Exploring the Capabilities of Optical Satellite Imagery in Evaluating Pavement Condition
Publication: Construction Research Congress 2022
ABSTRACT
This study explores the use of satellite imagery as a rapid and cost-effective alternative to evaluate road surface condition. Spectral and texture information derived from 30-cm panchromatic and 1.2-m spaceborne optical imagery were used to assess the capabilities of satellite images in describing pavement distresses. The spectral analysis of the multispectral images indicates that pavements become brighter across all the bands as the roads become rougher. The pixel brightness values decreased when pavements were classified based on deteriorating rutting condition. While this may be useful in characterizing individual distresses, this opposing behavior of the pixel brightness values limits the capabilities of spectral indices approach in analyzing overall pavement condition when multiple distresses are present in the same location. All the texture metrics were found to follow a logical relation with pavement conditions, indicating strong evidence that satellite data have the potential to be an alternative method to evaluate pavement condition.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Apollo Mapping. (2021). “WorldView-3 Satellite Imagery.” <https://apollomapping.com/worldview-3-satellite-imagery>(Aug. 25, 2021).
Arhin, S. A., Noel, E. C., and Ribbiso, A. (2015). “Acceptable International Roughness Index Thresholds based on Present Serviceability Rating.” Journal of Civil Engineering Research, 5(4), 90–96.
Emery, W. (2014). Large-Area Road-Surface Quality Using Very-High Spatial Resolution Aerial and Satellite Data. U.S. Department of Transportation.
Fagrhi, A., and Ozden, A. (2015). Satellite Assessment and Monitoring for Pavement Management. Newark, Delaware.
FHWA. (2016). Highway Performance Monitoring System Field Manual.
Herold, M., Roberts, D., Noronha, V., and Smadi, O. (2008). “Imaging spectrometry and asphalt road surveys.” Transportation Research Part C: Emerging Technologies, 16(2), 153–166.
Hoppe, E., Bruckno, B., Campbell, E., Acton, S., Vaccari, A., Stuecheli, M., Bohane, A., Falorni, G., and Morgan, J. (2014). “Transportation Infrastructure Monitoring Using Satellite Remote Sensing.” Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment.
Mohammadi, M. (2012). “Road Classification and Condition Determination Using Hyperspectral Imagery.” ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIX-B7(September), 141–146.
Pan, Y., Zhang, X., Jin, X., Yu, H., Rao, J., Tian, S., Luo, L., and Li, C. (2016). “Road pavement condition mapping and assessment using remote sensing data based on MESMA.” IOP Conference Series: Earth and Environmental Science, 34(1).
Pierce, L., and Weitzel, N. D. (2019). Automated Pavement Condition Surveys. Transportation Research Board.
Politis, S. S., Zhang, Z., Kouchaki, S., and Caldas, C. H. (2020). “Framework for network-level pavement condition assessment using remote sensing data mining.” Journal of Applied Remote Sensing, 14(02), 1.
Shahi, K., Shafri, H. Z. M., and Hamedianfar, A. (2017). “Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery.” Geocarto International, Taylor & Francis, 32(12), 1389–1406.
Shahi, K., Shafri, H. Z. M., Taherzadeh, E., Mansor, S., and Muniandy, R. (2015). “A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery.” Egyptian Journal of Remote Sensing and Space Science, Authority for Remote Sensing and Space Sciences, 18(1), 27–33.
Information & Authors
Information
Published In
History
Published online: Mar 7, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Alekhya Pachika, Lu Gao, Lingguang Song, Pan Lu, Xingju Wang, Estimating the Impact of COVID-19 on Travel Demand in the Houston Area Using Deep Learning and Satellite Imagery, International Conference on Transportation and Development 2023, 10.1061/9780784484876.038, (437-444), (2023).