TerraMosaic Daily Digest: Jan 11, 2026
Daily Summary
Today's collection of 81 new papers on landslides highlights a diverse range of research, from fundamental soil mechanics to advanced remote sensing and AI applications. A significant portion of the research focuses on improving landslide prediction and monitoring through innovative techniques. These include novel applications of machine learning for P-wave detection in earthquake early warning, landslide susceptibility mapping, and deformation field construction. Several papers address the impact of climate change, particularly on permafrost degradation, glacial lake outburst floods, and wildfire-induced changes in rock properties. Flood-related research is also prominent, with studies focusing on urban flood susceptibility, flood severity assessment using citizen science data, and the impact of topographic uncertainty on flood modeling. Finally, several papers explore the use of remote sensing data, including SAR and LiDAR, for landslide detection, vulnerability assessment, and the creation of high-resolution digital terrain models. The overall trend indicates a move towards more sophisticated, data-driven approaches for understanding and mitigating landslide risks.
Key Trends
- AI/ML for Hazard Assessment: Machine learning is increasingly used for landslide susceptibility mapping, P-wave detection, flood prediction, and early warning systems.
- Remote Sensing Integration: SAR, LiDAR, and optical satellite imagery are being combined and enhanced with AI to improve landslide detection, deformation monitoring, and vulnerability assessment.
- Climate Change Impacts: Research is focusing on the effects of climate change on permafrost, glacial lakes, wildfires, and extreme precipitation events, and their implications for landslide hazards.
- Focus on Flows: A significant portion of the papers are related to flood hazards, including urban flood susceptibility, flood severity assessment, and the impact of topographic uncertainty on flood modeling.
Selected Papers
This digest features 81 selected papers from 2,337 papers analyzed across multiple journals. Each paper has been evaluated for its relevance to landslide research and includes links to the original publications.
1. Prediction of Fault Slip Tendency in CO₂ Storage using Data-space Inversion
Core Problem: Assessing fault slip potential in subsurface CO2 storage, which is crucial for safe operations but challenging with conventional methods.
Key Innovation: VAE-based data-space inversion framework to predict pressure, stress, strain, and fault slip tendency without generating posterior geomodels, reducing uncertainty in key parameters.
2. Accurate Interpolation of Ambient Noise Empirical Green's Functions by Denoising Diffusion Probabilistic Model and Implicit Neural Representation
Core Problem: Improving the resolution of EGF-based tomography, which is limited by the spatial density of seismic stations due to cost and logistical constraints.
Key Innovation: DIER, a self-supervised learning framework that integrates implicit neural representation with denoising diffusion probabilistic models to achieve high-fidelity EGF interpolation.
3. FeatureSLAM: Feature-enriched 3D gaussian splatting SLAM in real time
Core Problem: Creating real-time tracking SLAM system with photorealistic feature-enriched mapping.
Key Innovation: Integrating dense feature rasterization into novel-view synthesis, aligned with a visual foundation model, enabling new downstream tasks via free-viewpoint, open-set segmentation.
4. Variational Autoencoders for P-wave Detection on Strong Motion Earthquake Spectrograms
Core Problem: Accurate P-wave detection is critical for earthquake early warning, yet strong-motion records pose challenges due to high noise levels, limited labeled data, and complex waveform characteristics.
Key Innovation: Reframing P-wave arrival detection as a self-supervised anomaly detection task using Variational Autoencoders, showing that architectural constraints favoring global context over pixel-perfect reconstruction are essential for robust detection.
5. GeoSurDepth: Spatial Geometry-Consistent Self-Supervised Depth Estimation for Surround-View Cameras
Core Problem: Achieving accurate surround-view depth estimation for 3D scene understanding in autonomous driving, providing a competitive alternative to laser-based sensors.
Key Innovation: Leveraging geometry consistency as the primary cue, utilizing foundation models as a pseudo geometry prior, and introducing a novel view synthesis pipeline with dense depth reconstruction via spatial warping.
6. Dense 3D Displacement Estimation for Landslide Monitoring via Fusion of TLS Point Clouds and Embedded RGB Images
Core Problem: Accurate landslide monitoring is crucial, but existing point cloud methods are sparse or lack 3D displacement estimates.
Key Innovation: Hierarchical partitioning integrates 3D point clouds and RGB images for dense 3D displacement vector fields, improving spatial coverage and accuracy.
7. LightFormer: A lightweight and efficient decoder for remote sensing image segmentation
Core Problem: Real-time deployment of deep learning for remote sensing image segmentation on edge platforms is limited by decoder complexity.
Key Innovation: LightFormer, a lightweight decoder with a feature-fusion module and spatial information selection module, achieves high accuracy with reduced computational cost.
8. WaveDiffusion: Joint Latent Diffusion for Physically Consistent Seismic and Velocity Generation
Core Problem: Reconstructing high-resolution subsurface properties from surface measurements.
Key Innovation: Models seismic and velocity data jointly from a shared latent space via a diffusion process.
9. Flood severity assessment using K-means and K-medoids clustering based on flood height and duration in Balikpapan City, Indonesia
Core Problem: Citizen science flood data requires transformation and analysis for decision-making. Need to evaluate flood severity data by clustering flood parameters and severity labelling the clusters.
Key Innovation: Novel flood levelling or classification based on clustering-based severity levelling, impacting flood disaster management planning.
10. Hybrid SVM–XGBoost framework for wildfire susceptibility mapping in palm oasis ecosystems to enhance risk assessment in arid and semi-arid regions
Core Problem: Oasis fires are increasingly frequent due to climate change, threatening fragile ecosystems and agricultural livelihoods in semi-arid regions. Limited scientific assessments exist for wildfire susceptibility mapping in Moroccan oasis ecosystems.
Key Innovation: Innovative ML-based fire susceptibility mapping framework for palm oasis ecosystems using a hybrid SVM–XGBoost model, achieving high predictive accuracy.
11. Modeling urban flood susceptibility and identifying key flood-inducing factor chains using Bayesian network
Core Problem: Urban flooding presents serious threats to contemporary cities, driven by climate change and urbanization. There is a need for a model to assess urban flood susceptibility, quantify uncertainties, and capture interdependencies among influencing factors.
Key Innovation: Urban flood susceptibility assessment model using Bayesian Network, integrating climatic, topographical, hydrological, and socio-economic data to identify high-risk areas and key flood-inducing factor chains.
12. Power grid risk zoning of rainstorm disasters in Anhui Province of China based on the Bayesian Structural Equation Model
Core Problem: Escalating frequency of extreme climatic events, particularly torrential rainfall, has acquired enhanced importance in spatial risk zoning for power grid infrastructure.
Key Innovation: BSEM framework offers an innovative methodology for assessing grid risks during rainstorm disasters, providing practical insights to enhance power sector resilience via improved early warning systems, resource optimization, and infrastructure planning.
13. Effective emergency planning strategies for enhancing flood response
Core Problem: Recurring flash flood events pose a significant threat to everyday life and property in many regions. The lack of emergency preparedness increases the likelihood of flooding, affecting numerous cities annually.
Key Innovation: Develops an evaluation framework for studying emergency planning of flood hazards, comparing two popular approaches to disaster preparedness—the dominant approach and the community-based model—with a focus on their potential application.
14. Classification of earthquake records into fault proximity and pulse characteristics based on machine learning methods
Core Problem: Earthquake record selection is crucial to ensure accurate representation of seismic demands on structures under seismic motions. There is no strict definition for the distinction among earthquake types, despite the significant differences in the resulting structural responses to these vibrations.
Key Innovation: Earthquakes are classified based on fault proximity and pulse characteristics by using three machine learning methods, artificial neural networks (ANN), convolutional neural networks (CNN) and random forest (RF) algorithms.
15. Strengthening coastal flood forecasting through event-based data capitalization: a case study from the December 2022 storm surge in Québec City
Core Problem: Maritime conditions due to climate change will worsen coastal risks. Flood risk mapping is necessary to help decision-makers in coastal management.
Key Innovation: Validates a 2D hydrodynamic model of Saint-Lawrence Fluvial Estuary (SLFE) for coastal flooding risk management in Quebec City and use it to analyze the flood event of December 23rd, 2022.
16. Topographical influence on kilometer-scale hourly precipitation prediction during the 2021 Zhengzhou flood
Core Problem: Topographical forcing plays a fundamental role in extreme precipitation events. There is a need to investigate the influence of topographical factors on precipitation forecast.
Key Innovation: Expands the application of GTWR to quantify the temporally varying topographical influence on extreme precipitation event, filling a gap in the understanding of non-stationary topographical forcing.
17. Volunteer-based disaster response during the 2023 Kahramanmaraş earthquakes: a SWOT analysis
Core Problem: There is a need to investigate the volunteer response to the 2023 Kahramanmaraş earthquakes through a SWOT analysis, focusing on the perspectives of both volunteers and coordinators.
Key Innovation: Identifies significant strengths, weaknesses, opportunities, and threats in volunteer management.
18. Microbially induced calcite precipitation for soil solidification and erosion mitigation: a review
Core Problem: Soil solidification and remediation in geotechnical engineering.
Key Innovation: Provides a comprehensive overview of recent advancements in MICP treatment, focusing on its performance, mechanisms, applications and challenges.
19. Modification of dispersive soil using xanthan gum and water glass: dispersivity, mechanical properties and mechanisms
Core Problem: Dispersive soil has strong dispersibility and low erosion resistance in water.
Key Innovation: Investigates the effects of biopolymer xanthan gum (XG) and water glass (WG) on the and properties of dispersive soil.
20. Effect of geotextile reinforcement on the mechanical behavior of marine calcareous sand retrieved from South China sea
Core Problem: Calcareous sand has been widely utilized as the primary hydraulic filling material in offshore engineering projects.
Key Innovation: Examines the influence of geotextile arrangement type on the mechanical behavior of calcareous sand through a series of drained triaxial shear tests.
21. Response of rock material properties of quartzose sandstones to wildfire: a case study from Bohemian Switzerland National Park, Czechia
Core Problem: Wildfires alter rock properties, increasing vulnerability to erosion and slope processes.
Key Innovation: Multi-step framework using in situ sampling, lab tests, and GIS to assess post-fire rock quality via ultrasonic methods.
22. Transition in rate-dependent shear response of rock-like joints due to clay-infill: strength, dilation, stick-slip, and roughness analysis
Core Problem: Understanding the impact of clay infill on the shear behavior of rock joints under varying displacement rates and normal stresses.
Key Innovation: Quantifying changes in joint roughness using 3D laser scanning and Delaunay triangulation to explain the transition from velocity strengthening to velocity weakening.
23. Soil erosion estimating and hazard mapping using SWAT and RUSLE models in a semiarid-arid region, Zayandeh-Rood Dam Watershed, Central Iran
Core Problem: Accurate soil erosion estimation and hazard mapping in a semiarid-arid watershed.
Key Innovation: Comparison of SWAT and RUSLE models with slope-based SDR for soil loss estimation, identifying regions with severe erosion requiring conservation measures.
24. Unveiling decadal variability of soil erosion and sediment yield using GIS-based RUSLE and SDR techniques – a case study of mountain watershed of the Western Ghats, India
Core Problem: Quantifying soil erosion and sediment yield variability in a mountain watershed due to climate change and land-use changes.
Key Innovation: GIS-based RUSLE-SDR model to identify erosion hotspots and validate sediment yield with dam sedimentation data, providing a database for soil and water conservation planning.
25. Shear characteristics and damage mechanism of carbonaceous mudstone soil-rock mixed fillers based on electrical resistivity
Core Problem: Long-term stability of carbonaceous mudstone soil-rock mixed fillers in road embankments.
Key Innovation: Combining direct shear and resistivity measurements to characterize internal structural damage during shear, providing a theoretical basis for stability analysis.
26. Dynamic characteristics of large-scale ice avalanches under the effect of frictional heat
Core Problem: Understanding the dynamics of large-scale ice avalanches, particularly the role of frictional heat in increasing mobility.
Key Innovation: A two-dimensional model combining thermodynamic and dynamic properties, considering friction weakening due to thermal effects, to simulate ice avalanche dynamics.
27. Key mechanical component properties for a high-ductility energy-absorbing rockfall barrier
Core Problem: Traditional rockfall barriers often fail under high-energy impacts.
Key Innovation: Development of a high-ductility energy-absorbing system using negative Poisson's ratio materials, validated through static/dynamic tests and field monitoring.
28. Three-dimensional deformation field construction method for landslides using ICP algorithm and physics-informed deep learning
Core Problem: Constructing accurate 3D deformation fields for landslides to improve monitoring and early warning.
Key Innovation: Combining an improved ICP algorithm with a physics-informed deep learning model to integrate surface and subsurface deformation data for 3D deformation field reconstruction.
29. Evaluation method of wellbore leakage of underground gas storage salt cavern by intensive injection and production
Core Problem: Preventing wellbore leakage in gas storage salt caverns due to cyclic loads from intensive injection and extraction.
Key Innovation: Developing a nonuniform amplitude fatigue damage model to calculate wellbore leakage pathways and permeability characteristics of the cement sheath.
30. Study on the Mechanical Behavior and Failure Mechanism of Composite Rocks with Rough Structural Planes Under Triaxial Compression and Brazilian Splitting Tests
Core Problem: Understanding the anisotropic mechanical response and failure mechanisms of composite rocks with rough structural planes.
Key Innovation: Performing triaxial compression and Brazilian splitting tests to examine the influence of structural plane parameters on strength, deformation, and failure modes, developing a strength-reduction model.
31. Accurate Measurement of Creep-Induced Degradation in High-Steep Rock Slopes Using Multi-source Monitoring Data: A Case Study of the Lianghekou Pumped Storage Power Station
Core Problem: Accurately assessing long-term creep-induced degradation in high-steep rock slopes.
Key Innovation: Integrating satellite-based InSAR data with multi-point extensometer measurements to constrain a numerical model, using a GAN to augment the dataset, and developing a dual-layer ensemble learning regression model.
32. Evaluating the applicability of simulated soil moisture index in forecasting post-earthquake debris flows
Core Problem: Forecasting debris flows after earthquakes is challenging due to altered soil properties and hydrological conditions.
Key Innovation: Using a simulated soil moisture index to improve the prediction of post-earthquake debris flow events.
33. Factors Influencing Return Migration after Disaster Relocation: Housing Dissatisfaction and Place Attachment in the 2021 Mount Semeru Eruption, Indonesia
Core Problem: Understanding factors influencing return migration after the Mount Semeru eruption.
Key Innovation: Analysis of housing dissatisfaction and place attachment as drivers of return migration.
34. Navigating risk and resilience: Exploring cultural, local responses, livelihoods, and institutions to Mount Merapi's volcanic hazards
Core Problem: Understanding community resilience in the face of volcanic hazards at Mount Merapi.
Key Innovation: Exploration of cultural practices and local knowledge in disaster response.
35. Multiple liquefaction of granular soils in the light of critical state theory: A fundamental review
Core Problem: Understanding the mechanisms and factors influencing multiple liquefaction events in granular soils.
Key Innovation: Review of critical state theory to explain and predict multiple liquefaction in granular soils.
36. Ecohydrological and geomorphological importance of glacial lakes
Core Problem: Understanding the role of glacial lakes in ecohydrology and geomorphology.
Key Innovation: Synthesis of the ecohydrological and geomorphological impacts of glacial lakes.
37. Integrated scan simultaneous trajectory enhancement and mapping (IS²-TEAM) for fine resolution forest inventory using backpack LiDAR
Core Problem: Improving forest inventory using backpack LiDAR.
Key Innovation: Development of IS2-TEAM method for enhanced forest mapping using backpack LiDAR.
38. Unveiling the long-term cascading effects of the 2018 Baige landslide and subsequent outburst flood with satellite radar observations
Core Problem: Understanding the cascading effects of the Baige landslide and subsequent outburst flood.
Key Innovation: Using satellite radar observations to analyze the long-term impacts of a landslide and its consequences.
39. An extended vector inclination method for inferring detailed slip surfaces beneath landslides from SAR and optical satellite remote sensing image
Core Problem: Inferring detailed slip surfaces beneath landslides.
Key Innovation: Extending the vector inclination method using SAR and optical satellite imagery to map landslide slip surfaces.
40. Mapping wide-area land subsidence from groundwater use in the North China plain by machine learning-based InSAR adjustment
Core Problem: Mapping land subsidence due to groundwater use.
Key Innovation: Using machine learning to adjust InSAR data for mapping land subsidence.
41. Sentinel-1 imagery for wide-scale quantitative landslide vulnerability assessment of buildings
Core Problem: Assessing landslide vulnerability of buildings.
Key Innovation: Using Sentinel-1 imagery for quantitative landslide vulnerability assessment.
42. Prediction and interpretation of high-temperature heat damage risk zones in the Eastern Tibetan Transport Corridor based on a location-information-fusion convolution neural network
Core Problem: Predicting heat damage risk zones in a transport corridor.
Key Innovation: Using a location-information-fusion CNN to predict high-temperature heat damage risk.
43. AEOS: Active Environment-aware Optimal Scanning Control for UAV LiDAR-Inertial Odometry in Complex Scenes
Core Problem: UAV LiDAR-Inertial Odometry in complex scenes.
Key Innovation: Active Environment-aware Optimal Scanning Control for UAV LiDAR.
44. TFRSUB: A terrain-feature retention and spatial uniformity balancing method for simplifying LiDAR ground point clouds
Core Problem: Simplifying LiDAR ground point clouds while retaining terrain features.
Key Innovation: Terrain-feature retention and spatial uniformity balancing method.
45. Enhancing monocular height estimation via sparse LiDAR-guided correction
Core Problem: Monocular height estimation.
Key Innovation: Sparse LiDAR-guided correction.
46. TasselNetV4: A vision foundation model for cross-scene, cross-scale, and cross-species plant counting
Core Problem: Plant counting across different scenes and scales.
Key Innovation: Vision foundation model for plant counting.
47. Enhancing snow depth estimation in forested regions of the northern hemisphere: A physically-constrained machine learning approach with spatiotemporal dynamics
Core Problem: Snow depth estimation in forested regions.
Key Innovation: Physically-constrained machine learning with spatiotemporal dynamics.
48. High-precision flood change detection with lightweight SAR transformer network and context-aware attention for enriched-diverse and complex flooding scenarios
Core Problem: Flood change detection in complex scenarios.
Key Innovation: Lightweight SAR transformer network with context-aware attention.
49. Response of the Sentinel-1 radar backscattering to an extreme wildfire event: surface soil moisture and vegetation cover implications
Core Problem: Understanding the impact of wildfires on surface soil moisture and vegetation cover using remote sensing data.
Key Innovation: Analyzing Sentinel-1 radar backscattering to assess changes in soil moisture and vegetation following a wildfire.
50. Towards 90 m resolution digital terrain model combining ICESat-2 and GEDI data: Balancing accuracy and sampling intensity
Core Problem: Creating high-resolution digital terrain models (DTMs) using satellite data.
Key Innovation: Combining ICESat-2 and GEDI data to generate a 90m resolution DTM, balancing accuracy and sampling intensity.
51. Synergistic effects of drip irrigation and vegetation on the stability of biochar-stabilized expansive soil slopes
Core Problem: Stabilizing expansive soil slopes, which are prone to landslides and erosion.
Key Innovation: Investigating the combined effects of drip irrigation, vegetation, and biochar on the stability of expansive soil slopes.
52. Automated gully erosion extraction in the typical black soil region of Northeast China using a deep learning approach based on multi-source remote sensing data
Core Problem: Gully erosion in black soil regions needs efficient and accurate monitoring.
Key Innovation: Deep learning approach for gully erosion extraction using multi-source remote sensing data.
53. Electrical resistivity tomography involvement to investigate collapse dolines in Mogress area, Doukkala plain (Western Morocco)
Core Problem: Investigating collapse dolines and subsurface structures in the Mogress area.
Key Innovation: Using electrical resistivity tomography to characterize subsurface features related to collapse dolines.
54. Experimental investigation and calculation prediction model of frost heave-induced pressure in sand under lateral constraint freezing condition
Core Problem: Understanding and predicting frost heave pressure in sand under constrained conditions.
Key Innovation: Developing a calculation model for frost heave-induced pressure based on experimental data.
55. Assessing the impacts of climate warming and engineering activities on the thermal regime of permafrost in the Kunlun Mountains, Qinghai-Tibet Railway
Core Problem: Assessing permafrost degradation due to climate warming and engineering activities.
Key Innovation: Analyzing the thermal regime of permafrost along the Qinghai-Tibet Railway.
56. The impact of soluble salt on the reconstruction of saline gap-graded soil particle composition during freeze-thaw cycles
Core Problem: Understanding the impact of soluble salts on soil composition during freeze-thaw cycles.
Key Innovation: Investigating the reconstruction of saline gap-graded soil particle composition.
57. The novel high-order two-phase updated-Lagrangian nonlocal general particle dynamics for the coupled seepage flow and large deformation problems
Core Problem: Modeling coupled seepage flow and large deformation problems.
Key Innovation: A high-order two-phase updated-Lagrangian nonlocal general particle dynamics method is introduced.
58. Mitigation of Liquefaction Risk in Layered Soils via Stone Column Drains: Numerical Study and Novel Uncoupled Approach
Core Problem: Liquefaction risk in layered soils.
Key Innovation: Numerical study of stone column drains using a novel uncoupled approach.
59. Impacts of elevation bias and topographic uncertainty on flood modeling: model robustness and floodplain sensitivity mapping in a lowland River Basin
Core Problem: Impact of topographic uncertainty on flood modeling accuracy.
Key Innovation: Analysis of model robustness and floodplain sensitivity mapping in a lowland river basin.
60. Urban flood prediction using a hybrid XGBoost-enhanced U-Net model
Core Problem: Accurate urban flood prediction.
Key Innovation: Hybrid XGBoost-enhanced U-Net model for improved flood prediction.
61. Probabilistic stratigraphic modeling of waste soil landfills using multiple UAV data
Core Problem: Characterizing waste soil landfills.
Key Innovation: Using multiple UAV data for probabilistic stratigraphic modeling.
62. Probabilistic evaluation of earthquake-induced soil liquefaction using 3D spatial variability modeling and performance-based design: A case study from İzmir, Türkiye
Core Problem: Assessment of soil liquefaction potential in İzmir, Türkiye, considering 3D spatial variability.
Key Innovation: Probabilistic evaluation of liquefaction using 3D spatial variability modeling and performance-based design.
63. Temporal evolution of site response in Petobo's post-liquefaction zone, Palu, Indonesia: A comparative HVSR curve analysis
Core Problem: Understanding the temporal changes in site response following liquefaction in Petobo, Palu, Indonesia.
Key Innovation: Comparative analysis of HVSR curves to track site response evolution post-liquefaction.
64. Seismic fragility assessment of the crane and pile-supported wharf system under liquefaction conditions considering IMs optimization and damage characteristics
Core Problem: Assessing the seismic vulnerability of crane and pile-supported wharf systems under liquefaction.
Key Innovation: Fragility assessment considering optimized intensity measures and damage characteristics under liquefaction.
65. Novel seismic Dam Damage Intensity scale and empirical models for predicting seismically induced damages in embankment dams
Core Problem: Predicting seismically induced damage in embankment dams.
Key Innovation: Development of a new seismic Dam Damage Intensity scale and empirical models.
66. Energy-based evaluation of strain accumulation and shakedown behavior of red-stratum mudstone fill material under long-term cyclic loading
Core Problem: Evaluating the long-term behavior of red-stratum mudstone fill under cyclic loading.
Key Innovation: Energy-based assessment of strain accumulation and shakedown behavior.
67. Improved analysis method for frame beams with prestressed cables in slopes based on inclined foundation stiffness
Core Problem: Analysis of frame beams with prestressed cables in slopes.
Key Innovation: An improved analysis method for frame beams in slopes.
68. Bearing capacity, shear band evolution, and deformation characteristics of slopes reinforced by root-inspired anchors using transparent soil model testing
Core Problem: Understanding the behavior of slopes reinforced with root-inspired anchors.
Key Innovation: Using transparent soil model testing to analyze slope reinforcement.
69. Slope rockbolting using key block theory: Force transfer and artificial intelligence-assisted multi-objective optimisation
Core Problem: Optimizing slope rockbolting design.
Key Innovation: Combining key block theory with AI for rockbolting optimization.
70. Prediction of long-term deformation of slope in Baihetan hydropower station based on updated rheological parameters with Bayesian estimation
Core Problem: Long-term slope deformation prediction.
Key Innovation: Bayesian estimation for updating rheological parameters.
71. Soil desiccation cracking triggered by surface defects: Insight and mechanism based on strain/displacement analysis using DIC
Core Problem: Soil desiccation cracking mechanism.
Key Innovation: Strain/displacement analysis using DIC.
72. Erosion dynamics in loess with artificial joints: An experimental approach
Core Problem: Understanding erosion processes in loess soils, particularly the role of joints in accelerating erosion.
Key Innovation: Experimental investigation of erosion dynamics in loess with artificial joints.
73. Creep damage characteristics of microbially induced calcite precipitation-treated fractured sandstone: Experimental and particle flow approaches
Core Problem: Investigating the creep behavior of fractured sandstone treated with microbially induced calcite precipitation (MICP).
Key Innovation: Combining experimental and particle flow approaches to analyze creep damage characteristics in MICP-treated sandstone.
74. Early warning system for risk assessment in geotechnical engineering using Kolmogorov-Arnold networks
Core Problem: Developing an early warning system for risk assessment in geotechnical engineering.
Key Innovation: Using Kolmogorov-Arnold networks for early warning in geotechnical applications.
75. Glacial Lake Observatory (GLO): Annual dataset of glacial lakes in Nepal and transboundary catchments (2017–2024)
Core Problem: Glacial lakes are expanding due to melting glaciers in High Mountain Asia, posing a risk of outburst floods (GLOFs) to downstream communities. Monitoring these lakes is crucial for hazard reduction.
Key Innovation: A dataset of glacial lakes in Nepal-transboundary region (2017–2024) mapped using satellite imagery and deep learning, revealing rapid lake growth, especially in the Koshi basin. Supports the Glacial Lake Observatory for long-term monitoring and hazard reduction.
76. Towards Bedmap Himalayas: a new airborne glacier thickness survey in Khumbu Himal, Nepal
Core Problem: Models estimating ice storage and predicting future changes in High Mountain Asia require accurate glacier thickness measurements, which are currently limited.
Key Innovation: A new, extensive dataset of glacier thickness from the Khumbu Himal around Mount Everest, doubling the extent of thickness measurements in High Mountain Asia. Key input for models estimating ice storage and predicting future changes.
77. Spatial distribution patterns and landslide susceptibility analysis from a global–local perspective along the Zhuzhou-Guangzhou section of the Beijing–Guangzhou railway
Core Problem: Landslides along the Beijing-Guangzhou Railway pose a significant risk due to steep terrain and climatic variability.
Key Innovation: A multi-scale landslide susceptibility assessment framework based on a 'global modeling–local analysis' approach, integrating random forest and SHAP method to quantify factor contributions.
78. Global landslide mapping using U-Net architecture with diverse backbones across multi-regional and multi-sensor remote sensing datasets
Core Problem: Accurate and scalable landslide detection techniques are needed for societies in hilly regions worldwide.
Key Innovation: Comprehensive benchmark of U-Net architecture with various backbone networks (EfficientNet, ResNet, etc.) for semantic segmentation of landslides using multi-regional remote sensing datasets, emphasizing the importance of backbone selection for accuracy.
79. The failure modes of different bedding slate under uniaxial compression
Core Problem: Understanding the mechanical behavior and failure modes of slate, influenced by its layered structure, is crucial for slope stability analysis.
Key Innovation: Systematic examination of the effects of dip angles on compressive strength, stress-strain curves, and failure patterns of slate under uniaxial compression, combined with PFC-2D modeling to simulate the failure process.
80. Editor's Choice: Dense 3D Displacement Estimation for Landslide Monitoring via Fusion of TLS Point Clouds and Embedded RGB Images
Core Problem: Accurate landslide monitoring is crucial for risk assessment and mitigation, but existing point cloud methods often suffer from sparsity or lack detailed 3D displacement estimates.
Key Innovation: This research addresses this limitation by developing a hierarchical partitioning method that integrates 3D point clouds and RGB images to generate dense 3D displacement vector fields. This fusion of data sources promises to improve spatial coverage and accuracy in landslide monitoring, providing valuable information for early warning systems and infrastructure protection. The combination of readily available data (RGB images) with more precise point cloud data makes this approach particularly promising for real-world applications.