AN UAV-BASED PHOTOGRAMMETRIC MAPPING SYSTEM 
FOR ROAD CONDITION ASSESSMENT 
Chunsun Zhang 
Geographic Information Science Center of Excellence (GIScCE), South Dakota State University 1021 Medary Ave, 
Brookings SD 57007, USA - chunsun.zhang@sdstate.edu 
ThS-15 
KEY WORDS: UAV, Mapping, Transportation, Road, Condition Assessment 
ABSTRACT: 
We present an Unmanned Aviation Vehicle-based Photogrammetric mapping system in this paper. This work is part of a project 
monitoring of unpaved road condition using remote sensing and other technology, sponsored by the US Department of 
Transportation. The system is based on a low cost model helicopter equipped with a GPS/IMU and a geomagnetic sensor to detect 
the position, attitude and velocity of the helicopter. An autonomous controller was employed to control helicopter to fly along a 
predefined flight path and reach the desired positions. At the ground station, a computer was used to communicate with the 
helicopter in real-time to monitor flight parameters and send out control commands. The entire processing system includes camera 
calibration, integrated sensor orientation, digital 3D road surface model and orthoimage generation, automated feature extraction and 
measurement for road condition assessment. In this paper, both the project and the system architecture are described, and the recent 
development results are presented. 
1. INTRODUCTION 
The unpaved roads are typically low-volume roads linking 
small agricultural communities to nearby towns and markets. 
These roads tend to experience seasonal variations in traffic 
volumes with significantly higher flows occurring around 
harvest time each year. If periods of wet weather and high 
traffic volumes coincide, damage to unpaved roads can be very 
severe. Such roads are also susceptible to damage because of 
the kind of vehicles that traverse them. Heavy farm machinery 
and trucks laden with farm produce can do more damage to a 
road than a series of smaller vehicles of equal net mass. 
The construction and maintenance of unpaved roads is usually 
performed by local townships and county governments. In US, 
for instance, local transportation departments must conduct 
field surveys to identify problem areas and schedule 
maintenance activities. Due to the small funding base of local 
government, the human and financial resources available for 
maintaining roads are often inadequate. While state and federal 
transportation departments often have vehicle-mounted 
roughometers and other devices to assess road surface 
conditions, local officials typically rely on visual inspection, 
intuition and occasional spot measurements in their assessments. 
Yet the importance of timely identification and rectification of 
road deformation through loss of crown or damage to the road 
base cannot be overstated. 
The predominant method in conducting road condition survey 
and analysis is still largely based on extensive field observation 
by experts. Recent efforts include the development of a 
pavement survey vehicle coupled with sensor technologies and 
data-processing onboard. Some such systems have been used by 
highway agencies (Kenneth, 2004). However, no similar 
technology or system exists for unpaved roads. Nevertheless, 
data collection using a moving vehicle still remains an 
expensive and troublesome survey, while cost and safety 
considerations require that it be done at regular intervals. 
Recently, commercial remote sensing technologies have been 
introduced for pavement assessment. A study has been 
conducted to find a correlation between spectral reflectance and 
physical characteristics such as rutting and cracking (NCRST, 
2003a). The results show it is possible to describe general 
pavement age and specific surface defects such as raveling, and 
to estimate their spatial characteristics. Due to limited spatial 
resolution (4m), other important pavement quality parameters 
such as rutting and cracking are undetectable. A further effort 
was performed using sub-meter (50cm) hyperspectral remote 
sensing data (Herold et al., 2004). They used image ratios and 
spatial variance measures and related them with road condition 
parameters such as the Pavement Condition Index. While the 
results of this research are promising, the main drawback of this 
method is that it relies only on spectral information, thus the 
results are not always accurate. Especially on older roads which 
might be subject to maintenance, the results usually tend to high 
levels of uncertainty. This suggests the need for further research 
to develop a more efficient road condition mapping strategy. 
First, the road image should contain sufficient spatial detail. 
High resolution imagery is essential to efficiently detect and 
measure features on unpaved roads. Aerial imagery can be a 
choice, but the limited maneuverability of the platform to 
acquire the image data and the associated high costs are 
shortcomings. In contrast, UAVs are highly flexible, collecting 
image data at lower cost, faster and more safely (NCRST, 
2003b). Moreover, UAVs are able to operate rather close to the 
object and acquire images with few centimeter resolution 
(Eisenbeiss, 2006), providing sufficient detail for identification 
and extraction of road condition parameters. Second, 
sophisticated methods should be developed to extract various 
road features. These methods should include the examination 
and value of spectral, contextual and edge features, and 3D 
models of road surface. These information sources can then be 
fused to derive robust and reliable road condition parameters to 
meet the operational use in transportation agencies.