3 research outputs found
Landslide monitoring using mobile device and cloud-based photogrammetry
PhD ThesisLandslides are one of the most commonly occurring natural disasters that can cause a
serious threat to human life and society, in addition to significant economic loss.
Investigation and monitoring of landslides are important tasks in geotechnical
engineering in order to mitigate the hazards created by such phenomena. However,
current geomatics approaches used for precise landslide monitoring are largely
inappropriate for initial assessment by an engineer over small areas due to the labourintensive and costly methods often adopted. Therefore, the development of a costeffective landslide monitoring system for real-time on-site investigation is essential to aid
initial geotechnical interpretation and assessment.
In this research, close-range photogrammetric techniques using imagery from a mobile
device camera (e.g. a modern smartphone) were investigated as a low-cost, non-contact
monitoring approach to on-site landslide investigation. The developed system was
implemented on a mobile platform with cloud computing technology to enable the
potential for real-time processing. The system comprised the front-end service of a mobile
application controlled by the operator and a back-end service employed for
photogrammetric measurement and landslide monitoring analysis. In terms of the backend service, Structure-from-Motion (SfM) photogrammetry was implemented to provide
fully-automated processing to offer user-friendliness to non-experts. This was integrated
with developed functions that were used to enhance the processing performance and
deliver appropriate photogrammetric results for assessing landslide deformations. In
order to implement this system with a real-time response, the cloud-based system required
data transfer using Internet services via a modern 4G/5G network. Furthermore, the
relationship between the number of images and image size was investigated to optimize
data processing.
The potential of the developed system for monitoring landslides was investigated at two
different real-world UK sites, comprising a natural earth-flow landslide and coastal cliff
erosion. These investigations demonstrated that the cloud-based photogrammetric
measurement system was capable of providing three-dimensional results to subdecimeter-level accuracy. The results of the initial assessments for on-site investigation
could be effectively presented on the mobile device through visualisation and/or
statistical quantification of the landslide changes at a local-scale.Royal Thai Government and Naresuan
University for the scholarship and financial suppor
Detection and elimination of rock face vegetation from terrestrial LIDAR data using the virtual articulating conical probe algorithm
A common use of terrestrial lidar is to conduct studies involving change detection of natural or engineered surfaces. Change detection involves many technical steps beyond the initial data acquisition: data structuring, registration, and elimination of data artifacts such as parallax errors, near-field obstructions, and vegetation. Of these, vegetation detection and elimination with terrestrial lidar scanning (TLS) presents a completely different set of issues when compared to vegetation elimination from aerial lidar scanning (ALS). With ALS, the ground footprint of the lidar laser beam is very large, and the data acquisition hardware supports multi-return waveforms. Also, the underlying surface topography is relatively smooth compared to the overlying vegetation which has a high spatial frequency. On the other hand, with most TLS systems, the width of the lidar laser beam is very small, and the data acquisition hardware supports only first-return signals. For the case where vegetation is covering a rock face, the underlying rock surface is not smooth because rock joints and sharp block edges have a high spatial frequency very similar to the overlying vegetation. Traditional ALS approaches to eliminate vegetation take advantage of the contrast in spatial frequency between the underlying ground surface and the overlying vegetation. When the ALS approach is used on vegetated rock faces, the algorithm, as expected, eliminates the vegetation, but also digitally erodes the sharp corners of the underlying rock. A new method that analyzes the slope of a surface along with relative depth and contiguity information is proposed as a way of differentiating high spatial frequency vegetative cover from similar high spatial frequency rock surfaces. This method, named the Virtual Articulating Conical Probe (VACP) algorithm, offers a solution for detection and elimination of rock face vegetation from TLS point cloud data while not affecting the geometry of the underlying rock surface. Such a tool could prove invaluable to the geotechnical engineer for quantifying rates of vertical-face rock loss that impact civil infrastructure safety --Abstract, page iii