Rockfall detection using LiDAR and deep learning

Farmakis, Ioannis; DiFrancesco, Paul-Mark; Hutchinson, D. Jean; Vlachopoulos, Nicholas

doi:10.1016/j.enggeo.2022.106836

Cited by 20 publications

(9 citation statements)

References 23 publications

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“…Many of these false positives were a result of shadows or other lighting differences between dates being compared. Reliable methods of cluster identification and classification are required for the methods presented in this study to be fully automated and applicable to real-world scenarios where rigorous manual validation is not practical [46,47]. Relatedly, almost half of the validated changes over 5 m 3 corresponded to slope-ravel-type processes where there was no identifiable or coherent rock block that moved.…”

Section: Prevalence Of False Positive Clusters and Monitoring Methods...mentioning

confidence: 99%

High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations

Butcher,

Walton,

Kromer

et al. 2023

Remote Sensing

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Research on high-temporal-resolution rock slope monitoring has tended to focus on scenarios where spatial resolution is also high. Accordingly, there is a lack of understanding of the implications for rock slope monitoring results in cases with high temporal resolution but low spatial resolution, which is the focus of this study. This study uses automatically captured photos taken at a daily frequency by five fixed-base cameras in conjunction with multi-epoch Structure-from-Motion (SfM) photogrammetric processing techniques to evaluate changes in a rock slope in Majes, Arequipa, Peru. The results of the monitoring campaign demonstrate that there are potential issues with the common notion that higher frequency change detection is always superior. For lower spatial resolutions or when only large changes are of concern, using a high-frequency monitoring method may cause small volume changes that eventually aggrade into larger areas of change to be missed, whereas most of the total volume change would be captured with lower-frequency monitoring intervals. In this study, daily change detection and volume calculation resulted in a cumulative rockfall volume of 4300 m3 over about 14 months, while change detection and volume calculation between dates at the start and end of the 14-month period resulted in a total rockfall volume of 12,300 m3. High-frequency monitoring is still the most accurate approach for evaluating slope evolution from a rockfall frequency and size distribution perspective, and it allows for the detection of short accelerations and pre-failure deformations, but longer-term comparison intervals may be required in cases where spatial resolution is low relative to temporal resolution to more accurately reflect the total volume change of a given rock slope over a long period of time.

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Section: Prevalence Of False Positive Clusters and Monitoring Methods...mentioning

confidence: 99%

High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations

Butcher,

Walton,

Kromer

et al. 2023

Remote Sensing

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“…Satellite interferometry [ 59 ] and laser scanning are other technologies used for remote land-movement monitoring [ 60 , 61 ]. These solutions offer good performance for movement assessment but, in some cases, are not accessible and have moderate costs.…”

Section: Rockfall and Landslide Monitoring Based On Wsnsmentioning

confidence: 99%

Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review

Ragnoli,

Scarsella,

Leoni

et al. 2023

Sensors

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Rockfalls and landslide events are caused by different factors among which are included geomorphological and climatic factors and also human interaction. Therefore, the economic and social impacts can be significant and the remote monitoring of such hazards has become an essential topic in various applications. Wireless sensor networks (WSNs) are well suited for the deployment of monitoring systems, benefiting from the different technologies and topologies that are available and evolving nowadays. This review paper aims to summarize and overview the up-to-date state of the art of rockfall and landslide monitoring systems based on WSNs. The implementation and methods were analyzed for each solution, along with the system architecture and relevant hardware aspects. All the retrieved data were used to analyze the current trends and future possibilities in the field of WSN geohazard monitoring.

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“…Normal faults and shear zones cut the sedimentary layers along the slopes, creating fa-vorable conditions for rockfalls. More details about the geologic setting and the failure patterns and mechanisms can be found in [30,35].…”

Section: Mile 109mentioning

confidence: 99%

“…The rockfall database utilized in this study resulted from a 5-year-long monitoring program that was initiated after intense rockfall activity caused severe problems for the underlying infrastructure. It includes 24 instances of the rock slope with a point spacing of 5 cm acquired periodically between 2013 and 2018 and more than 3400 recorded rockfall events of varying magnitude and shape [35].…”

Section: Mile 109mentioning

confidence: 99%

Slope-Scale Rockfall Susceptibility Modeling as a 3D Computer Vision Problem

et al. 2023

Self Cite

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Rockfall constitutes a major threat to the safety and sustainability of transport corridors bordered by rocky cliffs. This research introduces a new approach to rockfall susceptibility modeling for the identification of potential rockfall source zones. This is achieved by developing a data-driven model to assess the local slope morphological attributes with respect to the rock slope evolution processes. The ability to address “where” a rockfall is more likely to occur via the analysis of historical event inventories with respect to terrain attributes and to define the probability of a given area producing a rockfall is a critical advance toward effective transport corridor management. The availability of high-quality digital volumetric change detection products permits new developments in rockfall assessment and prediction. We explore the potential of simulating the conceptualization of slope-scale rockfall susceptibility modeling using computer power and artificial intelligence (AI). We employ advanced 3D computer vision algorithms for analyzing point clouds to interpret high-resolution digital observations capturing the rock slope evolution via long-term, LiDAR-based 3D differencing. The approach has been developed and tested on data from three rock slopes: two in Canada and one in the UK. The results indicate clear potential for AI advances to develop local susceptibility indicators from local geometry and learning from recent rockfall activity. The resultant models produce slope-wide rockfall susceptibility maps in high resolution, producing up to 75% agreement with validated occurrences.

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Rockfall detection using LiDAR and deep learning

Cited by 20 publications

References 23 publications

High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations

High-Temporal-Resolution Rock Slope Monitoring Using Terrestrial Structure-from-Motion Photogrammetry in an Application with Spatial Resolution Limitations

Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review

Slope-Scale Rockfall Susceptibility Modeling as a 3D Computer Vision Problem

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