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Despite the good results that have been achieved in unimodal segmentation, the inherent limitations of individual data increase the difficulty of achieving breakthroughs in performance. For that reason, multi-modal learning is increasingly being explored within the field of remote sensing. The present multi-modal methods usually map high-dimensional features to low-dimensional spaces as a preprocess before feature extraction to address the nonnegligible domain gap, which inevitably leads to information loss. To address this issue, in this paper we present our novel I mbalance K nowledge- D riven Multi-modal Net work (IKD-Net) to extract features from multi-modal heterogeneous data of aerial images and LiDAR directly. IKD-Net is capable of mining imbalance information across modalities while utilizing a strong modal to drive the feature map refinement of the weaker ones in the global and categorical perspectives by way of two sophisticated plug-and-play modules: the G lobal K nowledge- G uided (GKG) and C lass K nowledge- G uided (CKG) gated modules. The whole network then is optimized using a joint loss function. While we were developing IKD-Net, we also established a new dataset called the N ational Agriculture Imagery Program and 3 D Elevation Program C ombined dataset in California (N3C-California) , which provides a particular benchmark for multi-modal joint segmentation tasks. In our experiments, IKD-Net outperformed the benchmarks and state-of-the-art methods both in the N3C-California and the small-scale ISPRS Vaihingen dataset. IKD-Net has been ranked first on the real-time leaderboard for the GRSS DFC 2018 challenge evaluation until this paper's submission. Our code and N3C-California dataset are available at https://github.com/wymqqq/IKDNet-pytorch. [ABSTRACT FROM AUTHOR]

Geofoam with good buffer performance and low density is proposed to replace part of the sand, forming a composite cushion to resist rockfall impact. On the other hand, falling rock is usually variable and irregular in shape. In this study, laboratory tests and numerical research are conducted to study the buffer performance of geofoam, as well as the effect of the rock shape. When the rock shape changes from the flat form to the cone form, more time is needed to undergo the impact process and the maximum impact force decreases. Thicker geofoam is advantageous for reducing the impact force. However, the decrease degree is affected by the rock shape. Both the geofoam thickness and the rock shape have an obvious effect on the maximum deformation and the vertical stress in the geofoam. Thicker geofoam can amplify the influence of the rock shape on the stress in the beam. Accordingly, in the design of an effective composite cushion in a rock-shed, the geofoam thickness necessarily requires appropriate determination to meet both the buffer performance and the cushion deformation. Furthermore, the rock shape plays a crucial role in evaluating the buffer performance of the composite cushion. • A series of laboratory impact tests are carried out to discuss the impact process of falling rocks. • A numerical model is validated by a large-scale physical experiment and the laboratory impact tests. • The effect of geofoam thickness and rock shape on the dynamic responses of rock-shed is discussed. [ABSTRACT FROM AUTHOR]

• The Bafoussam-Dshang region in West Cameroon Highlands records numbers of landslides. • The Analytical Hierarchical Process (AHP) delineate the landslide susceptibility. • Geology, orography, hydrography and vegetation cover are landslide causative factors. • Landslides susceptibility defines the Low, Moderate, High, and Very High classes. • Landslides fall into the high to very high susceptibility zone at ∼ 87% in the BDR. The main goal of this study is to produce a landslide susceptibility map of the Bafoussam-Dschang region (BDR) which is often subjected to landsliding by using Analytical Hierarchical Process (AHP). To achieve this objective, landslide triggering factors such as the lithology, slope, soil, land use, flow density, curvature, elevation and aspect have been combined with field observations. These Parameters were plotted on thematic layers using Geographical Information System (GIS) according to the AHP principles. Factors of weights were attributed based on the frequency of mass movements. The impact of one to another was assessed, allowing weights to be assigned to the different parameters. Subsequently, field observations revealed 100 mass movements including 97 landslides, 01 mudflow, 01 rock fall and 01 collapse within - the BDR. The field data including remote sensing techniques have subdivided the landslides susceptibility map into "Low (3%)", "Moderate (10%)", "High (21%)", and "Very High (66%)" susceptibility zones respectively. The majority of these mass movements fall into the high to very high susceptibility zones equivalent to ∼ 87% of landslides. These results permit to identify the landslide susceptibility zones linked to the steep landscaping West Cameroon Highland (Cameroon Volcanic Line). [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR)-derived point cloud has become the standard spatial data for digital terrain model (DTM) construction; however, it suffers from huge data with much redundant information due to the oversampling. This often causes considerable inconvenience in the downstream data processing. To this end, an adaptive coarse-to-fine clustering and terrain feature-aware-based method is proposed to reduce data points in the context of terrain modeling in this paper. Firstly, a coarse-to-fine clustering method with the consideration of terrain complexity is developed to adaptively cluster LiDAR terrain points. Then, according to the geometric properties of terrain breaklines, a terrain feature-aware multi-strategy method is presented to pick representative points in the clusters. Finally, important boundary points including inflection point on the boundary curve and critical point on terrain features are further selected. The proposed method is compared with seven state-of-the-art point cloud simplification methods under six data reduction ratios on six plots with different terrain characteristics. Results indicate that the proposed method obtains a good balance between terrain-feature preservation and uniform distribution of data points. Compared to the state-of-the-art methods, the proposed method reduces the average root mean square errors (absolute errors) of the DTMs by 12.2%-51.7% (7.69%-83.8%) on the six plots. Moreover, the proposed method obtains the mean terrain slope and terrain roughness more reasonably approximate to the references. In short, the newly developed method can be considered as an alternative tool to select representative points from the huge remote-sensing-derived point cloud in the context of DTM production. [ABSTRACT FROM AUTHOR]

Although tremendous strides have been made in object recognition over large-scale 3D point clouds, one of the remaining open challenges is detecting tiny objects. The signal and appearance information of tiny objects are generally sparse and insufficient due to their tiny size, which makes the tiny object difficult to be detected with existing studies both in tiny 2D object recognition and general 3D object recognition since 3D topology relations are lost due to feature projection in tiny 2D object recognition and massive background may dominate the feature learner in general 3D object recognition. To the best of our knowledge, we explore tiny object recognition for the first time over large-scale 3D point clouds by designing deep neural network. To cope with this problem, a novel two-stage approach is proposed in this paper, which can effectively learn an informative and discriminative feature representation and efficiently process large-scale point clouds. Specifically, a class-aware filtering strategy is designed to dispense with the redundant background information. After that, we introduce a novel aggregating and sampling scheme in a supervised manner to progressively increase the receptive field for each 3D point, which can adaptively capture useful signals from tiny objects and learn complex geometric structures. Last, we propose a biased loss function that is inversely correlated to the number of tiny object points, thereby effectively handling the sparsity of tiny objects. Extensive experimental results over two real-world data sets validate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

For large-scale point cloud semantic segmentation, the relationships between long-range neighbourhoods are as important as short-range features. The current methods focus on aggregating the fine-grained geometric structures of local regions, the long-range features are usually ignored. To this end, we propose a novel framework named LG-Net that can efficiently learn local dual features and global correlations from point clouds. We adopt random sampling to decrease point density, thereby processing large-scale point cloud efficiently. Specifically, we propose a dual feature complementary (DFC) module that makes geometric and semantic features complementary to preserve local information. We utilize positional relations to capture geometric details, and aggregate semantic features according to the similarity of neighbours. The global correlation mining (GCM) module simply shares an attention map for all 3D points to learn long-range contextual information. We conducted extensive experiments on public datasets, including indoor scenes (S3DIS, ScanNet) and outdoor scenes (SemanticKITTI, SensatUrban). Experimental results demonstrate that our method outperforms the state-of-the-art approaches. [Display omitted] • Improving 3D point cloud semantic segmentation performance. • Random sampling strategy can reduce the memory footprint and computational cost. • DFC module considers the complementary of geometric and semantic features. • GCM module uses the attention strategy for obtaining long-range dependencies. [ABSTRACT FROM AUTHOR]

Over recent decades, 3D point clouds have been a popular data source applied in automatic change detection in a wide variety of applications. Compared with 2D images, using 3D point clouds for change detection can provide an alternative solution offering different modalities and enabling a highly detailed 3D geometric and attribute analysis. This article provides a comprehensive review of point-cloud-based 3D change detection for urban objects. Specifically, in this study, we had two primary aims: (i) to ascertain the critical techniques in change detection, as well as their strengths and weaknesses, including data registration, variance estimation, and change analysis; (ii) to contextualize the up-to-date uses of point clouds in change detection and to explore representative applications of land cover and land use monitoring, vegetation surveys, construction automation, building and indoor investigations, and traffic and transportation monitoring. A workflow following the PRISMA 2020 rules was applied for the search and selection of reviewed articles, with a brief statistical analysis of the selected articles. Additionally, we examined the limitations of current change detection technology and discussed current research gaps between state-of-the-art techniques and engineering demands. Several remaining issues, such as the reliability of datasets, uncertainty in results, and contribution of semantics in change detection, have been identified and discussed. Ultimately, this review sheds light on prospective research directions to meet the urgent needs of anticipated applications. [ABSTRACT FROM AUTHOR]

Registering point clouds of forest environments is an essential prerequisite for LiDAR applications in precision forestry. State-of-the-art methods for forest point cloud registration require the extraction of individual tree attributes, and they have an efficiency bottleneck when dealing with point clouds of real-world forests with dense trees. We propose an automatic, robust, and efficient method for the registration of forest point clouds. Our approach first locates tree stems from raw point clouds and then matches the stems based on their relative spatial relationship to determine the registration transformation. The algorithm requires no extra individual tree attributes and has quadratic complexity to the number of trees in the environment, allowing it to align point clouds of large forest environments. Extensive experiments on forest terrestrial point clouds have revealed that our method inherits the effectiveness and robustness of the stem-based registration strategy while exceedingly increasing its efficiency. Besides, we introduce a new benchmark dataset that complements the very few existing open datasets for the development and evaluation of registration methods for forest point clouds. The source code of our method and the dataset are available at https://github.com/zexinyang/GlobalMatch. [ABSTRACT FROM AUTHOR]

Point cloud registration is a research field where the spatial relationship between two or more sets of points in space is determined. Point clouds are found in multiple applications, such as laser scanning, 3D reconstruction, and time-of-flight imaging, to mention a few. This paper provides a thorough overview of recent advances in learning-based 3D point cloud registration methods with an emphasis on non-rigid transformations. In this respect, the available studies should take various challenges like noise, outliers, different deformation levels, and data incompleteness into account. Therefore, a comparison study on the quantitative assessment metrics and robustness of different approaches is discussed. Furthermore, a comparative study on available datasets is reviewed. This information will help to understand the new range of possibilities and to inspire future research directions. • A review of point cloud registration methods for deep learning approaches. • A study on the existing point cloud registration methods based on non-rigid transformations. • A comparative study on available datasets and their characteristics for point cloud registration. • A study on evaluation metrics and performance indicators for point cloud registration. • A study on how methods tackle the challenges, and how robust to challenges they are. [ABSTRACT FROM AUTHOR]

A High-Definition (HD) map is an essential component not only for autonomous vehicles but also for surveyors in favor of city management. Moreover, the HD map must be up-to-date by reflecting environmental changes continuously. The development of LiDAR Simultaneous Localization And Mapping (SLAM) techniques has provided an effective way to collect precise point clouds for the construction of HD maps, while traditional methods are time-consuming and labor-intensive on account of the complex street scene. Furthermore, life-long 3D mapping is quite necessary and challenging nowadays. In this paper, we introduce a novel change-aware 3D online mapping framework CAOM, 1 1 Code at https://github.com/DCSI2022/CAOM. using point clouds collected by Multi-Beam LiDAR (MBL) and Push-Broom LiDAR (PBL) for rapid city map update and change detection. Concerning the prior map with higher precision, heterogenous fusion is performed to rectify the SLAM drift, based on the View-Traced Clouds (ViTC) derived according to their view in common. Meanwhile, corresponding virtual range images are integrated with distance images to detect the 3D changes between the current map and the historical reference map offline. The detection results are later fused in a probabilistic way along with the homogeneous fusion process where a resilient graph consisting of diverse factors is established to maintain the local and global consistency of the final point cloud map. Compared with 3D-CSTM, the real-world experiments have shown an average 0.5 m and max 1.5 m improvement in the ATE (Absolute Translation Error) of the trajectory generated by our system. Quantitative measurements on the individual point accuracy are also conducted to verify the performance of 3D mapping with up to 16% improvement in comparison with respect to the 3D-CSTM method. The effects of the disparity detection and probabilistic fusion can be revealed from the small objects distinguished in the point cloud map as well as > 95% overall accuracy. [ABSTRACT FROM AUTHOR]

Automatic registration of multiple point clouds is a significant preprocessing step for 3D computer vision tasks including semantic segmentation, 3D modelling, change detection, etc. Many methods were proposed to deal with this problem and yet most of them are not fully utilizing the redundant information offered by multiple common overlaps among point clouds. The existing methods are also inefficient when dealing with large-scale point clouds. In this paper, a novel automatic registration framework is presented to align point clouds efficiently and robustly. First, the overall number of scans is grouped into several scan-blocks by a proposed blocking strategy, and we build the pairwise relationship among scans through a fully connected graph in each scan-block. Second, perform loop-based coarse registration in each scan-block using a proposed false matches removal strategy. The proposed strategy can effectively identify grossly wrong scan-to-scan matches. Third, the minimum spanning tree is extracted from the graph, and ICP is applied along its edges. Moreover, the Lu–Milios algorithm is used to further optimize all poses at once by utilizing all redundant information in each scan-block. Finally, global block-to-block registration aligns all scan-blocks into a uniform coordinate reference. We test our framework on challenging WHU-TLS datasets, ETH datasets, and Robotic 3D Scan datasets to evaluate the efficiency, accuracy, as well as robustness. The experiment results show that our method achieves the state-of-the-art accuracy, while the time performance is improved by more than 30% compared with the state-of-the-art algorithms. Our source code is made available at https://github.com/WuHao-WHU/HL-MRF for benchmarking purposes. [ABSTRACT FROM AUTHOR]

Point clouds contain high detail and high accuracy geometry representation of the scanned Earth surface parts. To manage the huge amount of data, the point clouds are traditionally organized on location and map-scale; e.g. in an octree structure, where top-levels of the tree contain few points suitable for small scale overviews and lower levels of the tree contain more points suitable for large scale detailed views. The drawback of this solution is that it is based on discrete levels, causing visual artifacts in the form of data density shocks when creating the commonly used perspective views. This paper presents a method based on an optimized distribution of points over continuous levels, avoiding the visualization shocks. The traditional distribution ratio's of data amounts over discrete levels of raster or vector data is considered the reference. How to convert this to point clouds with continuous levels (still benefiting from the proven advantages of the data distribution in discrete levels for efficient access at a wide range of scales)? In our solution, for each point a cLoD (continuous Level of Detail) value is computed and added as dimension to the point. A SFC (Space Filling Curve)-based nD data clustering technique can be used to organize the points, so that they can be efficiently queried. It should be noted that also other multi-dimensional indexing and clustering techniques could be applied to realize continuous levels based on the cLoD value. Besides the mathematical foundation of the approach also several implementations are described, varying from a 3D web-browser based solution to an augmented reality point cloud app in a mobile phone. The cLoD enables interactive real-time visualization using perspective views without data density shocks, while supporting continuous zoom-in/out and progressive data streaming between server and client. The described cLoD based approach is generic and supports different types of point clouds: from airborne, terrestrial, mobile and indoor laser scanning, but also from dense matching optical imagery or multi-beam echo soundings. [ABSTRACT FROM AUTHOR]

Local convolutional operation and global attention-based transformer operation can extract features of the point cloud from two different scales respectively, but few methods can combine them effectively. In this paper, we propose a novel point cloud feature extractor that has the advantages of both convolutional and transformer operation, which is named as CTpoint. Specifically, CTpoint is composed of two branches, where "C" represents the convolutional branch and "T" represents the transformer branch. The convolutional branch is responsible for extracting the local feature from grouped neighbor points, and the transformer branch performs the attention operation on the entire point cloud to capture the global feature. To make the two branches communicate with each other, the Feature Transmission Element (FTE) is proposed for the alignment of the spatial-feature dimension and the semantic space between the local and global features, so that the extracted local and global features can be effectively fused and the two branches can coordinately learn expressive features. We utilize CTpoint to construct point cloud classification and segmentation networks and evaluate their performance in several public datasets. In addition, we visualize the feature learned by CTpoint. The experimental results show that the expressive features learned by CTpoint make the networks achieve the state of the art performance on the point cloud classification and segmentation tasks. [ABSTRACT FROM AUTHOR]

Most existing 3D geometry copy detection research focused on 3D watermarking, which first embeds "watermarks" and then detects the added watermarks. However, this kind of methods is non-straightforward and may be less robust to attacks such as cropping and noise. In this paper, we focus on a fundamental and practical research problem: judging whether a point cloud is plagiarized or copied to another point cloud in the presence of several manipulations (e.g., similarity transformation, smoothing). We propose a novel method to address this critical problem. Our key idea is first to align the two point clouds and then calculate their similarity distance. We design three different measures to compute the similarity. We also introduce two strategies to speed up our method. Comprehensive experiments and comparisons demonstrate the effectiveness and robustness of our method in estimating the similarity of two given 3D point clouds. [ABSTRACT FROM AUTHOR]

• A BTreeNet with a novel forward propagation based on the binary tree is proposed. • A novel IBTreeNet is proposed to continuously improve the registration accuracy. • We adopt Chamfer and Earth Mover's Distances as the loss for unsupervised learning. • Our networks are tolerant to partiality and noise without training in such scenes. 3D point cloud registration is a computational process that aligns two 3D point clouds through transformation. i.e. finding matching translation and rotation. Existing state-of-the-art learning-based methods require ground-truth transformation as supervision and often perform poorly in dealing with partial point clouds and large scenes that are not trained, resulting in poor generalization for neural networks. In this paper, we propose a novel unsupervised deep learning network - Binary Tree Network (BTreeNet) that consists of a novel forward propagation, which learns features for the rotation separately from the translation and avoids the interference between the estimations of rotation and translation in one single matrix. We then propose an Iterative Binary Tree Network (IBTreeNet) to continuously improve the registration accuracy for large and dense 3D point clouds. The Chamfer Distance and the Earth Mover's Distance are adopted as the loss function for unsupervised learning. We show that BTreeNet and IBTreeNet outperform state-of-the-art learning-based and traditional methods on partial and noisy point clouds without training them in such scenarios. Most importantly, the proposed methods exhibit remarkable generalization and robustness to unseen large and dense scenes that are never trained. [ABSTRACT FROM AUTHOR]

As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent. In this paper, we propose a simple and effective augmentation method for the point cloud data to alleviate those problems. It finds the one-to-one correspondence between two point clouds and generates new training data by replacing the points in one sample with their corresponding pairs. Two replacement strategies are proposed to adapt to the accuracy or robustness requirement for different tasks, one of which is to randomly select all replacing points while the other one is to select k nearest neighbors of a single random point. Both strategies consistently and significantly improve the performance of various models on point cloud classification problems. By introducing the saliency maps to guide the selection of replacing points, the performance further improves. Since our method persists local semantic information of the points, we also at the first time extend MSDA for the point cloud segmentation problem. Moreover, PointCutMix is validated to enhance the model's robustness. When using as a defense method, our method outperforms the state-of-the-art defense algorithms. The code is available at: https://github.com/cuge1995/PointCutMix. [ABSTRACT FROM AUTHOR]

Detecting accurate 3D bounding boxes of the object from point clouds is a major task in autonomous driving perception. At present, the anchor-based or anchor-free models that use LiDAR point clouds for 3D object detection use the center assigner strategy to infer the 3D bounding boxes. However, in the real-world scene, due to the occlusions and the effective detection range of the LiDAR system, only part of the object surface can be covered by the collected point clouds, and there are no measured 3D points corresponding to the physical object center. Obtaining the object by aggregating the incomplete surface point clouds will bring a loss of accuracy in direction and dimension estimation. To address this problem, we propose a corner-guided anchor-free single-stage 3D object detection model (CG-SSD). Firstly, the point clouds within a single frame are assigned to regular 3D grids. 3D sparse convolution backbone network composed of residual layers and sub-manifold sparse convolutional layers are used to construct bird's eye view (BEV) features for further deeper feature mining by a lite U-shaped network; Secondly, a novel corner-guided auxiliary module (CGAM) with adaptive corner classification algorithm is proposed to incorporate corner supervision signals into the neural network. CGAM is explicitly designed and trained to estimate locations of partially visible and invisible corners to obtain a more accurate object feature representation, especially for small or partial occluded objects; Finally, the deep features from both the backbone networks and CGAM module are concatenated and fed into the head module to predict the classification and 3D bounding boxes of the objects in the scene. The experiments demonstrate that CG-SSD achieves the state-of-art performance on the ONCE benchmark for supervised 3D object detection using single frame point cloud data, with 62.77% mAP. Additionally, the experiments on ONCE and Waymo Open Dataset show that CGAM can be extended to most anchor-based models which use the BEV feature to detect objects, as a plug-in and bring +1.17%∼+14.23% AP improvement. The code is available at https://github.com/mrqrs/CG-SSD. [ABSTRACT FROM AUTHOR]

This study investigates the feasibility of implementing simple Machine Learning models to make fast and reliable predictions of rockfall energies and run-outs at the base of highwalls. Probabilistic rockfall simulations are performed to generate a synthetic dataset of rockfall trajectories using high-resolution 3D photogrammetric models of fifteen highwalls from open pit coal mines. An automated software solution is developed to extract 2D profiles along the full strike length of the highwalls and meaningful geometrical features are identified and quantified. Four representative highwalls are considered for the model calibration and the remaining walls are used for validation. The block release position, slope local roughness and average slope angle are chosen as input parameters, whereas the kinetic energy at the first impact, the first impact position and the final run-out of the blocks at the base of the highwall are used as target parameters in developing predictive models. The application of various regression models is investigated for each target parameter and their performances are compared. A multi-linear regression model shows the best predictions for the kinetic energy at the first impact at the base of the highwall, while the first impact position and the final rockfall run-out are better predicted by a multi-non-linear regression model. Overall, the models perform very similar. The results show reasonable applicability of the approach for a fast prediction of rockfall hazard for arbitrary highwalls based on the fully automatised extraction of geometrical features from 3D photogrammetric data. [Display omitted] [ABSTRACT FROM AUTHOR]

Relationships between rockfall and environmental factors have been the focus of considerable research over the last several decades. Many prior studies have not quantified rockfall volume due to challenges in volume estimation without the use of modern remote sensing methods, and the role of longer-term conditioning factors on rockfall activity is often not considered. As part of a broader effort to evaluate rockfall hazard for slopes along the heavily trafficked Interstate-70 corridor in Colorado, this study takes advantage of a high temporal resolution lidar-derived rockfall database consisting of over five years' worth of rockfall data at a particular cut slope to evaluate the relationship between rockfall activity and environmental factors. The combination of monitoring duration and spatiotemporal monitoring resolution used allows for evaluation of both triggering and conditioning factors. Correlations are evaluated between rockfall metrics and weather metrics at a seasonal scale as well as the scale of individual monitoring intervals. Based on the results, it is interpreted that surficial rock material is weakened each Winter by freezing processes, large amounts of rockfall occur in the Spring as a result of the slope thawing out, elevated rockfall activity continues over the Summer as surficial rocks destabilized by freezing processes in the previous Winter are fully dislodged, and rockfall activity significantly reduces in the Fall due to the number of unstable blocks available to fall being significantly reduced. From comparison with the literature, we conclude this cycle is relatively consistent with rockfall trends observed in alpine environments, while more temperate regions exhibit notably different trends in rockfall seasonality. • Lidar monitoring recorded 1134 rockfalls at the Floyd Hill rock slope. • Winters with high air freezing index have larger rockfalls in the following seasons. • Rockfall is triggered by rain in Summer, mostly independent of rainfall amount. [ABSTRACT FROM AUTHOR]

Many large-scale landslides have occurred along the active Pingding-Huama fault in Zhouqu segment, Gansu, China. To better understand the failure mechanisms of these landslides, we use the Yahuokou landslide as a detailed case study. Field investigation was conducted to retrace the kinematics of the landslide and corresponding timeline of triggering mechanics. Dynamic triaxial tests were conducted to quantify the effect of rainfall and rockfall load on the physical and mechanical character of the landslide materials with in-situ stress level. Numerical simulation was used to evaluate the landslide stability under rainfall and rockfall load. The results show that a smaller initial rockfall of the limestone blocks along the upper headscarp of the landslide triggered a series of larger failure events that propagated through the greater landslide complex. We proposed that continuous rainfall and this rockfall load increased the pore water pressure and significantly reduced the shear strength parameters of the sliding materials. In addition, the rockfall load destroyed the structure of the shallow soil with buried depth <5 m, increasing the pore volume and water absorption capacity, which may cause the water content of the soil to exceed its liquid limit, and finally promoted plastic flow. Stability calculation further showed that rainfall alone was not sufficient to induce the landslide failure, but rather the coupled action of rainfall and rockfall load was needed. The conclusions drawn from this study outline complex failure mechanics of the Yahuokou landslide and may be helpful in understanding the fault-zone landslides widely distributed along the Pingding-Huama fault. • The failure kinematics of upper-part deposits of the Yahuokou landslide is retraced. • The failure of the gravel clay triggers a series of the failure events of the landslide. • The coupled action of rainfall and collapse load induces the 2019 Yahuokou landslide. [ABSTRACT FROM AUTHOR]

The trajectory of falling rock is significantly affected by its shape. In order to capture the falling rock shape, spherical harmonic function expansion method is used. The reasonable order of spherical harmonic function expansion items is determined according to the error analysis. Spherical harmonic based discrete element method is utilized to simulate the rockfall process of cylindrical and irregular shape rocks. The influences of the rock and terrain shapes on the rockfall trajectory are analyzed. The results show that the shape of falling rock can be described precisely by spherical harmonic function expansion method, and only a little data should be stored. The predicted trajectory of cylindrical rockfall is close to the experimental result. Spherical and block falling rocks usually show more regular horizontal displacement, lateral offset and jump height during the motion process. Those motion quantities for long strip and flaky falling rocks are more sensitive to their initial rotations. Terrain has a strong influence on rockfall trajectory, falling rocks are prone to move along or rest at mountain valleys. • Spherical harmonic function expansion method is used to precisely describe the shape of falling rock. • The detailed implementation of spherical harmonic based discrete element method for rockfall simulation is presented. • Spherical harmonic based discrete element method is utilized to model cylindrical rockfall and verified by comparing with experimental result. • The influences of falling rock and terrain shapes on rockfall trajectory are numerically investigated. [ABSTRACT FROM AUTHOR]

Change-detection monitoring plays a crucial role in geoscience, facilitating the examination of earth surface processes and the mitigation of potential risks due to natural hazards. A significant aspect of this monitoring involves the use of images, enabling 2D to 4D monitoring approaches. Our objective is to bridge the knowledge gap in developing very low-cost camera units by providing insights into specific products, assembly processes, and utilized codes. The presented approach involves prioritizing cost reduction albeit a trade-off in system quality. The results obtained in the study area of Puigcercós cliff in Spain demonstrates the system's efficacy in detecting rockfalls and pre-failure deformation with a notable level of detection of only 8 cm in the change detection analysis. Additionally, two system versions are presented; one emphasizing real-time image transmission, while the other provides a simpler, energy-efficient approach conducive to long-term data capture using a single battery. Both solutions showcase the potential of leveraging very low-cost technology in geohazard monitoring. [Display omitted] • Very low-cost photogrammetric system allows daily monitoring of rockfall activity (4D). • The system requires minimal attention, reducing on-site maintenance efforts. • The system allows for detailed change detection comparisons. • Despite compromising image quality, we effectively identify pre-failure deformation. [ABSTRACT FROM AUTHOR]

Rockfall is one of the recurrent geo-hazards in Pakistan and poses challenge not only to elements at risks but also to the infrastructure planners, engineers and government. In this study, rockfall susceptibility is spatially assessed in Salt Range along Motorway (M-2) within 500 m buffer zone. The events of rockfall become more frequent with damaging effect after the construction of M-2 even in the presence of certain structural measures like fences, protective walls, terraces and water diversion channels. Rockfall susceptibility was assessed using geological, meteorological, topographical, geo-morphometric conditions and rockfall events characteristics as input risk factors or spatial layers. Factor based ranking methodology was adopted in integration with geo-spatial tools for spatial assessment of rockfall risk. Similarly, importance of each risk factor is individually calculated and analysed in terms of cause and effect relation. Global Positioning System (GPS) based field measurements, landsat images, Shuttle Radar Topography Mission (SRTM) and Digital Elevation Model (DEM) is used as input data to map land cover conditions and carry out watershed analysis and slope-aspect analyses in ArcGIS10.2, respectively. The cumulative rockfall susceptibility assessment was finally done at 4-class ordinal scale (very high, high, moderate and low) by applying weighted overly analysis in GIS environment. Analysis revealed that very high susceptibility zone is in the lower southern part of the study area- attributed mainly to the fault, porous sedimentary rocks of laterite, sand stone and shale. This study carries great importance in rockfall risk reduction through structural and non-structural measures within Salt Range vis-à-vis other mountainous regions in Pakistan. In addition, the current study approach can be utilized effectively to prioritize and geo-visualize the severity of potential future rockfall risk along other highways having similar geological and geomorphological settings. • Rockfall susceptibility is spatially assessed using geological, meteorological, topographical, geo-morphometric conditions. • Great importance in rockfall risk reduction through structural and non-structural measures within Salt Range vis-à-vis other mountainous regions in Pakistan. • Multi factors (Geology, Slope, Water, transportation etc) are responsible for the development of rockfall phenomenon. [ABSTRACT FROM AUTHOR]

As a frequent occurring geomorphological process characterized by high energy and mobility, the rockfall movement is the basis of the disaster prediction and mitigation. In this study, the kinematic evolution behaviors of rockfalls are investigated using three-dimensional discontinuous deformation analysis (3D DDA) based on block kinematics. A series of field experiments are designed by orthogonal experimental method to study the block movement considering the influence of the slope angle, the characteristics of the block itself and the initial falling conditions. Selecting the combination of factors that have the most significant influence on the block movement, the 3D DDA numerical model is established and the effectiveness of 3D DDA is verified by comparing the displacement and kinetic energy evolutions obtained by experiments. Then, the 3D DDA is applied to simulate the rockfalls of Wangxia slope in Chongqing, and the kinematic evolutions of the entire rockfall processes are further studied by analyzing the rock mass instability and the movement trajectory and kinetic energy evolutions of blocks. The experimental results show that slope angle has the greatest influence on block kinematics, followed by block mass, falling height, block shape and falling angle. In the practical slope analysis, the rockfall processes consist of sliding failure, oblique projectile movement, flying over or colliding with the highway, and flying away from the slope, showing 3D movement characteristics due to lateral translation and rotation. The rockfall kinematic evolutions induce slope disasters to the highway, and pose a threat to shipping in Yangtze River channel. The 3D DDA method can quantitatively analyze the failure of dangerous rock masses and the kinematic evolutions of rockfalls after instability, which is helpful to provide revealing insights into the rockfall disaster processes and mechanisms. [ABSTRACT FROM AUTHOR]

The particle essence of the discrete element method could prevent the rock block from sliding on the slope surface due to the stress concentration between the convex particles of the sliding block and the slope. To eliminate this sliding blockage effect and simulate more realistic rockfall, slope surface was transformed into a series of successive joints by the discrete fracture network technique, and a damping joint model was developed by adding a normal dashpot and a shear dashpot on the smooth joint model that does not have damping properties. The motion processes of sliding, rolling, and collision were simulated by the proposed method, and the damping and movement performances were investigated. The macroscopic sliding and rolling friction coefficients significantly increase with the microscopic friction coefficient and the shear damping ratio. The normal restitution coefficient of collision decreases with the normal damping ratio, while the shear restitution coefficient of collision decreases with both the shear damping ratio and the friction coefficient. Furthermore, the simulation of a rockfall case demonstrates the proposed method represented a more realistic movement process and more plentiful characteristics of collision, sliding and rolling, compared with the simulated results of Rocfall software. [ABSTRACT FROM AUTHOR]

This paper proposes a novel bond model based on three-dimensional sphere discontinuous deformation analysis (SDDA) to simulate rockfall-flexible passive net interactions. At first, submatrices of the bond model added to the simultaneous equilibrium equations are derived. Then the relationship between elastic coefficient and spring stiffness for avoiding parameter calibration is built. And a failure criterion for breaking the bond between every two connected spheres is established. Four numerical tests are conducted to simulate the deformation and failure of a flexible passive net under the actions of forces and rockfall. The correctness, accuracy, and feasibility of the proposed bond model are verified by comparing the results with analytical solutions. Further, parameter sensitivity analysis is performed to investigate the influence of the anti-bending coefficient on computational stability and convergence, and to determine its reasonable value range. Finally, a simulation of flexible passive nets intercepting rockfalls on a mountain is carried out. Numerical performances show that the proposed bond model enables SDDA to properly simulate rockfall-flexible passive net interactions, providing a practical tool for rockfall mitigation in geotechnical engineering. [ABSTRACT FROM AUTHOR]

Climate change in the European Alps, in particular in the high-elevation environments, is causing an increase in mass movements and hazards. To learn more about relationships between mass movements and climate drivers, the location of the starting zone and date of the instability events need to be known. Nevertheless, not all existing inventories of mass movements are suitable for the purpose. For these reasons, we have implemented a specific inventory of mass movements which occurred in the Italian sector of the Alps at an elevation >1500 m. Currently, the inventory contains information relating to 772 mass movements. The most frequent types of processes documented are rockfall and debris/mud flows, with 279 and 191 cases respectively. The highest number of events occurred in 2022 (71 events), and an evident trend towards an increase over the years and during summer was found. This inventory is an excellent support tool for many activities that take place in and for the mountains, its consultation, both online and offline, makes the inventory suitable for use with different types of devices and can be used not only as a consultation tool on mass movements occurred in the past, but also to insert new events. This use can be particularly suitable for monitoring activities, managed by civil protection structures, municipalities, natural parks, environmental agencies, researchers, freelancers and so on. • Highlight 1 This is the first national inventory of high-elevation mass movements in the Italian Alps. • Highlight 2 The inventory contains information relating to 772 mass movements. • Highlight 3 An evident trend towards an increase in mass movements over the years and during summer was found. • Highlight 4 The inventory is an excellent support tool for many activities that take place in and for the mountains. • Highlight 5 The consultation both online and offline, makes the inventory suitable for use with different types of devices. [ABSTRACT FROM AUTHOR]

• Cosmogenic dating of moraines indicates a maximum age of 10 ka for talus slopes. • Frost cracking and permafrost are driving erosion in deglaciated alpine rockwalls. • Deglaciated rockwalls show Holocene-averaged exceeding recent erosion rates. • Paraglacial adjustment increases erosion rates in currently deglaciating rockwalls. Alpine rockwall erosion studies suggest that deglaciated rockwalls in the European Alps are eroding slower today than at earlier times in the Holocene. Explanations for this have included a waned glacial debuttressing effect since the retreat of Last Glacial ice loads and the establishment of more moderate climates, but seldom have such explanations been robustly tested. We combine field data with modelling to reconstruct changes in rockwall erosion and associated climate drivers, in an alpine valley of the European Alps since the beginning of the Holocene. Paleo (Holocene to decadal-scale) erosion rates were calculated from talus accumulation below rockwalls, and compared with recent rates (2016–2019) measured from repeat laserscan surveys of the same rockwalls. We reconstructed the glacial retreat history in the valley, and modelled Holocene changes in permafrost distribution and frost cracking using calibrated rockwall temperature reconstructions. We found that rockwalls that have been free of glacier ice since ∼10 ka experienced higher Holocene-averaged erosion rates compared to recent erosion rates. Our modelling suggests this relates to periods of higher intensities of frost cracking and cycles of permafrost aggradation and degradation in the Holocene, relative to today. For a recently deglaciated high-elevation rockwall, erosion rates were 1–2 orders of magnitude greater than the lower-elevation sites, but decayed rapidly over time since deglaciation. A high, but rapidly decaying rate results from short-lived paraglacial adjustment, permafrost thaw and high frost cracking activity. Our findings suggest that periglacial activity strongly influences the rates and patterns of erosion of deglaciated alpine rockwalls. This helps to explain why paleo Holocene deglaciated rockwall erosion rates tend to exceed recent rates in the European Alps. [ABSTRACT FROM AUTHOR]

An inappropriately maintained road cut-slope is likely to fail, resulting in landslides or falling rocks that compromise road safety. Thus, road managers need to know the location of dangerous slopes along the road in order to prevent these events from happening. In this article, we compare two different approaches for conducting the digitization of the road environment and the automatic detection and delimitation of road slopes: Mobile Laser Scanners (MLS) and Aerial Laser Scanners (ALS). The point clouds obtained using the first kind of devices are dense, rich in detail and generated from a ground perspective; the second type of scanners produce less dense clouds from a zenithal perspective. We explore what is the effect of the point cloud density and scanner point of view over the slope detection procedure. Two road segments from the Spanish A55 and A52 highways were used as study zones, and a total of 28.61 km were analyzed. Better detection and delimitation results were achieved when using the ALS data and its corresponding algorithm. It was observed that the higher point density and detail of the MLS clouds were not an advantage for the slope detection task, and that measuring the road from a terrestrial perspective affected in a negative way during the detection process: the crest of the slopes often remained unmeasured, hidden behind vegetation or man-made elements, thus resulting in the slopes not being complete in the MLS clouds. Meanwhile, the whole slope structure is scanned when the scene is measured from an aerial perspective, henceforth obtaining better detection rates despite the relatively low resolution. The findings of this study provide valuable information in the field of road asset management, and help road managers make decisions when choosing what technology to use for the data gathering process. • Different laser scanning systems were compared for measuring the road environment. • Automatic detection of critical road slopes was achieved using point clouds. • More slopes were detected using low density aerial point clouds. • Importance of the point of view and point density of the cloud are discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the relationships between hillslope stability and fabric anisotropy of brittle rock materials and the implications for landscape shaping. We use discrete element models to study the stability and failure modes of slopes made of transverse isotropic rock materials, focusing more particularly on the influence of the material orientation relative to the topographic slope. After validating the numerical approach with a limit equilibrium analytical solution in the case of an isotropic material, we modify our numerical slope models to simulate the rheological features of anisotropic gneissic rocks. Systematic exploration of the transverse isotropy plane's orientation in two dimensions (dip angle) reveals that slope collapse requires strength values that are highly dependent on the orientation of the material relative to the slope. For a 1000 m high escarpment, the stability of a slope with a fixed gradient requires strength that is one order of magnitude greater in a configuration where the isotropy plane is slightly less inclined than the topographic slope (i.e., cataclinal overdip configuration) than in a configuration where the isotropy plane is perpendicular to the slope (i.e., anaclinal configuration). Mirroring this highly variable stability according to the relative orientation of the material, four modes of deformation or gravitational instability are observed: in order of appearance, when the transverse isotropy plane orientation goes from 0 to 180° with respect to the horizontal (going from cataclinal to anaclinal configurations), the slope collapses respectively by sliding, buckling, toppling and crumbling. The crumbling mode corresponds to a very stable configuration for which the preferred ground movements will be rock falls from the cliff compared to the structurally controlled, deep-seated deformation modes leading to sliding and toppling. Despite the simplifications inherent to the numerical approach, our study highlights the essential characteristics of landslides occurring along slopes cut in transverse isotropic materials and reproduces the various instability modes observed in natural slopes. It also enables assessing their respective kinetics as well as the volumes of material they mobilize. Finally, by comparing our findings on the azimuthal variations in hillslope gradients observed along the central Himalaya (Nepal), in an area characterized by the relatively uniform orientation of the anisotropy in gneissic and mica-schist formations, we show that, even though multiple environmental factors come into play, landscape shaping is indeed strongly controlled by material anisotropy. • Anisotropic rock strength affects slope stability by foliation dip angle. • Stability and failure modes are investigated by means of discrete element models. • Sliding and buckling failures occur in cataclinal slopes. • Toppling and crumbling failures occur in anaclinal slopes. • Himalayan slopes confirm how anisotropy controls landscape evolution. [ABSTRACT FROM AUTHOR]

A digitally supported rock mass stability analysis is herein presented for the implementation of non-contact close-range remote surveys against landslide hazard in sheltered environments, such as Marine Protected Areas. An airborne photogrammetric survey was carried out at the Lachea Islet (Sicily), which is part of a small volcanic archipelago with a peculiar geological history, hosting unstable rock masses belonging to the first eruptive stages of Mount Etna. The digital model built for this study allowed extracting the discontinuity orientation data, performing a kinematic analysis along the recognized discontinuity clusters, and quantifying the volume of potentially unstable boulders. Laboratory tests allowed achieving the main physical-mechanical parameters of involved rocks, which were used to perform a quantitative stability analysis under both deterministic and probabilistic approaches, based on the extracted digital data. Finally, a 3D rockfall trajectory analysis was carried out by simulating statistical rockfall path on the digital model built for the study. Results highlighted a poor stability in the studied islet sector, which is worsened by taking into account the high seismicity that characterizes the area. The main tourist elements of the islet must be considered at risk, as they are located within the simulated trajectories of the landslides. The methodological protocol carried out for this study, based on conventional procedures but using remotely derived and digitally processed information, can be considered a useful tool in the frame of nature-based solutions for hydro-meteorological hazards connected to climatic changes. • Close-range remote surveys were used to support rock mass stability analysis. • Digital outcrop models are built from UAV photogrammetric surveys. • A Non-invasive surveying protocol is shown as a study tool at a nature reserve. • 3D rockfall simulations allowed evaluating the landslide impact on elements at risk. [ABSTRACT FROM AUTHOR]

Rockfalls are small–scale mass wasting events that have been observed across the solar system. They provide valuable information about the endo- and exogenic activity of their host body, but are difficult to identify and map in satellite imagery, especially on global scales and in big data sets. Past work implemented convolutional neural networks to automate rockfall mapping on the Moon and Mars with the caveat of (1) achieving sub–optimal performance and (2) requiring substantial manual image labeling efforts. Mixing annotated image data from the Moon and Mars while keeping the total number of labels constant, we show that including a small number (10%) of rockfall labels from a foreign domain (e.g. Moon) during detector training can increase performance in the home domain (e.g. Mars) by up to 6% Average Precision (AP) in comparison to a purely home domain-trained detector. We additionally show that using a large number of foreign domain training examples (90%) in combination with a small number (10%) of home domain labels can be as powerful or more powerful as exclusively (100%) using home labels in the home domain. We further observe that rockfall detectors trained on multiple domains outperform single–domain trained detectors in completely unknown domains by up to 16% AP, using image data from Ceres and comet 67P. We conduct an experiment varying only image resolution on a single planetary body (Mars) to test whether the improvement was due to training on differing resolutions specifically and show that none of the improvement can be explained by this effect alone. This means that the benefits of multi–domain training mostly draw from either variations in lighting condition, differing physical appearance/backgrounds around the target of interest for generalization purposes, or both. Our findings have important applications such as machine learning–enabled science discovery in legacy and new planetary datasets. The used dataset of martian and lunar rockfalls including a detailed description is available here: https://edmond.mpdl.mpg.de/imeji/collection/DowTY91csU3jv9S2. [ABSTRACT FROM AUTHOR]

Remote sensing methods which produce point clouds, such as terrestrial laser scanning (TLS), terrestrial photogrammetry (TP), and small unmanned aerial systems (sUAS) have become an integral component of geotechnical monitoring programs. Applications such as rock-slope monitoring benefit from multi-platform datasets to be acquired in two or more different epochs. Accurate registration of these datasets in a common coordinate system is essential for detecting slope changes. Their registration often relies on initial feature-based alignment followed by fine alignment with the iterative closest point (ICP) algorithm. When practical, ground control points (GCPs) and other surveying targets with well-defined coordinates are used. However, establishing such GCPs on rock surfaces can be difficult, expensive and dangerous. In addition, GCPs and targets can be lost or destroyed with time and re-establishing them is difficult. This paper develops an automated registration algorithm based on edge detection that can register multi-platform and multi-epoch point clouds. Rock-surface edges are expected to remain largely the same and be captured in point clouds collected in two different epochs. For edge detection, we use α -molecules that offer a unified framework of most multi-scale transforms that can be adapted to any rock-surface. Then the algorithm identifies edge correspondences based on the discrete Fréchet distance. From the corresponding edges we derive matching points between datasets. Transformation parameters are then derived through Procrustes analysis. Using real and simulated scenarios, we demonstrate the utility and performance of the proposed algorithm. For example, sUAS scenarios with 0 and 1 GCPs show that initial root mean square error (RMSE) values of a few decimeters drop to a few centimeters. Scenarios with simulated translations, rotations, and scale showed that the developed algorithm registers multi-platform point clouds with mm differences from their original RMSE values. Results demonstrate that the algorithm can successfully register multi-platform point clouds and support rockfall monitoring. [ABSTRACT FROM AUTHOR]

• 3D change between point clouds is commonly measured normal to the local surface. • Our approach finds a dominant movement direction (DMD) occurring at each point. • It does this by considering local changes in multiple directions (n = 60). • We show that the DMD can differ considerably from the surface normal. • This can provide an important alternative view of movement: • where overall displacement of a surface or feature is needed, • where movement processes operate in a direction that is not surface-normal, • where the underlying process(es) may not be known, • where movement(s) across the point cloud scene is not oriented along a single axis. Point clouds continue to be acquired with greater accuracy and less occlusion over complex scenes, characterised by high roughness and topographic variation in all three dimensions. The most widely adopted approach to change detection, M3C2, measures change along the local surface normal, which varies between points and bypasses the uncertainties involved in mesh or DEM generation. While adaptive, this direction of comparison is nevertheless user-defined and becomes less relevant where the movement direction deviates from the surface normal. Measured change therefore also becomes less meaningful, as it is a projection onto this direction. Sliding of a failing slope, for example, is predominantly surface parallel rather than along the surface normal. We present an approach that derives a dominant movement direction (DMD) at each point based on multi-scale, multi-directional change quantifications. The DMDs differ from the surface normals in three LiDAR-derived test cases; a rockfall, an avalanche, and rock glacier movement, providing more accurate measures of rockfall depth and boulder movement across the rock glacier. When the direction of change detection is orthogonal to local relief (i.e. across the surface), a variable length search cylinder that intersects only a single (corresponding) surface is necessary during change detection. Where movement results in new regions of occlusion in the second point cloud, we show that the proportion of points for which no valid change could be recorded decreases by up to 15% using the DMD rather than the surface normal. We emphasise the importance of examining the direction over which change is measured, and highlight that a comparison direction that adapts to movement rather than to the local surface can provide more relevant and accurate measures of change where the movement is not orthogonal to the surface. Our approach represents a supplementary tool for cloud-to-cloud comparison, where a choice of tool should be made based on the expected DMD deviation from the surface normal. [ABSTRACT FROM AUTHOR]

In the case of the rockfall hazards, multiple rockfall impacts usually occur. To improve the ductility and the multi-impact resistance ability of the rock-shed, geofoam is proposed to replace part of sand, forming a composite cushion. Two types of geofoam are selected in the paper, namely, expandable polyethylene (EPE) and expanded polystyrene (EPS). A systematic experimental study was conducted to compare the properties of EPE and EPS. The compression tests showed that EPE geofoam had better resilience than EPS geofoam. Under the laboratory multi-impact tests, the buffer performance of the sand-EPE composite cushion performed well under the multi-impacts. However, for the sand-EPS composite cushion, the buffer performance became poor with increased impact number. For the thicker geofoam, the buffer performance of the composite cushion changed slightly from the second impact to the fifth impact, especially for the EPE geofoam. Finally, large-scale rockfall experiments were carried out to further study the buffer performance of different cushions. Compared with EPS geofoam, EPE geofoam was more suitable for improving the capability of the composite cushion to resist multi-impacts, as well as to protect the rock-shed under multiple rockfall impacts. • New composite cushion, sand-EPE gefoam, was proposed to resist multi impact loading. • Laboratory mutil impact testing was carried out to study impact resistance of composite cushion. • Large-scale multi-impact test investigating application of composite cushion to prevent the impact of rockfall. [ABSTRACT FROM AUTHOR]

The devastating Kahramanmaraş earthquake sequence occurred on February 6, 2023. Two main events, M w 7.8 and M w 7.5 occurred 9 hours apart, affected 11 cities in Turkey, and subjected an area of ∼90,000 km2 to shaking levels known to trigger landslides (peak ground acceleration > 0.08 g). Extensive landsliding was expected given the hilly terrain affected by this significant ground shaking—about 15% of the topography is steeper than 20°—but was not initially apparent in early satellite imagery, mostly because of obscuring snow that fell just after the earthquakes. However, after a more detailed investigation using high-resolution satellite images, aerial photos, and a field survey, we confirmed that this earthquake sequence did, indeed, trigger numerous landslides. In this study, we present those findings and provide a preliminary characterization of the spatial distribution, general characteristics, and dominant types of landslides and hillslope deformation triggered by the earthquake sequence. We mapped 3673 coseismic landslides, mostly concentrated in the northern half of the impacted area. Rock falls are the most abundant landslide type, but bedrock rotational landslides, translational slides and lateral spreads are also numerous. Surface rupture through mountainous terrain caused several large, and in some cases fatal, landslides. Incipient landslides and ground cracks are also widespread, especially in the north. Lithology, spatial variability of ground shaking, and topographic relief appear to be the main variables controlling the spatial distribution of coseismic landslides. There are few detailed studies of earthquake-triggered landslides in arid and semi-arid regions such as this one, nor for such complex earthquake sequences. Therefore, this contribution provides valuable information for future hazard and modeling efforts in arid and semi-arid regions. • The Türkiye-Syria earthquake sequence triggered 3673 landslides. • The ground shaking, topography and lithology control the landslide distribution. • Surface rupture through mountainous terrain caused several large and fatal landslides. [ABSTRACT FROM AUTHOR]

This article presents the development and calibration of a numerical model simulating the response of a novel rockfall protection structure subjected to localized dynamic loading. This structure is made of piled-up concrete blocks interconnected via metallic components whose dynamics response under projectile impact is examined via real-scale experiments. The corresponding numerical model is developed in a python based open source software Siconos which implements the Non-Smooth Contact Dynamics (NSCD) method. The geometrical features and mechanical properties are incorporated in the model via specific developments pertinent to the modelling requirements in Siconos. Some parameters peculiar to the numerical model are calibrated against the spatial–temporal measurements from two full-scale impact experiments. The Bayesian interface statistical learning method aided by the polynomial chaos expansion based meta-model of the NSCD model is deployed for the calibration. The additional understanding of the model dynamics through the byproducts of the meta-model is highlighted. In the end, the NSCD model is successfully calibrated against the spatial–temporal response of the experimental structure with more than 90% accuracy for impact energies up to 1 MJ. • Non-smooth contact dynamics (NSCD) based numerical model of a novel rockfall protection structure. • Model development incorporating the design intricacies in the counterpart full-scale experimental structure. • Identification of model parameters for spatio-temporal calibration against experimental database. • Model calibration via Bayesian interface statistical learning method aided by the surrogate models. [ABSTRACT FROM AUTHOR]

Blast load is a crucial parameter in blasting construction design and numerical simulation. However, it is characterized by dense energy, high loading rate and complex action process, which makes its accurate measurement difficult. In this study, based on experimental data-driven physical simulation, a data assimilation method for blast load prediction was proposed by integrating PFC2D software and Python language. The method can accurately measure the blast load, as it achieves an exact match between the numerical calculation results and the test far-field strain waves by automatically adjusting the peak blast pressure, rise/fall time and rock damping ratio. Furthermore, the effectiveness of the method was verified by performing rock blasting disturbance tests, and the superiority of the data assimilation algorithm was illustrated from two aspects, i.e., time-loading path and rock damping ratio. The following research results were obtained: 1) When the blast pressure is constant, the number of micro cracks within the rock decreases first and then increases with the rise of loading rate, while it always increases with the rise of unloading rate. Therefore, it is necessary to measure the time-loading path of blast pressure. 2) When the rock damping ratio deviates more notably from the true value, the error rate of the peak blast pressure by the assimilation method is greater correspondingly, indicating that the rock damping ratio is a prerequisite for accuracy of the assimilation method. 3) Superior to the traditional isentropic expansion theory, the assimilation algorithm not only accurately measures the peak blast pressure but also considers both the time-loading path and the rock damping ratio. The research results enrich rock dynamic tests and provide valuable references about blasting engineering. [ABSTRACT FROM AUTHOR]

• The residual performance of the flexible barrier is related to the three-stage deflection mechanism of the barrier. • The residual performance of the flexible barrier can be evaluated by the damage identification method and the calculation method of the residual protection capacity. • The damage of the barrier can be identified by comparing the deflection of key components with the ultimate deflection of those components. • The residual protection capacity of the barrier can be estimated by the energy dissipated by dissipators. Currently, there is no effective analysis algorithm to calculate and evaluate the residual performance of flexible barriers after suffering rockfall impact. To solve this problem, a novel approach for assessing and quantifying the residual performance of flexible barriers through the deflection degree of different components is proposed, based on the substantial experimental database from impact tests that were conducted with impact energies between 750 kJ and 1500 kJ, revealing a high correlation between the three-stage macroscopic deflection characteristics and the residual performance. The approach consists of two significant aspects, one is the calculation method of the residual protection capacity, which was established according to the simplified calculation method of the energy dissipation of the dissipators. The other aspect is the damage identification method for the barrier system based on the deflection of key components, which was determined by comparing the deflection angle of the post and the punching deflection of the net obtained from the analytical geometric model with the ultimate deflection of those components calibrated by tests. Utilizing the ultimate force-deflection relationship calibrated by tests, a method to discriminate the system damage based on the analytical calculation value of the deflection of key components is established. Furthermore, both full-scale tests and numerical simulations were conducted to validate the proposed analytical calculation method. The tested flexible barrier had a nominal energy level of 2000 kJ, and the tests included service energy level (SEL) and maximum energy level (MEL) continuous impacts. The analysis results show that the maximum calculation errors for the energy dissipated by the energy dissipators, elongation of the energy dissipator, elongation of the wire ring, and out-of-plane deflection angle of the post are -3%, 10%, 12%, and -8%, respectively. The evaluation results of the residual performance as well as the damage identification both have satisfied agreement with the test results, confirming the reliability and applicability of the analytical method to engineering maintenance decisions. [ABSTRACT FROM AUTHOR]

Tuffaceous material of the Lower Triassic Baikouquan Fm., NE Junggar Basin is closely associated with detrital particles and particularly with crenulated quartz grains and kaolinite. The amount tuff is reduced closed to the source area. The main component of the tuffaceous interstitial material is SiO 2 , followed by K 2 O, FeO, and MgO. The tuffaceous interstitial material can be further divided into three types: medium (basic) potassium-rich tuffaceous material, ultrabasic iron-magnesium tuffaceous material, and ultrabasic iron-magnesium rich tuffaceous material. Medium (basic) potassium-rich is dominated by fusiform shrinkage pores, often associated with kaolinite, while ultrabasic iron-magnesium rich tuff pores are not developed, and ultrabasic iron-magnesium tuffaceous material is dominated by dissolution pores. The solution pores have large diameters and large volume of pores and throats, but low pores and throats coordination number. The pore size and the pores and throats volume of shrinkage fractures are small, but the pores and throats coordination number are large. Dissolution pores are mainly distributed in Mbr 1, whereas shrinkage pores mainly occur in Mbr 2. However, the interlayer productivity data from a single well show that the Mbr 1 has a higher productivity than Mbr 2. However, the interlayer productivity data from a single well show that Mbr 1 has a higher productivity than Mbr 2. In addition, tuff is not responsible for wind action, but the cause of intermittent water flow transport in volcanic rocks. Distributary channels at the fan delta front comprise the dominant facies for development of tuffaceous interstitial materials. The composition of tuffaceous interstitial material clearly determines reservoir quality. Medium basic potassium-rich tuff has a high SiO 2 content and is prone to devitrification, so it is dominated by shrinkage pores, whereas ultrabasic iron-magnesium tuff contains FeO and MgO and is vulnerable to acid corrosion and the formation of corrosion pores. However, acid minerals such as kaolinite reduce reservoir connectivity. Compared with solution pores, shrinkage pores have a small pore volume, but high pores and throats coordination number, indicating that they have a high seepage capacity. Shrinkage pores are an important factor in causing production differences between single layers and should be given more attentions. • Tuff is the cause of intermittent water flow transport in volcanic rocks and not falling mechanism. • Composition of tuffaceous material determines obvious selectivity of reservoir development. • Medium basic potassium rich tuff is dominated by shrinkage pores. • Shrinkage pores not only impact reservoir performance, but also have high seepage capacity. [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing (DAS) using fiber-optic cables shows promise for rockfall monitoring by potentially offering high spatial resolution and wide coverage at lower costs. However, further research is needed to fully evaluate DAS performance for rockfall applications. This study assesses DAS capabilities for rockfall monitoring by analyzing DAS and seismometer recordings of 38 artificially triggered rockfalls with block masses ranging from 139.6 to 638 kg. Results demonstrate strong correlations between DAS waveforms and spectra with colocated seismometers, validating DAS data for detecting rockfall impacts. Fiber configuration impacted DAS sensitivity: fully in-contact cables improved signal-to-noise ratios and detection of high frequencies from 55 to 86 Hz. Analyses of seismic waves identified three distinct rock block propagation modes associated with intact, fragmenting, and collapse-triggering movements. While DAS signal feature values agreed with seismometers, no clear mass correlation emerged within the examined range. However, spectral features like frequency centroid (48.8 ± 6.5 Hz) and predominant frequency (41.0 ± 12.1 Hz) exhibited spatial stability across channels, indicating potential for event identification. Preliminary attempts to locate rockfall sources from DAS arrays proved feasible but require optimization. Overall, enhanced fiber coupling, identification of propagation modes, spatially-dependent analyses, and dense-array source location may enable DAS to complement sparse seismic networks for higher resolution, cost-effective rockfall monitoring. These insights could inform the development of improved rockfall monitoring strategies leveraging distributed fiber-optic sensing. • DAS signals match colocated seismometers, validating impact detection. • Fully coupled fiber improves DAS sensitivity/high-frequency detection up to 86 Hz. • Three distinct rock block propagation modes identified, linked to movements. • DAS spectral features spatially stable, indicating event identification potential. • Preliminary dense-array source location using DAS demonstrates feasibility. [ABSTRACT FROM AUTHOR]

The analysis of three-dimensional point cloud data is becoming one of the most used approaches to assess instabilities processes affecting rock slopes. With the increased collection of point cloud data, there is an increasing demand for rapid computational point cloud segmentation techniques to format data for rock fall risk analysis. However, there is a scarcity of semantic segmentation research focusing on geological data and the practical issues they pose. In this study, we propose a novel application to automatically define the spatial extent and intensity of weathered rock slope areas. The studied rock slopes are in Hegra Archaeological Site (al-Hijr / Madā ͐ in Ṣāliḥ), the first UNESCO World Heritage area inscribed in the Kingdom of Saudi Arabia (KSA) in 2008. Here, cavernous weathering processes, with honeycombs and tafoni features, affect the carved rock slope and increase the rockfall hazard. With the spread and improvement of close-range methods, such as Terrestrial Laser Scanner (TLS) and Unmanned Aerial Vehicle Digital Photogrammetry (UAV-DP), it is possible to obtain fast and accurate high-resolution point cloud data of the studied scenario, i.e., rock mass. This paper presents the automated mapping results obtained by a combination of explainable artificial intelligence (XAI) algorithms and three-dimensional geometric analysis of a point cloud using the geometric feature tools available for free on CloudCompare (CC) open-source software. Three-dimensional data were obtained using the UAV-DP technique. The proposed workflow allows, using random forest modelling (RF), to quantify the cavernous weathering extent and estimate the relative intensity on the rock slope, faster than using traditional time-consuming manual segmentation processes. The RF model was able to accurately forecast the cavernous weathering regions (accuracy of up to 85%), with a slight propensity to underpredict, as indicated by the somewhat low recall (∼ 63%), and a limited number of false positives, as indicated by the high precision (up to 84%). This approach is easily reproducible at low cost in terms of computational, human, and financial resources. The results highlight the reliability of the proposed method for the preliminary assessment of cavernous erosional features with the aim of future assessment, monitoring, and deeper analysis of rock-carved slopes. [Display omitted] • UAV-DP surveying provides 3D point cloud of weathered rock slope. • Point cloud 3D geometric features allow to identify weathered areas. • AI-aided cavernous weathering assessment using geometric features. • Automated cavernous weathering detection on UNESCO rock-carved cultural heritage. • Rockfall source areas detection linked to weathering feature. [ABSTRACT FROM AUTHOR]

Rock surface temperatures are of fundamental importance for studies on weathering, rockfall and permafrost. Point temperature measurements may not reflect the small-scale variability of temperature as a function of micro-topography. To close this gap, infrared thermography (IRT) seems to be a simple and promising approach. However, there are several pitfalls in terms of interpretation, as radiation temperatures depend on emissivity and reflectivity of the rock, which in turn are influenced by rock type, surface roughness, wetness, surrounding weather conditions, and angle to the camera axis. We performed laboratory and exemplary field experiments in order to estimate the magnitude of possible errors. We used rough and smooth (sawn) specimen of six different stone types in wet and dry condition and took IRT images at different tilt angles between camera axis and rock surface. Furthermore, we applied the approach to a small rock outcrop (approx. 3 × 3 m) and to a rockwall (approx. 100 × 100 m). The results of the laboratory measurements show that the temperature error increases with increasing tilt angle of the rock surface. Depending on the nature of the reflected surroundings, radiation temperatures can be warmer or cooler than sensor temperatures. In typical settings, the error is low (<0.5 K) up to a tilt of 40° but it may increase to >1 K at tilt angles of 50° and more. Smooth and wet surfaces tend to be more prone to deviations. The field examples confirm the results of the laboratory tests. But they show that spatial differences in temperature can still be detected as the "true" differences are usually larger than the magnitude of error. We suggest to reduce the error of the IRT image by correcting temperatures using a high-resolution surface model. • Reflection of surrounding surfaces, especially the sky, is a significant source of error in IRT investigations of rock • We performed laboratory and exemplary field experiments in order to estimate the magnitude of these errors • In typical settings, errors are low (< 0.5 K) up to a tilt of 40° but may increase to several K at tilt angles of ≥ 50° • We provide a table for estimating the magnitude of error [ABSTRACT FROM AUTHOR]

Rockfall events represent a serious hazard for people, structures, and infrastructures. The phenomenon can occur in several environments, natural and artificial. In surface mining operations, blasting, bench clean-up and scaling can induce rock blocks detachment, compromising operators' and operations' safety. The installation of passive mitigation measures coupled with a specific design of the pit geometry can mitigate this risk. The bench height, width, and slope angle, the number of benches, together with the material characterizing the slope, affect in different ways the kinematics of the possible detached blocks. In this paper, a large set of configurations is studied and trajectory analyses are performed to evaluate the influence of each geometrical or material variable. Results are analysed considering blocks kinematics both on the pit floor and at each bench. The findings provide a useful a tool for a preliminary design of pit geometry and mitigation measures. Design charts are proposed for different slope materials, reporting, as function of the total pit height, the blocks stopping distance, their velocity at the pit floor, their passing height and velocity at each bench, for each geometrical configuration. The obtained charts can be effectively implemented in a quantitative risk assessment procedure for mining activities. [ABSTRACT FROM AUTHOR]

Flexible rockfall barriers are protection systems against risks of falling rocks. Their complex behaviour is still not well understood and the development of relevant models allowing for quick calculations is a need toward the optimisation of such structures. The key issue is the modelling of the so-called "curtain effect", where the cable net slides along the supporting cables. The present paper proposes hence a model of sliding cable submitted to concentrated forces. It addresses the stability conditions of the centred finite difference scheme, especially in the framework of the Dynamic Relaxation Method where estimates are found for the best possible fictitious masses at nodes. Expressions are derived in the general case, but also for monotonically curved cables and looped cable elements or rings. Elementary problems are shown for validation, before a case study based on experiments conducted during the French National project C2ROP demonstrates the accuracy and the reliability of the proposed methodology. The present study is however limited to quasi-static loadings and therefore needs further developments to be extended to realistic dynamic cases which are discussed in the conclusion. [ABSTRACT FROM AUTHOR]

As direct observations of rockfalls are rare and difficult to obtain over long timescales, stem injuries and their dating with dendrogeomorphic techniques have been applied frequently in the past to reconstruct process activity. However, the analysis of tree-ring samples requires considerable temporal efforts, which can be detrimental in applied research and expert opinions. To compensate for this shortcoming, several studies have lately explored the potential of the counting of visible scars as an alternative method to dendrogeomorphology as it requires much less time and human efforts to reconstruct spatial patterns of rockfall activity. Yet, to date, both approaches have not been compared at the same site. In this paper, based on the extensive analysis of 278 conifer and broadleaved trees (from which 1097 tree-ring cores were extracted) from a mixed forest plot of the Vercors Massif (French Alps), we demonstrate that both methods provide similar spatial patterns of rockfall activity with a strong increase of recurrence intervals down the talus slope and a clear lateral zonation of activity. Despite this apparent convergence between both approaches, our study also evidences that rockfall frequencies strongly differ with tree species and diameter: the visual inspection of conifer stems and the analysis of tree-ring signals in large-diameter broadleaved trees typically lead to an overestimation of recurrence intervals. Based on these findings, we recommend giving priority to the scar-counting approach on small diameter (<15 cm) broadleaved stems and to restrict tree-ring analysis to old conifers that are more susceptible – through the identification of tangential rows of traumatic resin ducts – to provide reliable estimations of long recurrence intervals (as generally observed in the lower portions of the slope). These guidelines should help to improve the reliability of recurrence interval maps of rockfall process activity in forests further, but also to improve cost-benefit ratios of future studies. • Comparison of the counting scar and dendrogeomorphic approaches to reconstruct rockfall in a mixed forest stand • Both approaches provide comparable spatial patterns with an increase of recurrence intervals down the talus slope • Rockfall frequency differs quite substantially with tree species and diameter. • Tree-ring signals in large-diameter broadleaved trees typically lead to an overestimation of recurrence intervals • Our study provides key guidelines to improve the reliability of rockfall hazard zoning [ABSTRACT FROM AUTHOR]

A multi-method monitoring was conducted to detect the timing and trigger of rockfall activity on an alpine rockslide cliff composed of Cretaceous sandstone and shale in the southern Japanese Alps (Aresawa rockslide, 2900 m above sea level). The monitoring programme includes manual measurements of flaking from painted rock surface and collection of fallen debris, daily time-lapse imaging of the rockwall, automated recordings of rock temperature, rock moisture and influencing meteorological factors (air temperature and precipitation). A stereographic pair of sequential photographs is used to visually identify the location of new erosion and semi-quantitative evaluation of detached materials at daily resolution. Six years (2010–2016) of debris trapping show major rockfall activity in winter (between November and May) and occasional activity associated with heavy rains in summer, and yield an average rate of rockwall retreat on the order of 1 mm a−1. The rockwall shows heterogeneous debris production mainly reflecting joint spacing. Time-lapse imaging displays at least 10–15 rockfall events per year within the shot area (~500 m2). The integration of multiple data suggests at least five types of triggering processes towards rockfalls, which recur annually. (1) In summer and early autumn, heavy rainfalls raise the moisture content of the shallow bedrock, often triggering significant rockfalls, probably due to raised water pressure in rock joints or lubrication of joints. (2) In late autumn and late spring, light or intermediate rainfalls are sometimes followed by high moisture, shallow freezing, rapid thawing and eventually by different scales of rockfalls. (3) In winter and early spring, the same process as in the second case, but rainfall is replaced by snowfall, triggers submeter-scale rockfalls. (4) Deep seasonal freezing to about 5 m enables release of meter-scale rockfalls during seasonal thawing periods from late spring to early summer. (5) Short-term large thermal fluctuations that produce cyclic thermal stress may also contribute to submeter-scale rockfalls, mainly in winter locally enhanced by freeze-thaw action. Unlabelled Image • Multi-method monitoring of alpine rockfall activity for six years • Combination of time-lapse imaging, sensory data logging and manual debris collection • Seasonal change in the rockfall triggers • Heavy rain, rain/snowfall followed by freeze-thaw and seasonal thaw are major drivers. [ABSTRACT FROM AUTHOR]

• A new method for detecting the spatial attitude of pipeline welding based on point cloud registration. • We propose a coarse registration algorithm based on the axial line of pipeline point clouds to help facilitate the alignment process. • Providing guidance on welding posture prior to welding to minimize rework situations. This paper introduces a new method for detecting the spatial attitude of pipeline welding based on point cloud registration. The method aligns the measured point cloud of the pipeline with the CAD model point cloud using point cloud registration and guides the pipeline adjustment accordingly. This automated measurement method has resolved the inefficiencies and potential for rework that can arise from manual instrument operation during pipeline pose detection, and has subsequently improved detection efficiency and accuracy. In addition, we propose a coarse registration algorithm based on the axial line of pipeline point clouds to help facilitate the alignment process. We conducted simulation experiments using 3D CAD models, which demonstrated that our coarse registration algorithm outperformed the other three compared methods in terms of accuracy. The simulation experiments also proved that the absolute value of the overall method's relative error was less than 0.1%. [ABSTRACT FROM AUTHOR]

Defining the relationship between volume and return period is critical when estimating the risk of rockfalls and/or rock avalanche, especially during continued global warming at high altitudes that threatens rock wall stability. Characterizing the volume-frequency relationship based on historical datasets is, however, limited by observation and quantification biases, which have not received enough attention. Here, to monitor recent activities for the Brenva Spur (Mont-Blanc massif, Italy) that is also a rock avalanche scar and estimate the return period of future rock failures based on the volume-frequency relationship (and the corresponding uncertainty), a structure-from-motion photogrammetric survey was conducted from 2017 to 2021. 39 rockfall sources with volumes ranging from 11 to 13,250 m3 were identified within the scar. The total failure volume is 22,438 m3, with an associated erosion rate of 15.5 mm/year, indicating very active morphodynamics possibly linked to the permafrost evolution in the spur. The volumes were characterized by a negative power-law that fits significant two events in 2016 (3.4 × 104 m3) and one in 1997 (2.0 × 106 m3) remarkably well, and the randomness of the fit was evaluated by a Monte Carlo approach. 7 potential failure scenarios ranging from 3.1 × 104 m3 (S 1) to 4.8 × 106 m3 (S 7) were defined according to a structural analysis and the sloping local base level concept. Their extrapolated return periods derived by the power-law fit indicate a longer return period for the maximum failure scenario than for the smaller scenarios. S 1 has a 50% chance of occurring every 3 years, while S 7 has a 50% chance of occurring every 31 years. Though the median return period of S 7 is 31 years, the 95% and 68.2% confidence intervals range from 8 to 399 years and 14 to 93 years, respectively, which reflects a high level of uncertainty but is realistic when considering global warming, progressive rock failure, etc. In addition to characterizing recent rock failure activities in high mountains, this study offers a preliminary examination of the return periods of some extreme scenarios and provides primary data for risk management in mountainous areas that are very sensitive to global warming. • Updated rock failure database by helicopter based SfM photogrammetry survey for Brenva Spur. • Detailed rockfall sources analysis linking geo-structures on the Brenva avalanche scar. • Extreme failure scenarios definition by discontinuity characterization and the SLBL concept. • Failure scenario return period estimation by volume-frequency relationship considering the uncertainties. [ABSTRACT FROM AUTHOR]