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evaluation are labor-intensive, time-consuming, and ex-
pensive. To overcome these limitations, we present a novel
approach for overall pavement surface condition evaluation
through the analysis of routinely-acquired and publically-
available HSR multispectral digital aerial photographs. These
images are already paid for through a variety of means, per-
mitting a dramatic reduction in the cost of intensive survey
through manual or automated samples, making it extremely
practical and immediately implementable across all regions
without tree cover. Our results have shown that natural color
aerial photographs of 6-inch spatial resolution can be used to
evaluate the overall pavement distress conditions with a high
degree of certainty (R2 >95%). At a lesser degree of certainty,
12-inch and 24-inch natural color aerial photographs can also
be used to detect overall pavement conditions. When consid-
ering the associated cost, the lower resolution aerial photo-
graphs can be potentially applied to evaluate overall pave-
ment surface distress for rapid, high-level information checks.
Our results also have shown that visible red band or spectral
features alone can be used to estimate the overall pavement
conditions with a high degree of certainty (R2 >92%).
The proposed method of detecting pavement surface dis-
tress conditions by analyzing HSR multispectral, digital aerial
photography could be used as a predictor of overall distress
conditions in situations where field inspectors cannot evalu-
ate except with considerable labor (e.g., sections in remote ar-
eas). It is not likely that the proposed method will completely
replace field pavement surface inspection due to its lack of
crack-level detailed pavement surface information and the
necessity of using field pavement surface inspection results
as reference data to develop the regression models. How-
ever, the spectral response in HSR multispectral digital aerial
photography presents additional information not considered
in field inspection and could be used to predict the overall
pavement surface conditions in un-sampled areas based on
a dramatically reduced number of intensive survey sites.
Therefore, it can reduce the amount of work, time, and money
associated with pavement surface evaluation. Operationally
this proposed approach could be readily implemented as a
service internally by transportation agencies such as NMDOT
or implemented through consulting firms. Eventually this
proposed method could be automated through software devel-
opment. Such software would only require users to insert the
pavement surface distress rates of a limited number of manual
survey sites, add associated HSR multispectral digital aerial
photography, and upload the evaluation polygon, while the
computing-intensive processes such as eliminating unwanted
features is fully automated.
Acknowledgments
We would like to express our greatest gratitude to New
Mexico Department of Transportation (NMDOT) and Earth
Data Analysis Center (EDAC) at the University of New Mexico
(UNM). Without their enormous support, it would not have
been possible to complete this research. Thanks also go to
the PE&RS journal editor and anonymous reviewers for their
valuable comments to improve this paper.
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