The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B5. Beijing 2008 
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2. ROAD CONDITION ASSESSMENT AND 
MONITORING STRATEGY 
This paper explores the potential for applying remote sensing 
technology to monitoring the condition of unpaved roads. It 
aims to develop an efficient yet cost-effective method for 
maintaining statewide information on the condition of unpaved 
roads. The strategy for monitoring the condition of unpaved 
roads is a hybrid approach comprising two interrelated systems: 
UAV-based Remote Sensing of Road Condition (UAVRS), and 
Predictive Road Condition Modeling using Remote Sensing 
Data (PRCM). UAVRS will acquire road imagery with high 
resolution from an UAV platform, and assess roads based on 
the condition parameters derived through the development of 
sophisticated algorithms for image processing and analysis. In 
PRCM, the road condition data are produced by developing a 
robust road condition model using satellite derived environment 
data and other road data. The extracted road condition 
parameters in UAVRS will be directly used in local 
transportation agencies for road condition monitoring and 
maintenance. In addition, these data will also be used in the 
development of the statistical model in PRCM as field data for 
training and verification. Furthermore, the statistical model that 
will be developed in PRCM will be constantly updated and 
improved by the observation data and the derived parameters 
provided in UAVRS, leading to an operational system for road 
condition monitoring. The details of the strategy can be found 
in Zhang (2008). 
The UAV-based remote sensing system focuses on acquiring 
imagery using a UAV and extracting information from the 
image data. 
Figure 1. Proposed UAV-based remote sensing system for 
unpaved road condition assessment. 
In addition to operation of UAV for road image collection, the 
development of the processing will include methods for 
accurate georeferencing, automated extraction of accurate 3D 
road surface model, and generation of high resolution 
orthoimages. Afterwards, a set of image processing algorithms 
will be developed to detect and extract features related to road 
surface and condition. The main features include length and 
size of corrugation, cross section geometry, rutting, potholes, 
secondary ditches, and road surface roughness, etc. Examples of 
deteriorate unpaved roads are given in Figure 2. Based on the 
extracted features, we will derive information about road 
quality to enable advanced warning of road deterioration. 
Figure 2. Ground images of deteriorated unpaved roads. Above: 
Serious distress with potholes and bad surface drainage. Below: 
Loose aggregate with corrugation. 
3. UAV SYSTEM AND SENSORS 
3.1 Airframe 
We are currently operating an Airstar International Mongoose 
airframe helicopter. The airframe is a modified radio controlled 
helicopter with primary flight systems consisting of the engine 
and drive train, main rotor and tail rotor assembly, control 
actuators, and structural components. The airframe is powered 
by a 26cc, single cylinder, Zenoah G260H engine producing 
approximately 1940W at 12,000rpm, providing an operating 
head speed of approximately 1250-1500rpm. 
The weight of the Mongoose airframe is approximately 6.1kg 
dry, and the payload the airframe is capable of carrying is 
approximately 6.4kg. The fuel capacity is 475cc allowing 
approximately 45 minutes of flight without payload, and 
approximately 30 minutes of flight carrying full payload. The 
battery powering all onboard electronic components will 
provide approximately 90 minutes of power-on time for the 
entire system (Raunaq et al., 2007). 
3.2 Autonomous Flight Controller 
Autonomous navigation and control of the main vehicle is 
achieved via the combination of the Rotomotion Automatic 
Flight Control System hardware (AFCS) and custom Mission 
Control System software (MCS). The combination of these two 
systems allows for designing and executing pre-programmed 
waypoint paths, monitoring mission-specific intelligent control 
software, and maintaining full control of the vehicle at all times.