TerraMosaic Daily Digest: Jan 20, 2026
Daily Summary
This digest synthesizes 94 selected papers and focuses on landslide process mechanics and slope evolution, high-resolution remote-sensing monitoring workflows, flood generation, routing, and hydroclimatic forcing. Top-ranked studies examine satellite and LiDAR-based deformation monitoring, flood generation and hydroclimatic forcing, and wildfire hazard and adaptation.
Across the full set, evidence converges on mechanism-constrained analysis with operational relevance, especially for seismic source-to-ground response pathways and infrastructure-focused hazard performance. The strongest contributions pair interpretable process evidence with monitoring or forecasting workflows that support warning design and risk prioritization.
Key Trends
- Landslide studies increasingly resolve process chains: Contributions connect triggering conditions, slope deformation, and mobility outcomes, improving the basis for warning thresholds and scenario testing.
- Monitoring workflows rely on integrated remote-sensing products: Multi-source satellite and airborne observations are used for deformation retrieval, change detection, and rapid post-event mapping.
- Flood analyses are becoming event-specific and process-based: Papers emphasize precipitation structure, antecedent wetness, and catchment controls rather than static hazard descriptors.
- Seismic hazard research links source behavior to ground response: Recurring topics connect rupture or loading conditions with geotechnical performance and consequence assessment.
- Infrastructure-facing outputs are increasingly decision-ready: Asset performance is evaluated with uncertainty-aware frameworks to support mitigation and maintenance prioritization.
Selected Papers
This digest features 94 selected papers from 3,097 papers analyzed across multiple journals and preprint servers. Each paper has been evaluated for its relevance to landslides and related geohazards and includes links to the original publications.
1. The use of spectral indices in environmental monitoring of smouldering coal-waste dumps
Core Problem: Monitoring thermally active zones and vegetation conditions in degraded post-industrial environments using remote sensing data.
Key Innovation: UAV-based remote sensing combined with field validation to assess environmental indices for monitoring smouldering coal-waste dumps, highlighting seasonal variability and disrupted vegetation cycles.
2. UAV-Based Infrastructure Inspections: A Literature Review and Proposed Framework for AEC+FM
Core Problem: Improving accuracy and reliability in detecting structural defects, thermal anomalies, and geometric inconsistencies in infrastructure inspections using UAVs.
Key Innovation: A workflow framework integrating RGB imagery, LiDAR, and thermal sensing with transformer-based architectures for precise and actionable insights in complex environments.
3. RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection
Core Problem: Localizing and characterizing scene changes between two time points in remote sensing images for environmental monitoring and disaster assessment.
Key Innovation: A new VAR-based change detection framework that conditions autoregressive prediction on multi-resolution fused bi-temporal features via cross-attention, and employs an autoregressive training strategy designed specifically for change map prediction.
4. SAR-Based Marine Oil Spill Detection Using the DeepSegFusion Architecture
Core Problem: Detecting oil spills from satellite Synthetic Aperture Radar (SAR) images with high accuracy and low false alarm rates.
Key Innovation: A hybrid deep learning model, DeepSegFusion, integrating SegNet and DeepLabV3+ with an attention-based feature fusion mechanism for improved boundary precision and contextual understanding in oil spill segmentation.
5. Physics-Constrained Denoising Autoencoders for Data-Scarce Wildfire UAV Sensing
Core Problem: Obtaining high-resolution atmospheric measurements during wildfires using low-cost sensors on Unmanned Aerial Vehicles (UAVs) that are prone to noise and data scarcity.
Key Innovation: A physics-informed denoising autoencoder (PC^2DAE) that addresses data scarcity by embedding physical constraints directly into the network architecture, ensuring physically admissible outputs and improving smoothness and noise reduction.
6. LTV-YOLO: A Lightweight Thermal Object Detector for Young Pedestrians in Adverse Conditions
Core Problem: Detecting vulnerable road users (VRUs), particularly children and adolescents, in low light and adverse weather conditions.
Key Innovation: A lightweight object detection model based on the YOLO architecture, termed LTV-YOLO, optimized for thermal detection using long-wave infrared (LWIR) cameras, separable convolutions in depth, and a feature pyramid network (FPN).
7. Task-Driven Prompt Learning: A Joint Framework for Multi-modal Cloud Removal and Segmentation
Core Problem: Cloud occlusion limits the utility of optical remote sensing imagery for Earth observation. Existing cloud removal methods often oversmooth textures and boundaries critical for analysis-ready data (ARD).
Key Innovation: A task-driven multimodal framework (TDP-CR) jointly performs cloud removal and land-cover segmentation. A Prompt-Guided Fusion (PGF) mechanism utilizes a learnable degradation prompt to encode cloud thickness and spatial uncertainty, adaptively integrating SAR information only where optical data is corrupted.
8. CroBIM-V: Memory-Quality Controlled Remote Sensing Referring Video Object Segmentation
Core Problem: Remote sensing video referring object segmentation (RS-RVOS) is challenged by weak target saliency and severe visual information truncation in dynamic scenes, making it extremely difficult to maintain discriminative target representations during segmentation.
Key Innovation: A memory-quality-aware online referring segmentation framework, termed Memory Quality Control with Segment Anything Model (MQC-SAM). MQC-SAM introduces a temporal motion consistency module for initial memory calibration and incorporates a decoupled attention-based memory integration mechanism with dynamic quality assessment.
9. IceWatch: Forecasting Glacial Lake Outburst Floods (GLOFs) using Multimodal Deep Learning
Core Problem: Classical methods of GLOF detection and prediction rely on hydrological modeling, threshold-based lake monitoring, and manual satellite image analysis, suffering from slow updates, reliance on manual labor, and losses in accuracy when clouds interfere and/or lack on-site data.
Key Innovation: A novel deep learning framework for GLOF prediction that incorporates both spatial and temporal perspectives. RiskFlow uses Sentinel-2 imagery and a CNN-based classifier. TerraFlow models glacier velocity, and TempFlow forecasts near-surface temperature, integrated via harmonized preprocessing and synchronization. This is the Editor's Choice paper highlighted in this digest.
10. Adaptive Multi-Scale Correlation Meta-Network for Few-Shot Remote Sensing Image Classification
Core Problem: Few-shot learning in remote sensing remains challenging due to the scarcity of labeled data, substantial domain shifts, and the multi-scale nature of geospatial objects.
Key Innovation: Adaptive Multi-Scale Correlation Meta-Network (AMC-MetaNet), a lightweight framework with correlation-guided feature pyramids, an adaptive channel correlation module (ACCM), and correlation-guided meta-learning that leverages correlation patterns instead of conventional prototype averaging.
11. SDCoNet: Saliency-Driven Multi-Task Collaborative Network for Remote Sensing Object Detection
Core Problem: In remote sensing images, complex backgrounds, weak object signals, and small object scales make accurate detection particularly challenging, especially under low-quality imaging conditions.
Key Innovation: A Saliency-Driven multi-task Collaborative Network (SDCoNet) that couples SR and detection through implicit feature sharing while preserving task specificity. A multi-scale saliency prediction module produces importance scores to select key tokens.
12. TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement
Core Problem: Accurate measurement of tree diameter at breast height (DBH) from aerial imagery is difficult due to distant and sparsely observed trunks.
Key Innovation: Uses 3D Gaussian Splatting as a continuous, densifiable scene representation for trunk measurement, extracting a dense point set and estimating DBH using opacity-weighted solid-circle fitting.
13. Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures
Core Problem: Crack detection in low-light environments is challenging due to poor image quality and the need for large annotated datasets.
Key Innovation: A dual-branch prototype learning network integrating Retinex theory with few-shot learning for low-light crack segmentation, using reflectance components and metric learning.
14. GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction
Core Problem: Existing geo-localization benchmarks largely ignore the temporal information inherent in images, which can further constrain the location.
Key Innovation: Introduces GTPred, a novel benchmark for geo-temporal prediction, comprising 370 globally distributed images spanning over 120 years, to evaluate MLLM predictions by jointly considering year and hierarchical location sequence matching.
15. HiT: History-Injection Transformers for Onboard Continuous Flood Change Detection
Core Problem: Natural disaster monitoring through continuous satellite observation requires processing multi-temporal data under strict operational constraints, especially for flood detection.
Key Innovation: Proposes History Injection mechanism for Transformer models (HiT), that maintains historical context from previous observations while reducing data storage by over 99% of original image size, achieving real-time hazard assessment without dependency on ground-based processing infrastructure.
16. DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes
Core Problem: Lack of suitable VQA datasets for disaster response, hindering the development of robust vision-language models for crisis contexts.
Key Innovation: A new benchmark dataset, DisasterVQA, consisting of real-world disaster images and expert-curated question-answer pairs grounded in humanitarian frameworks, designed to evaluate perception and reasoning in crisis scenarios.
17. OmniOVCD: Streamlining Open-Vocabulary Change Detection with SAM 3
Core Problem: Existing open-vocabulary change detection (OVCD) methods rely on combining multiple models (e.g., CLIP and DINO), leading to feature matching problems and system instability.
Key Innovation: A standalone OVCD framework, OmniOVCD, leveraging the Segment Anything Model 3 (SAM 3) and a Synergistic Fusion to Instance Decoupling (SFID) strategy to achieve state-of-the-art performance in change detection without relying on predefined categories or multiple models.
18. Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression
Core Problem: Traditional wildfire management is mostly reactive. There is a need for proactive AI frameworks that combine wildfire forecasting with intelligent suppression strategies.
Key Innovation: FireCastRL, a proactive AI framework that combines a deep spatiotemporal model to predict wildfire ignition with a reinforcement learning agent to execute real-time suppression tactics using helitack units inside a physics-informed 3D simulation.
19. Mining Citywide Dengue Spread Patterns in Singapore Through Hotspot Dynamics from Open Web Data
Core Problem: Predicting dengue fever outbreaks in urban environments using open web data is challenging due to the complex interplay of factors influencing disease transmission.
Key Innovation: A novel framework that uncovers latent transmission links between urban regions by modeling how hotspot formation in one area is influenced by epidemic dynamics in neighboring regions, aligning closely with commuting flows.
20. Forecasting Continuum Intensity for Solar Active Region Emergence Prediction using Transformers
Core Problem: Early and accurate prediction of solar active region (AR) emergence is crucial for space weather forecasting.
Key Innovation: A sliding-window Transformer architecture to forecast continuum intensity evolution up to 12 hours ahead, modified with early detection biases for high-sensitivity detection.
21. Synthetic Geology: Structural Geology Meets Deep Learning
Core Problem: Reconstructing subsurface structural geology from sparse surface data is ill-posed and lacks training data for deep learning.
Key Innovation: A geological simulation engine (StructuralGeo) generates synthetic 3D lithological models for training generative flow-matching models to reconstruct plausible 3D geological scenarios from surface data.
22. A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior
Core Problem: Lack of systematic understanding of the generalizability limits of fault delineation models across diverse seismic data.
Key Innovation: A large-scale benchmarking study provides guidelines for domain shift strategies in seismic interpretation, assessing pretraining, fine-tuning, and joint training across synthetic and real datasets.
23. Automating Traffic Monitoring with SHM Sensor Networks via Vision-Supervised Deep Learning
Core Problem: CV-based traffic monitoring suffers from privacy concerns and lighting sensitivity, while non-vision methods lack deployment flexibility.
Key Innovation: A deep-learning pipeline integrates CV-assisted dataset generation with supervised training and inference, using GNNs to transfer knowledge from CV to SHM sensor networks for automated traffic monitoring.
24. WaveletInception Networks for on-board Vibration-Based Infrastructure Health Monitoring
Core Problem: Analyzing on-board vibration signals for infrastructure health monitoring requires effective handling of varying speeds and localized assessments.
Key Innovation: A WaveletInception-BiGRU network uses a Learnable Wavelet Packet Transform, 1D Inception-ResNet modules, and BiGRU modules to analyze vibration signals at varying speeds and generate high-resolution health profiles.
25. Fine-grained spatial-temporal perception for gas leak segmentation
Core Problem: Efficient and accurate detection and segmentation of gas leaks are limited due to their concealed appearance and random shapes.
Key Innovation: A Fine-grained Spatial-Temporal Perception (FGSTP) algorithm captures motion clues across frames and integrates them with refined object features in an end-to-end network for gas leak segmentation.
26. Mapping Hidden Heritage: Self-supervised Pre-training on High-Resolution LiDAR DEM Derivatives for Archaeological Stone Wall Detection
Core Problem: Automated mapping of dry-stone walls in remote, vegetated areas is difficult due to occlusion and limited labeled data.
Key Innovation: A self-supervised cross-view pre-training framework (DINO-CV) using LiDAR-derived DEMs to map dry-stone walls, addressing occlusion and data scarcity.
27. Assessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-users
Core Problem: It is unclear whether Geo-Foundational Models (GFMs) outperform traditional models like U-Net for flood inundation mapping across different sensors and data availability scenarios.
Key Innovation: A systematic comparison of GFMs (Prithvi 2.0, Clay V1.5, DOFA, and UViT) against TransNorm, U-Net, and Attention U-Net using PlanetScope, Sentinel-1, and Sentinel-2 data for flood mapping.
28. CymbaDiff: Structured Spatial Diffusion for Sketch-based 3D Semantic Urban Scene Generation
Core Problem: Generating realistic and semantically rich outdoor 3D scenes from abstract sketches is limited by the absence of large-scale, well-annotated datasets.
Key Innovation: A new benchmark, SketchSem3D, and a Cylinder Mamba Diffusion (CymbaDiff) model that enhances spatial coherence in outdoor 3D scene generation from sketches.
29. Balanced Diffusion-Guided Fusion for Multimodal Remote Sensing Classification
Core Problem: Pre-training multimodal diffusion models may result in modality imbalance, and effectively leveraging diffusion features to guide complementary diversity feature extraction remains an open question.
Key Innovation: A balanced diffusion-guided fusion (BDGF) framework that leverages multimodal diffusion features to guide a multi-branch network for land-cover classification, using an adaptive modality masking strategy.
30. Characterization of the 2020 US-231 landslide near Lacey Springs, Alabama and the importance of nonlinear strength envelopes
Core Problem: A large landslide occurred on US-231 in Alabama, requiring a 7-month road closure. The movement occurred in a weathered shale layer, and understanding the soil properties is crucial for stability analysis.
Key Innovation: Characterization of a landslide in degraded illite, fitting a nonlinear residual strength envelope, and demonstrating the importance of considering this nonlinearity in slope stability analyses.
31. Emergency intervention for the early estimation of slow creep motion on the active faults of La Palma (Canary Islands, Spain)
Core Problem: Creep movements along the Tazacorte (TZF) and Mazo (MZF) faults on La Palma are affecting infrastructure after the 2021 Tajogaite eruption. Monitoring and understanding these movements are essential for land-use planning and risk assessment.
Key Innovation: Using precision fissurometer to monitor displacement rates along active faults, revealing increased creep velocity post-eruption, and delineating active fault zones through surface fracture patterns.
32. Ground deformation prediction based on SBAS-InSAR and RBF neural network: a case study of Zhengzhou Metro Line 10
Core Problem: Ground deformation from metro construction and operation can cause significant damage. Traditional monitoring methods are limited in spatial coverage and early warning capabilities.
Key Innovation: Integrating SBAS-InSAR and RBF neural networks to monitor and predict ground deformation along a metro line, providing an effective approach for early risk warning and route optimization.
33. Variability of landslide susceptibility models under different ground motion scenarios in Rasuwa district, Nepal
Core Problem: Landslide susceptibility modeling in earthquake-prone regions is challenging due to the spatial variability and uncertainty of ground shaking. Robust characterization of ground motion is critical for reliable susceptibility models.
Key Innovation: Integrating probabilistic seismic hazard assessment (PSHA) and landslide susceptibility modeling (LSM) to investigate the influence of earthquake recurrence and ground motions on landslide probability in Nepal.
34. Influence of thickness on the buffering effect of granular soil cushion against rockfall impact
Core Problem: Granular soil cushions are used on rock sheds to buffer rockfall impacts. A rational design of these cushions requires understanding the influence of cushion thickness on the buffering effect.
Key Innovation: Using a numerical discrete element model to investigate the impact of rock blocks on granular soil cushions of different thicknesses, analyzing the influence on impact force and proposing a method to determine the maximum allowed cushion thickness.
35. From hydraulic hysteresis to shear strength: A hydraulic history-dependent intergranular stress model for unsaturated soils
Core Problem: Accurate prediction of slope stability is challenged by hydraulic hysteresis induced by rainfall infiltration.
Key Innovation: A hydraulic history-dependent intergranular stress model is developed, integrating a state-dependent soil–water retention model to characterize capillary hysteresis and air entrapment.
36. Experimental study on the benefits of nature-based solutions for debris-flow mitigation via synergistic eco-geotechnical measures
Core Problem: Isolated mitigation measures for debris-flow hazards lack coordinated approaches.
Key Innovation: Tree-shrub mixed-vegetation filter strips (T-SMVFS) along S-shaped flow paths combined with dams for debris-flow velocity, sediment transport, and energy reduction.
37. Ground motion predictive equations for high order intensity and strong motion duration parameters for shallow earthquakes in Greece
Core Problem: Common strong motion parameters overlook the influence of the frequency content of ground motion and the duration of the shaking.
Key Innovation: New GMPEs for five high-order intensity parameters, as well as, for three definitions of strong motion duration for shallow earthquakes in Greece are proposed using ANN models and nonlinear regression.
38. Physics-based simulations of the foreshock and aftershock of the February 6, 2023 Turkiye earthquakes and ground motion validation
Core Problem: Understanding site-specific effects with reduced influence from the complex source characteristics typically associated with large events and from nonlinear behavior of near-surface soils.
Key Innovation: 3D physics-based ground motion simulations conducted for southern Turkiye, focusing on the foreshocks and aftershocks associated with the February 6, 2023 Turkiye earthquake sequence.
39. A multi-parameter empirical prediction model for the spectral amplification factor of pulse-like ground motions
Core Problem: Need for quantitatively optimizing the input of pulse-like ground motions (PGMs) during the design phase.
Key Innovation: A multi-parameter empirical model for the pulse amplification factor (PAF), incorporating the response spectrum amplification factor ( A f ) and the velocity pulse period ( T p ).
40. Self-centering prestressed concrete frames with low prestressing and slope friction: design method and seismic performance
Core Problem: Achieving complete self-centering of the structure typically requires that the contribution of energy-dissipating devices be less than that of the initial prestress, resulting in insufficient energy dissipation capacity and stiffness during large earthquakes.
Key Innovation: A novel self-centering prestressed concrete (SCPC) frame that employs low prestressing and slope friction to achieve a high energy dissipation ratio and improved stiffness following connection openings.
41. Adaptive portfolio optimization-based metamodel method for the multi-armed bandit problem of learning function in slope reliability analysis
Core Problem: Calculating failure probabilities of slopes is challenging due to the variability in slope scenarios and the difficulty in adaptively selecting the optimal function under high-dimensional random field domains.
Key Innovation: A portfolio optimization-based adaptive polynomial-chaos Kriging (POPCK) method is proposed that dynamically balances exploration and exploitation of distinct learning functions, thereby adaptively selecting better functions based on their historical performance.
42. Ontology - and data-driven defect diagnosis with knowledge graphs and causal reasoning: Application to the risk management of gravity dams
Core Problem: Gravity dams face multi-source defect evolution that challenges reliability and risk management. Traditional diagnostic approaches rely heavily on expert judgment, which limits efficiency and leads to incomplete defect coverage.
Key Innovation: A reliability-oriented defect diagnosis framework that integrates ontology construction, knowledge graph representation, and causal reasoning is developed. A domain-specific ontology is established to formalize defect concepts, causal relations, and monitoring indicators.
43. Study on unsupervised gas outburst hazard early warning method based on spatiotemporal graph convolution network
Core Problem: Coal and gas outbursts are jointly influenced by geological factors, gas occurrence, and the physical properties of coal. Analyzing the spatiotemporal data features during tunneling and constructing a spatiotemporal data fusion model are essential for addressing such disasters.
Key Innovation: A deep learning, based BN-spatiotemporal graph convolution model is proposed for gas outburst early warning. An STGAT module is constructed to learn the coupling relationships among spatiotemporal data and to obtain anomaly scores.
44. Adaptive blockchain task sharding for reliable V2X incident management
Core Problem: Integrating blockchain into V2X systems poses real-time processing system reliability challenges, as Roadside Units (RSUs) must simultaneously support latency-sensitive services and resource-intensive consensus protocols.
Key Innovation: A task-sharding mechanism for blockchain-integrated V2X incident management is proposed. The method comprehensively shards both blockchain operations and V2X services, leveraging Software-Defined RSUs (SD-RSUs) to allocate resources to distinct service shards.
45. STFI: A Spatio-Temporal Deep Fusion Architecture for High-Accuracy Fire Spot Identification Near Power Transmission Lines
Core Problem: Accurate identification of small-scale, localized fire events near power transmission infrastructure is challenging due to limited spatiotemporal resolution and generalization capabilities of conventional satellite-based wildfire detection methods.
Key Innovation: A novel spatio-temporal deep fusion-based fire spot identification (STFI) framework that leverages high-frequency Himawari-8/9 geostationary satellite data, with an emphasis on the simultaneous and hierarchical extraction of multi-spatiotemporal wildfire features at multiple levels and their deep fusion.
46. Amplified deviation flood index (ADFI) for fast non-prior flood detection
Core Problem: Timely and accurate flood mapping is crucial, but previous methods often require prior knowledge of flood events, which is usually incomplete or unavailable when studying historical floods.
Key Innovation: A new amplified deviation flood index (ADFI) using the time-series anomaly statistics from Synthetic Aperture Radar (SAR) data for mapping fully flooded areas without relying on prior knowledge of flood events.
47. A transformer-based multi-task deep learning model for urban livability evaluation by fusing remote sensing and textual geospatial data
Core Problem: Evaluating urban livability is important for policymakers to develop urban planning and development strategies aimed at improving livability, but mainstream methods of evaluating urban livability assign different weights to diverse indicators extracted from survey data, statistical data, and geospatial data, which is time-consuming and labor-intensive data collection.
Key Innovation: A transformer-based multi-task multimodal regression (TMTMR) model for the simultaneous evaluation of urban livability focusing on five domain-specific scores, using remote sensing and geospatial data directly.
48. Mapping urban built-up types from 2000 to 2022 at 10-m resolution using super-resolution of Landsat spectral-temporal metrics and center-patch classification
Core Problem: Mapping large areas over multiple decades poses several challenges, specifically, the coarse resolution of historical Landsat data (30-m) limits the capacity to capture the spatial detail of diverse built-up types, and existing mapping techniques face limitations in capturing spatial context, compromise mapping resolution, or rely on hand-crafted training data.
Key Innovation: Enhancing historical Landsat spatial resolution to 10-m using a generative super-resolution model, with a focus on synthetic images derived from spectral-temporal metrics, and a “center-patch classification” method, wherein patch images serve as input for the central pixel classification.
49. Remote sensing of the global cryosphere: Status, processes, and trends
Core Problem: Monitoring changes in glaciers, snow, glacial lakes, permafrost, sea ice, and ice shelves across the Earth's three poles is crucial but challenging.
Key Innovation: Review of multi-sensor satellite observations, high-resolution digital elevation models (DEMs), and deep learning techniques for cryosphere monitoring.
50. Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana)
Core Problem: Seasonal water fluctuations in flood-pulsed wetlands are critical for ecosystem dynamics, but monitoring these dynamics is challenging.
Key Innovation: Using Sentinel-1 InSAR coherence time series to characterize hydrological dynamics in the Okavango Delta, mapping flood frequency and extent with high accuracy.
51. Runoff hydrodynamic variations on terraced slopes with different soil types in the dry–hot valley region, Southwest China
Core Problem: Soil erosion on terraced slopes leads to substantial soil loss, but the impact of soil type on runoff hydrodynamics is not well understood.
Key Innovation: Artificial rainfall experiments on terraced slopes with varying soil types to quantify the influence on runoff shear stress and identify critical dynamic conditions for different erosion types.
52. Comparative studies of bench-scale experiments examining the effect of pipe flows on landslide initiation
Core Problem: Understanding the effect of pipe flows on landslide initiation, considering the combined influence of multiple factors.
Key Innovation: Intercomparison of bench-scale experiments to analyze how factors like clogging, joints, and air entrapment influence the critical flow rate of soil pipes and pore water pressure, leading to slope instability.
53. Sediment yield in mountain regions in the context of climate change: A systematic review
Core Problem: Climate change impacts on soil erosion and sediment yields in mountain environments are not well understood due to limitations in modeling practices and data.
Key Innovation: Systematic review of studies on climate-induced sediment yield, revealing the predominant use of conceptual and empirical models with coarse-resolution climate data, and highlighting the need for improved model validation and geographic representation.
54. Recurrent historical lahars in Jamapa Gorge, Pico de Orizaba volcano, Mexico: Geological and dendrochronological evidence
Core Problem: Lahars pose a significant risk in volcanic areas, but their historical occurrence in Mexican volcanoes is understudied, hindering accurate hazard assessments.
Key Innovation: Integration of UAV-based geological mapping and dendrochronological sampling to reconstruct the spatiotemporal occurrence of lahars in the Jamapa Gorge, providing minimum ages for terrace formation and correlating lahar deposits with specific events.
55. Experimental and numerical study of air–water two-phase flow in unsaturated loess with implications for slope stability
Core Problem: Air migration in the vadose zone affects hydrological processes and slope stability, but the dynamics of coupled water-air migration in loess are not well understood.
Key Innovation: Combining laboratory soil column experiments and numerical simulations to investigate coupled water–air migration under open and closed boundary conditions, highlighting the importance of pore-air dynamics in unsaturated loess and demonstrating that air pressure regulates infiltration rates.
56. Impacts of increasing extreme climate events on Muz Taw glacier, Central Asia
Core Problem: The impact of increasing extreme climate events on glacier ablation, particularly in regions of high latitude and low altitude, needs further investigation.
Key Innovation: Analysis of extreme climate events in the Muz Taw Glacier area using RClimDex model, ERA5-Land reanalysis, and remote sensing data, revealing rapid warming and increased precipitation intensity, contributing to accelerated glacier mass loss.
57. Stress-rate-controlled compression behavior of compacted snow
Core Problem: Understanding the compressive behavior of compacted snow under stress-controlled loading, which is more representative of real-world engineering scenarios than displacement-controlled loading.
Key Innovation: Identified unrecorded ductile-to-brittle transition under stress-rate loading and demonstrated the limited correspondence between displacement- and stress-rate-controlled regimes.
58. Wind tunnel test of non-uniform snow distribution on large-span suspended gable roofs with snow fences
Core Problem: Investigating snow distribution on large-span suspended gable roofs, which are sensitive to snow accumulation and prone to safety hazards under heavy snowfall.
Key Innovation: Demonstrated that snow fences, particularly 20 mm high, promote uniform snow distribution, reduce snow erosion, and enhance structural stability.
59. Stress corrosion behavior at steel pile–frozen clay and pile–ice interfaces and its nonlinear constitutive model
Core Problem: Understanding the shear behavior at pile-frozen soil and pile-ice interfaces, which governs the long-term deformation of pile foundations in permafrost regions.
Key Innovation: Established a viscoelastic-plastic constitutive model for the pile-frozen soil interface and elucidated distinctive characteristics in shear creep behavior compared to pile-ice interfaces.
60. Evolution of seepage characteristics in frozen-thawed sandstone: Insights from coupled PFC-COMSOL simulations
Core Problem: Investigating the impact of freeze-thaw cycles on the seepage characteristics of sandstone, which affects the stability of engineering structures in cold regions.
Key Innovation: Developed a novel cross-scale modeling approach using PFC-COMSOL coupling to reveal pore evolution under freeze-thaw and simulate complex infiltration in damaged rock.
61. Efficient simulation of landslide-induced surges and control effects of different position piles based on an improved flow-flow model
Core Problem: Simulating landslide-induced surges and their interaction with control structures efficiently.
Key Innovation: Improved two-layer 'flow-flow' model for simulating landslide surges and pile control, using a unified fluid framework and local mesh refinement.
62. Multi-objective optimization of green-grey infrastructure with comprehensive consideration of spatial layout and parameter configuration
Core Problem: Optimizing green-grey infrastructure for urban flood mitigation, considering both spatial layout and structural parameters.
Key Innovation: A multi-objective optimization framework coupling NSGA-II and SWMM to identify key parameters and synergistic combinations of green-grey infrastructure.
63. Thermo-hydro-mechanical modeling of root–soil interaction in unsaturated slopes
Core Problem: Vegetation effects on slope stability are often simplified, overlooking the progressive nature of root–soil interaction.
Key Innovation: Developed an extended Barcelona Basic Model (BBM-VEG) within a thermo-hydro-mechanical (THM) framework, introducing a strain-dependent reinforcement parameter linked to root mass fraction, dynamically modifying soil stiffness and strength.
64. Effects of moss cover patterns on hydrodynamic parameters and particle size selectivity during karst erosion under rock surface flow
Core Problem: Epilithic mosses in karst regions regulate rock surface flow, but their combined effect with slope moss on downstream soil erosion is not well understood.
Key Innovation: Quantified the joint effects of moss cover on rock and slope surfaces on erosion, showing that epilithic moss reduces erosion under moderate rainfall but exacerbates it under extreme rainfall.
65. Lagged streamflow depletion due to pumping-induced stream drying: Incorporation into analytical streamflow depletion estimation methods
Core Problem: Streamflow depletion caused by groundwater pumping is difficult to quantify from observational data and requires modeling.
Key Innovation: Developed an approach to incorporate stream drying into analytical depletion functions (ADFs) to improve their estimation of streamflow and streamflow depletion.
66. Thermo-hydro-mechanical modeling of root–soil interaction in unsaturated slopes
Core Problem: Vegetation effects on slope stability are often simplified, overlooking the progressive nature of root–soil interaction.
Key Innovation: Developed an extended Barcelona Basic Model (BBM-VEG) within a thermo-hydro-mechanical (THM) framework, introducing a strain-dependent reinforcement parameter linked to root mass fraction, dynamically modifying soil stiffness and strength.
67. Quantifying the scale effects on shallow foundation bearing capacity induced by shear band on sensitive marine clays
Core Problem: Mapping from small-scale model tests to prototype design remains challenging in geotechnics, primarily due to scale effects induced by progressive failure in sensitive marine clays.
Key Innovation: Investigates scale effects using a nonlocal Remeshing and Interpolation Technique with Small Strain (RITSS) method, incorporating a strain-softening constitutive model.
68. Uplift resistance mechanism of pipes in lightweight backfill material of ceramsite
Core Problem: Buried pipelines crossing active fault zones undergo significant seismic displacement, inducing substantial soil resistance that can damage the pipe structure.
Key Innovation: Investigates the suitability of ceramsite—a lightweight material characterized by its smooth surface and low density—as a novel backfill.
69. A hybrid finite-element material-point approach for anchored tunnels in large deformation
Core Problem: The stability of deep-buried tunnels undergoing large deformation critically depends on the interaction between rock masses and bolt reinforcement systems.
Key Innovation: Proposes a Hybrid Finite-Element Material-Point (HFEMP) method, where the surrounding rock is modeled using the Material Point Method (MPM), while the rock bolts are represented as axial-force-bearing bar elements within the Finite Element Method (FEM).
70. Multi-objective reliability-based design optimization of piles considering soil-structure interaction and soil spatial variability
Core Problem: Piles are vital foundation systems that require careful design optimization, but engineers often overlook soil uncertainties.
Key Innovation: Introduces a novel multi-objective reliability-based design optimization (MO-RBDO) framework for pile foundations, explicitly incorporating soil spatial variability.
71. A damage constitutive model for brittle rock considering compaction effect and energy dissipation characteristics
Core Problem: A comprehensive understanding of rock deformation and failure behaviors, along with brittleness characteristics under high-temperature conditions, is crucial for enhancing the fracturing efficiency in unconventional energy reservoirs and ensuring the stability of horizontal wellbores.
Key Innovation: Presents a novel damage constitutive model (CEED model) that incorporates thermal expansion, compaction effects, and energy evolution, offering a comprehensive framework for characterizing the whole stress–strain behavior of rock at the macroscale.
72. Thermo-hydro-mechanical modeling of root–soil interaction in unsaturated slopes
Core Problem: Vegetation effects on slope stability are often simplified, overlooking the progressive nature of root–soil interaction.
Key Innovation: Developed an extended Barcelona Basic Model (BBM-VEG) within a thermo-hydro-mechanical (THM) framework, introducing a strain-dependent reinforcement parameter linked to root mass fraction, dynamically modifying soil stiffness and strength.
73. Improved p-y curve modeling for large-diameter monopiles in asymmetric scour conditions in sandy soils
Core Problem: Scour is a critical factor affecting the bearing capacity of monopile foundations for offshore wind turbines (OWTs).
Key Innovation: Investigates the effects of geometric asymmetry of scour holes on the bearing capacity of large-diameter monopiles through the finite element method (FEM) in sandy soil.
74. A unified thermo-mechanical constitutive model for sand and clay based on hypoplasticity
Core Problem: Existing hypoplastic models treat sands and clays as different materials with distinct mathematical formulations.
Key Innovation: Develops a unified hypoplastic model that can describe the thermo-mechanical behavior of various soil types.
75. Dynamic seepage analysis of unsaturated subgrade bed for ballastless track considering rainfall and train loading
Core Problem: Long-term stability of ballastless tracks is affected by moisture within the subgrade, especially under rainfall and train loading.
Key Innovation: A coupled static–dynamic model for unsaturated seepage in the subgrade, validated through model tests, slope rainfall–infiltration simulations, and wheel-rail coupling analyses.
76. Shaking table test and energy dissipation mechanism of cable-seismic bolt-viscous damper composite energy dissipation system for wind turbine towers
Core Problem: Wind turbine towers need improved damping efficiency to withstand vibrations.
Key Innovation: A Cable–Seismic Bolt–Viscous Damper System (CSVDS) featuring a dual energy dissipation mechanism, validated through shaking table tests and cyclic loading tests.
77. Dynamic response characteristics of lateritic soil subgrade under combined action of wetting and vehicle loading: field test and numerical simulation
Core Problem: Dynamic response under traffic loading is the most direct influencing factor on the service performance of subgrade, especially with environmental factors.
Key Innovation: In-situ monitoring method for subgrade dynamic response under different moisture content is developed, and a prediction model between moisture content, vehicle speed, axle load, and dynamic response characteristics of the subgrade is established.
78. Dynamic approach-based assessment of debris flow susceptibility in the mountainous area of North China
Core Problem: Existing debris flow susceptibility models often lack dynamic capability, relying solely on static environmental factors, which limits their predictive performance for real-time risk mitigation.
Key Innovation: Development of a dynamic debris flow susceptibility model (CII-I) that integrates static environmental factors with hourly rainfall intensity, achieving improved predictive performance and applicability for real rainfall events.
79. 3D structural correlations to strength deterioration of sliding zone soil impacted by physicochemical interactions
Core Problem: Physicochemical interactions accelerate the internal structural degradation of reservoir landslides, potentially triggering catastrophic failures, thus necessitating the need to accurately link structural changes with mechanical deterioration within the sliding zone.
Key Innovation: This study investigates the combined effects of wet-dry cycles and chemical environments (acidic, saline, and distilled water) on the shear strength and three-dimensional (3D) structure of sliding zone soil through triaxial test and computed tomography (CT).
80. A comprehensive approach to predicting low-angle glacier detachment-induced river damming: Insights from the Zelunglung Glacier
Core Problem: Glacier detachments in cold, high-altitude regions typically involve large volumes and exhibit extremely high mobility due to the presence of ice, often leading to catastrophic events, and are expected to intensify as global warming continues. Existing studies provide valuable insights into post-event conditions, but predictive efforts based on remote sensing remain limited and often overlook internal glacier structures.
Key Innovation: This study presents an integrated approach combining remote sensing, geophysical exploration, finite element analysis, and a multiphase flow model to (a) reconstruct glacier geometry, (b) delineate potential detachment zones, and (c) simulate movement and damming hazards.
81. Liquefaction Characteristics and Damage Evolution of Rapid and Long-traveling Landslides
Core Problem: Rapid and long-traveling landslides can often pose severe threats to life and property due to their high velocities and large scales. Therefore, it is crucial to conduct further investigations into the causes of such landslides.
Key Innovation: In this study, the shear mechanical behavior and liquefaction mechanism of the Aranayaka landslide were investigated through undrained ring shear tests conducted under varying normal stresses. Moreover, a statistical damage constitutive model was developed using damage theory and the Weibull distribution function.
82. Machine learning-based assessment of three-dimensional slope stability using geometric features under heavy rainfall
Core Problem: Assessment of slope stability under rainfall at the regional scale remains a major challenge in disaster prevention. Traditional geographic information system (GIS)-based methods are efficient but oversimplify slope geometry, while precise numerical simulations are too computationally intensive for large-scale applications.
Key Innovation: This study applies machine learning (ML) to predict rainfall-induced slope stability at the regional scale using geometric features, including maximum and average slope gradients, surface undulation, and the frequency of undulations exceeding 10 m along the longitudinal direction of the slope, were used as input variables.
83. Projected increases in shoreline erosion and potential flooding risk along China's sandy coasts under a warming climate
Core Problem: Shoreline erosion and coastal flooding are major hazards, and their long-term assessment is needed for climate change adaptation.
Key Innovation: Integrated assessment of shoreline erosion and potential flooding risk along China's sandy beaches under SSP5-8.5, examining concurrent coastal hazards and exposure of assets and population.
84. Permeability Enhancement by Slow Faulting Under High Pore Fluid Pressure
Core Problem: Understanding how faulting mechanisms affect the permeability structure of fault zones.
Key Innovation: Quantifying the 3D pore distribution and permeability structures of rock samples deformed by slow and brittle faulting, showing higher porosity and permeability in fault cores formed by slow faulting.
85. Predicting the amplitude and runup of the water waves induced by rotational cliff collapse, considering fragmentation
Core Problem: The collapse of cliffs into small lakes and reservoirs induces powerful waves, threatening offshore infrastructure.
Key Innovation: Predicting the amplitude and runup of water waves induced by rotational cliff collapse, considering fragmentation.
86. Evaluation of highway collapse hazard susceptibility based on the coupling of information value model and multiple machine learning models
Core Problem: Highway collapse hazards pose severe challenges to transportation infrastructure, and accurate prediction is needed for risk mitigation.
Key Innovation: Developed coupled models (IV-RF, IV-XGBoost, IV-LGBM, and IV-CatBoost) by integrating an information value model with machine learning algorithms for highway collapse susceptibility mapping.
87. Landslide susceptibility mapping using ensemble learning integrating climate and seismic factors along the CPEC
Core Problem: Landslides are a major geohazard, and accurate landslide susceptibility mapping (LSM) is essential for disaster risk reduction, particularly under changing climatic and seismic conditions.
Key Innovation: Developed an integrated machine learning framework to predict landslide susceptibility, incorporating key climatic and seismic factors, with an ensemble model outperforming individual models.
88. Intelligent joint mapping and hazard areas of open-pit slopes under complex geology: the Yanshan iron mine case
Core Problem: Rapid identification of hazardous areas is crucial for reducing landslide risks in open-pit mines.
Key Innovation: Proposed a hazard assessment method based on UAV oblique photography and automated structural surface identification, combined with 3D stability analysis to identify high-risk areas and failure modes.
89. Ground deformation monitoring of landslides and precipitation-triggered mechanisms using GBInSAR
Core Problem: Landslides pose serious threats, and precipitation is a major triggering factor. Improved monitoring accuracy, spatiotemporal resolution, and early-warning capability are needed.
Key Innovation: Proposes an integrated landslide monitoring framework that combines satellite-borne InSAR, GB-InSAR, GNSS, and rain-gauge observations to improve monitoring accuracy and early warning.
90. Physical experimental study on the stability analysis of granite residual soil slope with boulder and dominant channel
Core Problem: Granite residual soil slopes with boulders and seepage channels are prone to instability under rainfall, requiring investigation of the failure mechanisms.
Key Innovation: Established a physical model testing system to analyze the stability characteristics and key influencing factors of granite residual soil slopes with boulders and dominant channels under rainfall conditions.
91. Evaluation of highway collapse hazard susceptibility based on the coupling of information value model and multiple machine learning models
Core Problem: Highway collapse hazards pose severe challenges to transportation infrastructure, and accurate prediction is needed for risk mitigation.
Key Innovation: Developed coupled models (IV-RF, IV-XGBoost, IV-LGBM, and IV-CatBoost) by integrating an information value model with machine learning algorithms for highway collapse susceptibility mapping.
92. Automated Recognition of Rock Mass Discontinuities on Vegetated High Slopes Using UAV Photogrammetry and an Improved Superpoint Transformer
Core Problem: Automated recognition of rock mass discontinuities in vegetated high-slope terrains remains a challenging task critical to geohazard assessment and slope stability analysis.
Key Innovation: Presents an integrated framework combining close-range UAV photogrammetry with an Improved Superpoint Transformer (ISPT) for semantic segmentation and structural characterization.
93. Remote Sensing, Vol. 18, Pages 309: Reading the Himalayan Treeline in 3D: Species Turnover and Structural Thresholds from UAV LiDAR
Core Problem: Fine-scale structural and species level dynamics of mountain treelines are poorly resolved in the Himalayas.
Key Innovation: UAV LiDAR is applied to quantify canopy structure and tree species distributions across a steep treeline ecotone in the Manang Valley of central Nepal.
94. Remote Sensing, Vol. 18, Pages 308: Localized Browning in Thermokarst-Dominated Landscapes Reverses Regional Greening Trends Under a Warming Climate in Northeastern Siberia
Core Problem: The role of thermokarst lake development in mediating vegetation-climate interactions remains poorly understood, particularly across heterogeneous landscapes of northeastern Siberia.
Key Innovation: Integration of multi-source remote sensing data to examine the drivers of NDVI variability across thermokarst lake coverage levels and vegetation types.