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3D Landslide Analysis Using Deep Learning

graph of 3D Landslide Analysis
3D Landslide Analysis

Reliable and accurate monitoring of landslides is critical to manage and mitigate potential hazards and risk to communities and their infrastructure. Continued advances in surveying and remote sensing technologies [1,2,3] have enabled frequent collection of high resolution, high accuracy data with the potential to measure 3D landslide movement with superior spatial resolution compared to conventional methods such as inclinometers, extensometers and GNSS monitoring [4,5,6].

Nevertheless, in practice, remote sensing data analysis approaches tend to be either overly simplistic in nature or require intensive manual processing such as expert development of site-specific data processing and parameter derivation.

Simultaneously, recent advances in the field of computer vision have demonstrated the suitability of deep learning approaches to RGB image- and video-based optical flow problems, which now achieve amongst the best performance on many widely used testing datasets [7,8,9,10]. Building on these advances, this paper develops and rigorously validates a deep learning approach for the task of landslide displacement mapping using geospatial DEMs (Digital Elevation Models) derived from remote sensing methods.

From a paper in mdpi.com by Andrew Senogles, et al.

In situ landslide monitoring can include drilling boreholes to house inclinometers, piezometers and other instrumentation to characterize the driving mechanisms, modes, and extent of failure. These instruments produce displacement readings with depth, which can be used to further understand the landslide kinematics, assist with rendering informed decisions regarding landslide activity, provide estimates of damage, and identify potential mitigation strategies.Notwithstanding, subsurface exploration is time-consuming, potentially hazardous, and often cost-prohibitive, especially for stakeholders burdened with numerous landslides or large landslides.

Due to these challenges, subsurface exploration and instrumentation often can only be conducted at several discrete locations within the landslide body and thus relies heavily on interpolation methods to infer landslide properties across its entire spatial extent. The installed instrumentation is also subject to shearing/damage under modest movements and thus does not serve as a permanent monitoring solution.

Remote sensing and surveying monitoring approaches are increasingly used in landslide monitoring and many other earth science applications. TLS (terrestrial laser scanning), for example, can produce high-resolution point clouds useful for monitoring slope deformation [11,12,13].

More recently, UAS lidar systems have become capable of mapping the surface of large landslides while maintaining satisfactory accuracy and coverage [14]. Photogrammetric data collected via UAS platforms can generate both high-resolution, orthorectified images, and point clouds using SfM (structure from motion) and MVS (multiview stereopsis) techniques [15]. Photogrammetric technology typically have a lower cost of entry compared to lidar and can provide a similar level of accuracy to UAS lidar methods for sparsely vegetated terrain if appropriate data collection and processing methods are followed [16].

For the complete article on 3D landslide analysis CLICK HERE.

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