Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

Rischioni, Lucas Germano; Babu, Arun; Baumgartner, Stefan V.; Krieger, Gerhard

doi:10.1109/jstars.2023.3258059

Cited by 1 publication

(4 citation statements)

References 39 publications

(63 reference statements)

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Section: Synthetic Aperture Radarmentioning

confidence: 99%

“…The use of SAR technology for evaluating pavement roughness has been the focus of multiple studies [24,[129][130][131][132][133][134][135]. SAR data from X-band [132][133][134][135], L-band [24,131], and C-band [130] was investigated for pavement roughness estimation. The data were collected using various satellites such as Sentinel-1 [24,130], Sentinel-2 [132], Cosmo-SkyMed [129], and Advanced Land Observing Satellite (ALOS) [131].…”

Section: Synthetic Aperture Radarmentioning

confidence: 99%

“…Thus, more research is required before assuming the robustness of this technology with the low-resolution publicly available satellite images. Alternatively, Babu et al [133] and Rischioni et al [135] used high-resolution airborne polarimetric SAR images to evaluate pavement roughness. The two studies utilized DLR's airborne radar sensor F-SAR to acquire fully polarimetric X-band datasets.…”

Section: Synthetic Aperture Radarmentioning

confidence: 99%

“…While satellite imagery is generally more cost-effective, airborne SAR images can offer higher resolution and greater accuracy. However, challenges such as the presence of vehicles on the road, tree coverage, and processing difficulties, including segmentation challenges, can limit the effectiveness of this approach in specific scenarios, particularly on urban roads [134,135]. Additionally, it was reported by Fiorentini et al [178] that roughness resulting from endogenous factors, such as traffic flow and pavement materials, may not be detectable using SAR technology.…”

Section: Research Communitymentioning

confidence: 99%

See 3 more Smart Citations

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Fares

Zayed

2023

Remote Sensing

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Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry.

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