道路质量监控机器学习技术指南

MARCH  GUIDEBOOK ON MACHINELEARNING TECHNIQUES FORROAD QUALITY MONITORING MARCH 2025 Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2025 Asian Development Bank6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, PhilippinesTel +63 2 8632 4444; Fax +63 2 8636 2444www.adb.org Some rights reserved. Published in 2025. ISBN 978-92-9277-230-7 (print); 978-92-9277-231-4 (PDF); 978-92-9277-232-1 (ebook)Publication Stock No. TCS250091-2DOI: http://dx.doi.org/10.22617/TCS250091-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policiesof the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for anyconsequence of their use. The mention of specific companies or products of manufacturers does not imply that theyare endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area in this document, ADB does notintend to make any judgments as to the legal or other status of any territory or area. This publication is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO)https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be boundby the terms of this license. For attribution, translations, adaptations, and permissions, please read the provisionsand terms of use at https://www.adb.org/terms-use#openaccess. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributedto another source, please contact the copyright owner or publisher of that source for permission to reproduce it.ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact pubsmarketing@adb.org if you have questions or comments with respect to content, or if you wishto obtain copyright permission for your intended use that does not fall within these terms, or for permission to usethe ADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Tables and FiguresvForewordviiiAbbreviationsxI.Introduction1Conventional Approaches of Evaluating Quality of Road Pavements1Challenges with Using Conventional Approaches of Evaluating Quality of Road Pavements2Potential of Innovative Data and Frontier Technologies2II.A Review of Satellite Imagery-Based Methods in Road Quality Assessment4Overview of Satellite Imagery Datasets5Some Similar Applications7Feasibility of Remote Sensing Techniques Using Satellite Imagery8III.Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring11Neural Networks12Generative Models13IV.Hardware and Software Requirements and Setup16Hardware16Software Requirements and Installation16V.Data Preparation28Acquiring Shapefiles28Acquiring Road Bounding Boxes30Uploading the Output to Google Drive32Processing Bounding Boxes35Downloading Satellite Imagery39VI.Training the Super-Resolution Model (REAL-ESRGAN)50Real-ESRGAN and Basic-SR50VII.Classification Using Satellite Imagery56Downloading the Philippines’ Road Sections56Training the Classification Model64 VIII.Alternative Method: Smartphone-Based Pavement Condition Assessment72 Smartphone Technology Used for Pavement Condition Assessment72Roughness Evaluation Using Smartphone-Based Data75Smartphone-Based Data for Pavement Condition Evaluation: State of the Practice82Expanding Smartphone-Based Data to Crowdsourced Data for Pavement Condition Assessment88Feasibility of Adopting Smartphone-Based Assessment Methods100 IX.Moving Forward: Integrating Different Techniques in Road Condition Monitoring107 Comparison of Pavement Condition Monitoring Techniques: Traditional versus Frontier Techniques107Summary of Financial and Technical Resource Requirements for Implementing These Techniques108Discussion on Integrating Different Techniques in Road Condition Assessment110 X.Summary and Conclusion112 Appendix114 References134 Tables and Figures Tables 1Smartphone Sensors Used to Evaluate Pavement Condition Metrics732Summary of Different Influencing Factors on Simulated Vehicle Body Acceleration743Vehicle Operation Speeds for Different Smartphone Sampling Frequencies774Summary of Machine Learning Techniques Used for Roughness Evaluation795Summary of Current Smartphone Technologies to Measure Roughness856Effects of Driving Regimes on Simulated Grms997Comparison of Cost Values for Pavement Roughness Measurement Compared to the Laser Profiler101(Class I), Bump Integrator (Class III) Illustrated from the Practice in Sri Lanka8Case Studies Conducted to Evaluate the Applicability of Road Condition Monitoring Using102Smartphone-Based Approaches9Cost Comparison for Smartphone-Based and Satellite-Image-Based Monitor