Landslides and related hazards

Últimos artículos

• EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models

  Yuanchao Su, Lianru Gao, Mengying Jiang, Jiangyi Chen, Jiaxin Cheng, Yicong Zhou · 2 de septiembre de 2026

  Landslides are widespread geological hazards, yet their automated detection and mapping in remote sensing imagery remain challenging because of their irregular morphology, ambiguous spectral signatures, and substantial domain shifts across imaging platforms. To overcome these challenges, we propose …

• Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping

  Yusen Cheng, Lei Fan, Qinfeng Zhu, Cheng Zhang, Yangyang Li, Ron Mahabir · 27 de agosto de 2026

  Data-driven methods are widely used in landslide susceptibility mapping (LSM) because they can effectively model the complex relationships between landslides and geo-environmental conditions. Existing data-driven approaches generally follow two types of data representations. Pixel-based models focus…

• SAGE-XGBoost: Spatially Augmented Graph Embeddings--Machine Learning Framework for Natural Hazards Susceptibility Mapping under Data Scarcity

  Mohammad H. Vahidnia, Ali Pourkarimi · 21 de agosto de 2026

  Natural hazard susceptibility mapping is often constrained by limited labeled data, reducing the generalizability of conventional machine learning and limiting the applicability of complex deep learning models. This study proposes SAGE (Spatially Augmented Graph Embeddings), a structurally informed …

• Probabilistic indirect models for undrained shear strength: addressing significant data missing and variability with advanced imputation and machine learning techniques

  Haibin Xiong, Shaoheng Dai, Peng Lan, Xuzhen He, Chenxi Tong, Sheng Zhang, Daichao Sheng · 17 de agosto de 2026

  Accurate prediction of undrained shear strength (su) is crucial for geotechnical design, but is often hampered by substantial uncertainty in traditional empirical methods. This study uses the CLAY/10/7490 global database to develop probabilistic indirect models to predict su based on Atterberg limit…

• Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods

  Matthew Kahn, Milan Arjel, Nirmala Adhikari, Mingmar Sherpa, James Pope · 14 de agosto de 2026

  Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress. A companion feasibility study found that deformation indicates w…

• Spatial Heterogeneity-Aware Multi-Hazard Susceptibility and Risk Mapping at Regional Scale

  Aswathi Mundayatt, Siddharth Anil, Hitanshu Seth, Jaya Sreevalsan-Nair · 11 de agosto de 2026

  Floods and landslides often co-occur, but their relationships with environmental controls vary spatially. This study develops a spatial heterogeneity-aware framework for flood-landslide susceptibility and relative-risk mapping in Kerala, India, and Nepal. It combines 15 km x 15 km grid cells with re…

• GeoPhysAdapter: Scale-Matched Geophysical Adaptation for Cross-Domain Landslide Mapping with Vision Foundation Models

  Zhihang Liu, Mei-Po Kwan, Jinlin Wu, Hao Li · 11 de agosto de 2026

  Newly triggered landslides rarely carry immediate annotations, so cross-domain transferability determines the value of landslide mapping for emergency response and regional risk assessment. Vision foundation models have strengthened representational transfer, yet on unseen regions, events, and data …

• Hybrid AI-Physical Modeling for Penetration Bias Correction in X-band InSAR DEMs: A Greenland Case Study

  Islam Mansour, Georg Fischer, Ronny Haensch, Irena Hajnsek · 28 de julio de 2026

  Digital elevation models derived from Interferometric Synthetic Aperture Radar (InSAR) data over glacial and snow-covered regions often exhibit systematic elevation errors, commonly termed "penetration bias." We leverage existing physics-based models and propose an integrated correction framework th…

• A UAV-Based Multi-Modal Vision System for Automated Sideslope Deformation Monitoring and Hazard Detection

  Jingfeng Zhang, Yi Li, Xianchong Liang, Huan Yang · 23 de junio de 2026

  Slope hazards constitute a major safety threat to expressway infrastructure, and their evolution is typically manifested as slow surface deformation. Conventional manual inspection suffers from low efficiency and inadequate operational safety, especially on severely deteriorated slopes. Accordingly,…

• LandslideAgent with Multimodal LandslideBench: A Domain-Rule-Augmented Agent for Autonomous Landslide Identification and Analysis

  Chengfu Liu, Dongyang Hou, Junwu Xiang, Cheng Yang, Xuezhi Cui, Zeyuan Wang, Liangtian Liu, Zelang Miao · 18 de junio de 2026

  Intelligent landslide hazard interpretation is critical for disaster prevention, yet current paradigms struggle to simultaneously extract visual features and high-level geoscientific semantics, while general-purpose vision-language models (VLMs) suffer from perceptual limitations and domain hallucin…

• Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection

  Huong Binh Vu · 16 de junio de 2026

  Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance. This study evaluates whether Clay v1.5, a Geospatial Foundation Model (GFM), can improve pixel-level landslide segmentation on the Landslide4Sense (L4S) benchmark, …

• Clay-CNN Hybrids: Leveraging Geo-Foundational Models as Auxiliary Context for Landslide Detection

  Huong Binh Vu · 15 de junio de 2026

  Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance. This study evaluates whether Clay v1.5, a Geo-Foundational Model (GFM), can improve pixel-level landslide segmentation on the Landslide4Sense (L4S) benchmark, which…

• Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Catastrophic Slope Failure

  Yuansan Liu, James Bailey, Antoinette Tordesillas · 4 de junio de 2026

  Local Intrinsic Dimensionality (LID) has shown strong potential for anomaly detection in high-dimensional data, including landslide failure detection in granular media, where early and accurate identification of failure zones is crucial for effective geohazard mitigation. However, this task is still…

• Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions

  Rahul A. Burange, Harsh K. Shinde, Omkar Mutyalwar · 20 de mayo de 2026

  Landslides pose severe threats to infrastructure, economies, and human lives, necessitating accurate detection and predictive mapping across diverse geographic regions. With advancements in deep learning and remote sensing, automated landslide detection has become increasingly effective. This study …

• Learning Displacement-Robust Representations for Landslide Early Warning under Rainfall Forecast Uncertainty

  Ren Ozeki, Hamada Rizk, Hirozumi Yamaguchi · 19 de mayo de 2026

  Rainfall-induced landslides pose a growing risk worldwide as climate change intensifies extreme rainfall events. To provide sufficient evacuation time, landslide early warning systems (LEWS) for real-time disaster monitoring must estimate near-future landslide risk by integrating observed rainfall w…

• Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model

  Yuting Yang, Gang Mei, Feng Chen, Yongshuang Zhang, Jianbing Peng · 29 de abril de 2026

  Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive accuracy but require sufficient conditioning factors and large-scale landslide inventories. However, in practical engineering applications across m…

• Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model

  Kaixuan Shao, Gang Mei, Yinghan Wu, Nengxiong Xu, Jianbing Peng · 29 de abril de 2026

  Landslide investigation relies on sufficient and well-balanced observational data influenced by geological, hydrological, and anthropogenic factors. Available landslide inventories are often sparse and imbalanced, which limits understanding of triggering conditions and failure mechanisms. Data gener…

• A Benchmark Study of Segmentation Models and Adaptation Strategies for Landslide Detection from Satellite Imagery

  Md Kowsher, Weiwei Zhan, Chen Chen · 21 de abril de 2026

  Landslide detection from high resolution satellite imagery is a critical task for disaster response and risk assessment, yet the relative effectiveness of modern segmentation architectures and finetuning strategies for this problem remains insufficiently understood. In this work, we present a system…

• Multi-Modal Landslide Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Multi-Encoder Vision Transformers and Ensemble Learning

  Ioannis Nasios · 8 de abril de 2026

  Landslides represent a major geohazard with severe impacts on human life, infrastructure, and ecosystems, underscoring the need for accurate and timely detection approaches to support disaster risk reduction. This study proposes a modular, multi-model framework that fuses Sentinel-2 optical imagery …

• Large-Scale Avalanche Mapping from SAR Images with Deep Learning-based Change Detection

  Mattia Gatti, Alberto Mariani, Ignazio Gallo, Fabiano Monti · 25 de marzo de 2026

  Accurate change detection from satellite imagery is essential for monitoring rapid mass-movement hazards such as snow avalanches, which increasingly threaten human life, infrastructure, and ecosystems due to their rising frequency and intensity. This study presents a systematic investigation of larg…

• Decadal sink-source shifts of forest aboveground carbon since 1988

  Zhen Qian, Sebastian Bathiany, Teng Liu, Lana L. Blaschke, Hoong Chen Teo, Niklas Boers · 19 de marzo de 2026

  Forest ecosystems are vital to the global carbon cycle, yet their long-term aboveground carbon (AGC) dynamics remain uncertain. Here, we integrate multi-source satellite observations with probabilistic deep learning models to reconstruct a harmonized, uncertainty-aware global forest AGC record from …

• Predicting the post-wildfire mudflow onset using machine learning models on multi-parameter experimental data

  Mahta Movasat, Ingrid Tomac · 13 de febrero de 2026

  Post-wildfire mudflows are increasingly hazardous due to the prevalence of wildfires, including those on the wildland-urban interface. Upon burning, soil on the surface or immediately beneath becomes hydrophobic, a phenomenon that occurs predominantly on sand-based hillslopes. Rainwater and eroded s…

• From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning

  Yusen Cheng, Qinfeng Zhu, Lei Fan · 13 de enero de 2026

  Data-driven landslide susceptibility mapping (LSM) typically relies on landslide conditioning factors (LCFs), whose availability, heterogeneity, and preprocessing-related uncertainties can constrain mapping reliability. Recently, Google AlphaEarth (AE) embeddings, derived from multi-source geospatia…

• Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Complex Catastrophic Slope Failure

  Yuansan Liu, Antoinette Tordesillas, James Bailey · 8 de enero de 2026

  Local Intrinsic Dimensionality (LID) has shown strong potential for identifying anomalies and outliers in high-dimensional data across a wide range of real-world applications, including landslide failure detection in granular media. Early and accurate identification of failure zones in landslide-pro…

• Neural emulation of gravity-driven geohazard runout

  Lorenzo Nava, Ye Chen, Maximillian Van Wyk de Vries · 19 de diciembre de 2025

  Predicting geohazard runout is critical for protecting lives, infrastructure and ecosystems. Rapid mass flows, including landslides and avalanches, cause several thousand deaths across a wide range of environments, often travelling many kilometres from their source. The wide range of source conditio…