Bank

No 675, ADB Economics Working Paper Series from 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.

Keywords: road quality; road maintenance; Sustainable Development Goals; remote sensing; deep learning (search for similar items in EconPapers)
JEL-codes: O18 R42 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2022-12-22
New Economics Papers: this item is included in nep-big, nep-cmp, nep-sea and nep-tre
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