[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120695-en":3,"doc-seo-120695-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120695,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar","Airborne synthetic aperture radar (SAR) enables large-scale remote monitoring of road traffic infrastructure, with road surface roughness serving as a key road safety indicator. The study develops and evaluates machine learning models to estimate roughness from fully polarimetric airborne SAR data collected using DLR’s F-SAR system. Algorithms based on artificial neural networks and random forest regression are compared against ground-truth roughness measurements and a semi-empirical roughness model from prior research, assessing accuracy and robustness.","This article has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. This is the author's version which has not been fully  \ncontent may change prior to final publication. Citation information: DOI 10. 1109/JSTARS.2023.3258059  \nIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 1  \nMachine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar  \nLucas Germano Rischioni, Arun Babu, Graduate Student Member, IEEE, Stefan V. Baumgartner, Senior Member,  \nIEEE, and Gerhard Krieger, Fellow, IEEE  \nAbstract—Airborne Synthetic Aperture Radar (SAR) has the potential to monitor remotely the road traffic infrastructure on a large scale. Of particular interest is the road surface roughness, which is an important road safety parameter. For this task, novel algorithms need to be developed. Machine learning approaches, such as Artificial Neural Networks (ANN) and Random Forest Regression, which can perform non-linear regression, can achieve this goal. This work considers fully polarimetric airborne radar datasets captured with DLR’s airborne F-SAR radar system. Several machine learning-based approaches were tested on the datasets to estimate road surface roughness. The resulting models were then compared with ground truth surface roughness values and also with the semi-empirical surface roughness model studied in previous work.  \nIndex Terms—Synthetic aperture radar, additive noise, surface roughness, machine learning, vehicle safety.  \nI. INTRODUCTION  \nROADS contribute crucially to the development and eco  \nnomic growth of a country, being responsible for bringing several social benefits [1], [2] . They provide access to different regions of the country and promote economic and social development [3], [4] . Therefore, monitoring the quality of road infrastructure and carrying out regular maintenance are equally important for a country’s economy and also for the safety of road users. There are several factors that affect the road surface quality, of which one important is the road surface roughness [5] . This is because the road surface roughness is responsible for the friction between the road surface and the tires of the vehicles [6], [7] . A sufficient level of friction is required for safe acceleration, steering, and braking of the vehicles [8] . If the friction is below the required level, this may cause the vehicle to skid [9], and if the friction is very high, this can result in increased fuel consumption, tire abrasion, noise, etc [10] . Although traffic accidents can occur for a variety of reasons, several studies have shown that poor’skid resistance’ increases the probability of an accident [8] . Therefore, regular inspection of the road surface is necessary to ensure that the roughness values of the road surface are within optimal limits, which in turn can help to reduce the number of road accidents.  \nAt present, road conditions in Germany are measured in average once every 4 years by special survey vehicles equipped  \nLucas Germano Rischioni was with the Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Weßling,˜ Germany. He is now with  \nthe Aeronautical Technological Institute (ITA), Sao Jos dos Campos - SP, 12228-900, Brazil (email: [lucasgrischioni@gmail.com](lucasgrischioni@gmail.com)). Arun Babu, Stefan V. Baumgartner, and Gerhard Krieger are with the Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Weßling, Germany (email: [arun.babu@dlr.de](arun.babu@dlr.de); [stefan.baumgartner@dlr.de](stefan.baumgartner@dlr.de); [gerhard.krieger@dlr.de](gerhard.krieger@dlr.de)).  \nwith various measuring devices driving over motorways and other major roads [11], [12] . The deterioration of the road surface occurs mainly in the winter season due to repeated freeze-thaw cycles [13] . This highlights the need to monitor the condition of the road surface more frequently, preferably a","cbCaih42NBmcmx4v","https://ap.wps.com/l/cbCaih42NBmcmx4v","pdf",50415974,1,13,"English","en",105,"# Introduction\n## Road surface roughness and road safety\n## Limitations of conventional inspections\n## SAR as an alternative for monitoring","[{\"question\":\"Why is road surface roughness important for vehicle safety?\",\"answer\":\"Road surface roughness affects tire-road friction, which is required for safe acceleration, steering, and braking. Insufficient or excessive friction can increase skidding risk and lead to other operational problems.\"},{\"question\":\"What sensing approach is used to monitor road conditions?\",\"answer\":\"The work uses airborne synthetic aperture radar (SAR), leveraging its sensitivity to surface roughness-related dielectric and geometric changes, along with high spatial resolution and day-night/cloud-penetration capabilities.\"},{\"question\":\"How do the proposed methods estimate roughness from SAR data?\",\"answer\":\"Machine learning models, including artificial neural networks and random forest regression, learn a non-linear mapping from fully polarimetric airborne SAR datasets to road surface roughness. The resulting estimates are compared with ground truth and a semi-empirical model from earlier studies.\"}]","Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar | PDF",1785731581,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-approaches-for-road-condition-monitoring-using-synthetic-aperture-radar","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-for-road-condition-monitoring-using-synthetic-aperture-radar/120695/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is road surface roughness important for vehicle safety?","Question",{"text":75,"@type":76},"Road surface roughness affects tire-road friction, which is required for safe acceleration, steering, and braking. Insufficient or excessive friction can increase skidding risk and lead to other operational problems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What sensing approach is used to monitor road conditions?",{"text":80,"@type":76},"The work uses airborne synthetic aperture radar (SAR), leveraging its sensitivity to surface roughness-related dielectric and geometric changes, along with high spatial resolution and day-night/cloud-penetration capabilities.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the proposed methods estimate roughness from SAR data?",{"text":84,"@type":76},"Machine learning models, including artificial neural networks and random forest regression, learn a non-linear mapping from fully polarimetric airborne SAR datasets to road surface roughness. 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