Framework for network-level pavement condition assessment using remote sensing data mining
Open Access
- 7 April 2020
- journal article
- research article
- Published by SPIE-Intl Soc Optical Eng in Journal of Applied Remote Sensing
- Vol. 14 (2)
- https://doi.org/10.1117/1.JRS.14.024504
Abstract
Pavement condition monitoring is fundamental for the efficient allocation of resources in transportation asset management. However, data collection involves laborious and costly procedures. Our study intends to investigate the usage of remote sensing data for network-level pavement condition assessment that offers a more cost-effective alternative and a rapid infrastructure assessment tool that can be used in the aftermath of natural disasters. Based on an extensive literature review, a data mining framework was established to train models that predict the pavement condition of different road segments. The framework exploits the inherent information of multispectral images by generating spectral related attributes. To identify pavement sampling areas, an automated procedure using image segmentation replaces manual surface digitizing. Unlike previous research, different classification models were used to approximate the mapping function from spectral information to pavement conditions. A preliminary case study was conducted with data provided by the City of Dallas and multispectral images acquired from the Texas Natural Resources Information System. The mean-shift segmentation algorithm was used to locate noise introducing areas on the pavement surface. Four different classification models were trained using k-nearest neighbors, naive Bayes, support vector machines, and a multilayer perceptron. The developed models were employed to predict the road surface condition class of a test set not included in the training procedure. The multilayer perceptron presented the highest accuracy level of 71%, showing that the framework might have the potential for future implementation. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)Keywords
This publication has 16 references indexed in Scilit:
- System for Automated Geoscientific Analyses (SAGA) v. 2.1.4Geoscientific Model Development, 2015
- Monitoring asphalt pavement damages using remote sensing techniquesPublished by SPIE-Intl Soc Optical Eng ,2015
- Review of remote sensing methodologies for pavement management and assessmentEuropean Transport Research Review, 2015
- Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing ImagesIEEE Transactions on Geoscience and Remote Sensing, 2014
- Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface DifferentiationRemote Sensing, 2014
- Investigation of hyperspectral remote sensing for mapping asphalt road conditionsInternational Journal of Remote Sensing, 2011
- Analysis of paved areas with field data and MIVIS hyperspectral imagesItalian Journal of Remote Sensing, 2011
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Mapping urban road infrastructure using remotely sensed imagesInternational Journal of Remote Sensing, 2009
- Imaging spectrometry and asphalt road surveysTransportation Research Part C: Emerging Technologies, 2008