Search Results (95)

Typhoon-induced landslides in coastal mountainous regions are controlled by the coupled effects of rainfall, wind, topography, and storm-track geometry. However, conventional static susceptibility models have limited ability to represent event-scale forcing under extreme weather conditions. This study develops a physics-guided dynamic landslide susceptibility framework and retrospectively applies it to the 2017 Tropical Cyclone Cempaka event in Pacitan Regency, Indonesia, where 743 landslides were identified. The framework integrates static terrain factors, antecedent wetness, event-scale rainfall accumulation and intensity, maximum wind speed, and a typhoon geometric exposure index derived from IBTrACS best-track information that represents track proximity, topographic shielding, rainfall-favored quadrant effects, and storm-motion effects. Under spatial block cross-validation, model performance improved progressively from the static baseline to the full-factor model, with the receiver operating characteristic area under the curve (ROC-AUC) increasing from 0.648 to 0.751, the precision–recall area under the curve (PR-AUC) reaching 0.826, and the F1-score reaching 0.744. The full-factor model also reduced missed landslide cases from 328 to 205 and concentrated predicted high-susceptibility zones along the typhoon exposure corridor. Additional parameter-sensitivity analyses further indicate that the event-based E_geo setting produced positive performance increments under the event-consistent quadrant convention. These results indicate that physically meaningful typhoon-exposure information can improve the spatial discrimination and interpretability of event-scale landslide susceptibility assessment. Full article

Accurate delineation of landslide boundaries from remote sensing imagery remains challenging because landslides exhibit irregular geometry, substantial scale variation, and strong background interference. We propose DCA-UNet, a U-Net-style segmentation network that integrates deformable convolution and aggregated attention to jointly improve geometric adaptation and local-global context modeling. Deformable convolution adjusts spatial sampling locations to irregular landslide boundaries, whereas aggregated attention enhances contextual discrimination in visually ambiguous terrain. We evaluate the method on three public benchmarks—Landslide4Sense, HR-GLDD, and GDCLD—under a controlled from-scratch benchmark with dataset-specific preprocessing and official data splits. DCA-UNet achieves the best overall IoU/F1 ranking across the three datasets, reaching 61.92%/76.48% on Landslide4Sense, 59.24%/74.41% on HR-GLDD, and 58.40%/73.74% on GDCLD. The model contains 29.50 million parameters, which is close to vanilla U-Net and substantially fewer than several transformer-based baselines, although its training-side runtime and memory consumption are not the lowest. These results show that combining adaptive spatial sampling with local-global contextual aggregation is effective for landslide segmentation in both multispectral and RGB remote sensing imagery. Full article

Rapid and accurate detection of landslide-affected areas is critical for disaster response and risk mitigation. Sentinel-1 SAR imagery offers all-weather, day-and-night observation capability, but existing deep learning approaches treat landslide detection as a single-pass segmentation problem, which limits performance in complex terrain where backscatter changes are confounded by soil moisture, surface roughness, urban double bounce, shadow, and layover effects. MAD-SAR, a rule-based agentic framework that coordinates anomaly detection, super-resolution, object detection, and semantic segmentation under a planning orchestrator and a physics-aware validation engine is proposed. The orchestrator selects specialist modules, their execution order, and the number of refinement iterations according to a scene complexity score computed from SAR-derived statistics. The physics-aware validation engine cross-checks every candidate detection against backscatter change thresholds, DEM-derived slope constraints, and radar geometry masks before any detection is committed to the output. MAD-SAR is evaluated on three Japanese disaster datasets: Hiroshima 2018, Kumamoto 2016, and Ibaraki 2019. On the held-out Ibaraki test event, the framework achieves an F1-score of 0.863 and IoU of 0.759, outperforming all baselines and reducing false alarms by 45% relative to standalone SegFormer. Ablation results confirm that each module contributes to the final performance. These results suggest that multi-module orchestration with embedded physical validation can meaningfully improve SAR-based landslide mapping, though broader validation across regions, sensor configurations, and failure mechanisms remains necessary. Full article

Accurate delineation of buried slip surfaces remains a major uncertainty in landslide hazard assessment, especially where subsurface data are limited. This study evaluates a displacement-based approach to estimate quasi-three-dimensional (quasi-3D) slip surfaces using ground-surface displacement vector gradients derived from multi-temporal UAV-based LiDAR data. Two landslides in Japan (Jimba and Kamitokitozawa), representing contrasting scales, were analyzed to assess the method’s applicability and limitations. Two-dimensional (2D) slip-surface profiles were derived through group-wise median grouping of displacement gradients and weighted non-uniform rational B-spline fitting along longitudinal sections. Transverse profiles were constrained using side-scarp gradients and depths estimated from longitudinal profiles. These profiles were integrated into quasi-3D surfaces and validated against borehole-derived slip surfaces. At the Jimba landslide, characterized by relatively coherent movement, the estimated surfaces closely match borehole data in both depth and geometry. At the larger Kamitokitozawa landslide, the method reproduces first-order geometry and extent but shows larger local deviations, particularly in a graben-like subsidence zone. Nevertheless, the estimated displaced volume reaches 96% of that derived from borehole data. These results demonstrate that the method provides useful first-order constraints on slip-surface geometry for preliminary hazard assessment, borehole planning, and 3D stability analysis. Full article

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article

Cascading failures of clustered landslide dams can intensify downstream hazards not only by increasing peak flood magnitude but also by accelerating the rise of water level immediately upstream of successive dams, thereby shortening the available response time before overtopping. This study reports large-scale flume experiments on a three-dam cascade built with identical geometry and similar soil gradation, while systematically varying longitudinal spacing and inflow discharge. The principal measured variable, $C_w\left(t\right)$, is defined here as the local forebay run-up/water-level record measured at a fixed gauge position immediately upstream of each dam. The run-up hydrographs were summarized using peak run-up $C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, threshold-arrival time $t_a$ defined at $0.1 C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, time to peak $t_p$, maximum rising-stage rate $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$, and above-threshold duration $T$. Across ten tests (five spacing configurations under low/high discharge), peak run-up at both downstream dams consistently exceeded that at Dam1, with amplification factors relative to Dam1 of 1.11–1.45 at Dam2 and 1.13–1.42 at Dam3; Dam3 was not always higher than Dam2. Amplification was much stronger in the rising-stage dynamics: $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$ increased relative to Dam1 by factors of 1.56–11.0 at Dam2 and 2.27–14.0 at Dam3, demonstrating pronounced downstream wavefront steepening. Higher discharge produced earlier threshold arrivals and peaks throughout the cascade, whereas shorter spacing generally produced more impulsive downstream responses with sharper peaks and larger rate amplification. Overall, the dataset provides stage/run-up-based constraints on cascade amplification and indicates that, within the present experimental matrix, dam spacing is the dominant geometric control on flood propagation and downstream hazard escalation. Full article

Investigating slope deformation in densely vegetated or remote areas is a major challenge for slope stability assessment. This study introduces and validates an integrated UAV-borne low-frequency Ground Penetrating Radar (UAV-GPR) and LiDAR methodology to characterize an unstable slope in Melizzano, Southern Italy. Radar data were acquired along an east–west transect at ~1 m above ground level, while high-resolution LiDAR were used to generate a detailed Digital Terrain Model for topographic correction and geomorphological analysis. The processed radargram images subsurface features down to ~15 m, revealing a laterally continuous high-amplitude reflector at ~10 m, interpreted as a key main sliding surface. Chaotic reflections above this interface indicate heterogeneous deposits associated with gravitational deformation, while more homogeneous reflections below correspond to stable geological units. The geometry of the reflector suggests a compound landslide mechanism. Borehole data validate the geophysical interpretation, showing depth discrepancies lower than 2 m. The integration of UAV-GPR and LiDAR enables a reliable correlation between surface morphology and subsurface structures. This non-invasive, spatially continuous approach provides an effective framework for subsurface characterization and for improving the interpretation of landslide geometry and internal structure in challenging environments. This study demonstrates the capability of low-frequency UAV-borne GPR to detect deep-seated sliding surfaces (>10 m) in vegetated environments when integrated with high-resolution LiDAR topography. Full article

The Tuzla Submarine Landslide represents one of the most significant mass-wasting features associated with the active North Anatolian Fault Zone (NAFZ). The failure surface geometry and sediment stratigraphy indicate the presence of a mechanically weak, saturated layer that may become unstable under strong seismic loading. This study presents a comprehensive geotechnical evaluation of the Tuzla Submarine Landslide. Based on regional sediment properties, the landslide was characterized and modeled with an estimated volume of 0.015 km³ and an average slope angle of 14°. The submarine landslide potential was investigated through re-analysis of seismic, geotechnical, and bathymetric datasets. Finite Element Method (FEM) simulations were conducted to model the seismic slope failure. Based on these analyses, the seismic slope displacements, stress distributions, and equivalent plastic strains were identified. The estimated landslide displacements under varying seismic acceleration scenarios corresponding to three major earthquakes ranged between 2.38 m and 4.12 m, depending on the triggering ground motion and slope stability conditions. These findings highlight that reactivation of the Tuzla submarine landslide, potentially triggered by a future large earthquake along the NAFZ, could pose a moderate landslide hazard to the coastal settlements bordering the Marmara Sea. Full article

As one of the most catastrophic geological hazards globally, landslides exhibit heightened risks due to their increasing frequency, destructive potential, and extensive spatial distribution. The primary objective of this study is to develop an integrated analytical framework to quantitatively evaluate the stability of variably shaped slopes under rainfall infiltration. The core hypothesis is that slope curvature significantly alters infiltration behavior and stress distribution, leading to morphology-dependent failure mechanisms. Employing Green-Ampt infiltration theory coupled with limit equilibrium analysis, we establish stability prediction models for three fundamental slope geometries (linear, concave, convex) under contrasting rainfall regimes (high-intensity vs. low-intensity precipitation). The derived analytical solutions reveal two critical phenomena: (1) progressive downward migration of the saturation front maintaining parallelism with slope surfaces during infiltration and (2) time-dependent stability deterioration following hyperbolic decay patterns. The proposed models are rigorously validated through numerical simulations employing finite element methods, which demonstrate remarkable congruence with theoretical predictions, showing safety factor discrepancies below 5% (ΔFs < 0.05). Particularly, concave slopes exhibit 18–22% faster destabilization rates compared to convex counterparts under equivalent rainfall conditions. The validated models elucidate the spatiotemporal evolution of matric suction and pore pressure distributions, providing quantitative insights into morphology-dependent failure thresholds. These findings advance predictive capabilities for rainfall-induced landslides through physics-based stability criteria, offering critical guidance for terrain-specific early warning systems and mitigation strategies in geohazard-prone regions. Full article

The determination of landslide thrust is one of the premises of slope protection. The normative calculation methods of landslide thrust are often difficult to develop because of the structural complexity and paroxysmal instability of rock slopes. In this study, the thin-plate buckling model was adopted to simplify the upper bedding slope rock mass of the protective structure into a rock plate considering transverse shear deformation. The critical load of bedding rock slope instability was selected as the primary indicator for landslide thrust analysis. The double Fourier series was used to solve the mechanical properties of rock plates with simply supported edges under unidirectional and bidirectional pressures, and the critical load expressions of small-deflection buckling of rock plate mechanics were modeled under corresponding conditions and obtained. The relationship and change rules of the dimensionless load coefficient and rock plate geometry size with different cases of thickness is discussed in detail. Finally, the model test and field test were conducted, and the obtained data were used to verify the theoretical results and applied to the landslide thrust calculation and protection structure design of bedding rock slope, providing a theoretical reference for guiding the design of anti-slide piles for slopes and ensuring the stability of slopes. Full article

The Taranto Landslide Complex (TLC) is a multi-episode submarine mass-failure system developed along the Apulian continental margin (Gulf of Taranto, northern Ionian Sea) between ~200 and ~900 m water depth. High-resolution multibeam bathymetry and chirp seismostratigraphy were integrated to map five partially overlapping Quaternary mass transport deposits (MTD1–MTD5) and quantify their geometry, conservative volumes, and first-order kinematics. Consistent morphometric parameters indicate mobilities (H/L) and angles of reach typical of continental-slope failures, whereas conservative volumes range between ~0.02–0.35 km³. A depth-averaged sliding-block approach yields bounds on peak velocity and travel time compatible with rapid emplacement. Cross-cutting relationships and post-failure sediment drapes constrain two principal phases of slope instability, expressed as time windows rather than fixed ages. This study develops a framework that integrates uniform morphometric, volumetric, and kinematic features with seismostratigraphy to reconstruct the evolution and relative mobility of multi-episode submarine landslide complexes. The proposed workflow provides a transferable framework for preliminary geohazard assessment on continental margins where repeated slope failure interacts with tectonic and sedimentary forcing. Full article

Using high-resolution remote sensing imagery to obtain the wind turbine height is a fast and effective method for monitoring the status of wind turbines after natural disasters such as earthquakes, landslides, and typhoons. A height estimation method tailored for wind turbines is proposed using high-resolution satellite images. First, deep learning techniques are employed to identify wind turbines and extract their shadow information from GaoFen-2 (GF-2) satellite imagery. Specifically, YOLOv5-CBAM and MSASDNet are used for target recognition and shadow extraction, achieving an identification accuracy of 96% and a shadow extraction accuracy of 82.53%. Next, the line-by-line scanning method is applied to remove blade shadow from the whole wind turbine shadow. By calculating the number of pixels occupied by the shadow length of the wind turbine after removing the blade shadow and multiplying by the image resolution, the wind turbine shadow length is obtained. Finally, a spatial geometry model involving the satellite angles, solar angles, and wind turbine shadow length is constructed to retrieve the wind turbine height. An experiment was conducted using GF-2 satellite remote sensing data from a wind farm in Huailai County of China. The actual heights of wind turbines in the estimation area were measured by the field experiment, and the average absolute error was verified to be 2.2 m, demonstrating the effectiveness of the proposed method. The experimental results show that this method can detect the post-disaster status of wind turbines. Full article

Landslide dams, typically composed of newly deposited, loose, and heterogeneous materials, are highly susceptible to failure induced by overtopping and seepage, particularly under extreme hydrological conditions. Accurate prediction of such breaching processes is essential for flood risk management and emergency response, yet existing models generally consider only a single failure mechanism. This study develops a mathematical model to simulate landslide dam breaching under the coupled action of overtopping and seepage erosion. The model integrates surface erosion and internal erosion processes within a unified framework and employs a stable time-stepping numerical scheme. Application to three real-world landslide dam cases demonstrates that the model successfully reproduces key breaching characteristics across overtopping-only, seepage-only, and coupled erosion scenarios. The simulated breach hydrographs, reservoir water levels, and breach geometries show good agreement with field observations, with peak outflow and breach timing predicted with errors generally within approximately 5%. Sensitivity analysis further indicates that the model is robust to geometric uncertainties, as variations in breach outcomes remain smaller than the imposed parameter perturbations. These results confirm that explicitly accounting for the coupled interaction between overtopping and seepage significantly improves the representation of complex breaching processes. The proposed model therefore provides a reliable computational tool for analyzing landslide dam failures and supports more accurate hazard assessment under multi-mechanism erosion conditions. Full article

Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. By applying this framework to ascending and descending Sentinel-1 data in the complex terrain of the Jishi Mountain region, we first introduce geometric distortion masking and a C-Index deformation consistency check, which enables the reliable identification of 530 active landslides, with 154 detected in both orbits. Second, we employ a local parallel flow model to invert the landslide movement geometry without relying on DEM-derived prior assumptions, successfully retrieving the two-dimensional (sliding and normal direction) deformation fields for all 154 consistent landslides. Finally, by synthesizing these 2D deformation patterns with geomorphological features, we achieve a systematic classification of movement types, categorizing them into retrogressive translational (31), progressive translational (66), rotational (19), composite (24), and earthflows (14). This integrated methodology provides a validated, transferable solution for deciphering landslide mechanisms and assessing risks in remote, complex mountainous areas. Full article

The analysis of rainfall-induced landslides, which involve complex interactions between hydrology, soil mechanics, and geometry, is still limited by simplifying assumptions in existing models. We introduced a numerical model that couples soil infiltration with three-dimensional (3D) slope stability analysis. After validating against benchmark problems, we used this model to investigate the effects of various hydro-geotechnical conditions on slope stability. The results show that rainfall intensity dictates the stability of shallow landslides, while for deep-seated landslides, it governs the rate of progression toward failure. A high initial groundwater table reduces slope stability by accelerating soil weakening, particularly for deep landslides. Although upward moisture redistribution via matric suction is possible, its effect is negligible during infiltration, allowing deep saturation and landslide risk to persist. Furthermore, a low-permeability basal layer impedes drainage, leading to pore pressure buildup and a rapid decline in stability. The proposed model could potentially overcome the limitations in predictive accuracy of current hydro-geotechnical models arising from their oversimplified representations. Full article