Geologic hazards such as coastal erosion, landslides, seismic loading, etc. constantly threaten public highway
construction and maintenance. Repeat surveys using terrestrial laser scanning (TLS, ground-based LiDAR) enable rapid
3D data acquisition to map, see, analyze, and understand the processes generating such problems. Previously, change
detection and analysis between scan surveys was conducted during post-processing upon return to the office, instead of
while collecting data in the field. Change detection in the field improves the effectiveness and efficiency of the field
investigation. We have developed a new algorithm that quickly geo-references scans upon field acquisition and
simultaneously performs change detection by comparing these newly acquired scans to baseline models.
This algorithm has been integrated with a simple, intuitive GUI that enables change detection to be completed quickly
(<1min per scan) while data are being acquired in the field. This software has also been enhanced to ensure a productive
Implementation and testing of the algorithm is underway at several sites that have been problematic for state agencies.
Two test sites showing active movement are along Highway 101 in Oregon. These are the Spencer Creek Bridge site and
the Johnson Creek landslide. An additional trial site is the US20 Pioneer-Eddyville highway realignment project, where
several active landslides and surficial slope failures of embankment fill slopes have significantly disrupted construction
efforts. Substantial baseline information at these sites was collected, which will be useful for future Oregon Department
of Transportation and Oregon Department of Geology and Mineral Industries studies.
Performing change detection in the field offers several significant advantages to current post-processing workflows.
First, field change detection serves as an augmented reality system, enabling field crews and researchers to see immediate
results, on site, so that they are able to make key observations while present at the site, instead of being reliant on their
personal memories or notes. Second, and importantly, it can improve the overall efficiency of the survey. When this
information is available to the operator during field data acquisition, areas of minimal change can be quickly surveyed at
coarser resolutions and areas of substantial change can be scanned at higher resolutions. This also translates into reduced
processing time and data maintenance, which are currently significant hurdles for analyzing 3D laser scan datasets.
Finally, this method provides immediate validation and quality control of the RTK GPS and laser scan data being
collected, leading to more confidence in the acquired data and allowing any issues to be resolved directly in the field.