Landslide Susceptibility Mapping

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Occupancy Modelling of Biased Landslide Inventories: When Does Separating Occurrence from Recording Improve on Neutralisation?
Nov 9, 2026Abdirashid Omar, Abdikadir UgaasLandslide Susceptibility Mapping
EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models
Sep 1, 2026Yuanchao Su, Lianru Gao, Mengying Jiang +3Open-Set Recognition Landslide Susceptibility Mapping
Department of Computer and Information Science, University of Macau, Macao 999078, China · Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China · College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, China
Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping
Aug 25, 2026Yusen Cheng, Lei Fan, Qinfeng Zhu +3Spatial Context Modeling Landslide Susceptibility Mapping
Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China · Department of Geography and Planning, University of Liverpool, Liverpool, L69 3BX, UK · Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK +2
SAGE-XGBoost: Spatially Augmented Graph Embeddings--Machine Learning Framework for Natural Hazards Susceptibility Mapping under Data Scarcity
Aug 20, 2026Mohammad H. Vahidnia, Ali PourkarimiGeospatial Representation Learning Landslide Susceptibility Mapping
Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods
Aug 12, 2026Matthew Kahn, Milan Arjel, Nirmala Adhikari +2Event Time Prediction Landslide Susceptibility Mapping
Applied Economics and Management, Cornell University, New York, United States of America · Department of Electronics and Computer Engineering, Purwanchal Campus, Institute of Engineering, Tribhuvan University, Dharan, Nepal · Department of Physics, University of Alabama at Birmingham, Alabama, United States of America +2
Spatial Heterogeneity-Aware Multi-Hazard Susceptibility and Risk Mapping at Regional Scale
Aug 8, 2026Aswathi Mundayatt, Siddharth Anil, Hitanshu Seth +1Spatial Generalization Landslide Susceptibility Mapping
Graphics-Visualization-Computing Lab, International Institute of Information Technology Bangalore, Karnataka 560100, India
Learning Displacement-Robust Representations for Landslide Early Warning under Rainfall Forecast Uncertainty
May 17, 2026Ren Ozeki, Hamada Rizk, Hirozumi YamaguchiDistribution Shift Robustness Contrastive Learning
Osaka University, Suita, Japan · Japan Meteorological Agency · RIKEN Center for Computational Science, Kobe, Japan +1
Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
Apr 28, 2026Yuting Yang, Gang Mei, Feng Chen +2Knowledge-Guided ML Landslide Susceptibility Mapping
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China · School of Geological Engineering and Geomatics, Chang’an University, Xi’an, 710064, China
Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model
Apr 28, 2026Kaixuan Shao, Gang Mei, Yinghan Wu +2Class-Imbalanced Learning Synthetic Tabular Data Generation
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China · School of Geological Engineering and Geomatics, Chang’an University, 710064, Xi’an, China
FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale
Apr 17, 2026Aswathi Mundayatt, Jaya Sreevalsan-NairLandslide Susceptibility Mapping Mixture-of-Experts Models
Graphics-Visualization-Computing Lab, International Institute of Information Technology Bangalore, Karnataka 560100, India.
From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning
Jan 12, 2026Yusen Cheng, Qinfeng Zhu, Lei FanGeospatial Foundation Models Geospatial Representation Learning
Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China · Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

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Occupancy Modelling of Biased Landslide Inventories: When Does Separating Occurrence from Recording Improve on Neutralisation?

Abdirashid Omar, Abdikadir Ugaas

Landslide Susceptibility Mapping

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Landslide susceptibility models are trained on inventories that record where landslides were seen, not where they occurred. The established remedy fits bias-describing variables and neutralises them when mapping. We adapt occupancy-detection modelling from ecology, treating several inventories as imperfect observers of one event and separating occurrence from recording, and ask when this improves on neutralisation. We use near-complete inventories of the 2018 Hokkaido Eastern Iburi, 2015 Gorkha and 2010 Haiti earthquakes with simulated reporting bias, and three real Gorkha inventories. Compared with standard practice, the occupancy model reduces road bias in 24 of 25 paired draws and raises AUC by 0.08-0.14 on Gorkha and 0.03 on Haiti, but only 0.01-0.02 on Iburi. Its median predicted-to-true landslide rate is 0.99 (range 0.44-1.89), against 0.24-0.30 for the baselines. Rescaling the baselines with the same anchor area corrects their median but leaves larger errors (range 0.20-4.34). Cell-level probabilities remain too extreme (calibration slope 0.59). Compared with the best neutralised model, AUC is higher on Iburi (by 0.03-0.07) and lower on Gorkha and Haiti (by 0.01-0.05). Transplant experiments tie the Iburi advantage to the structure of its true landslide pattern, not to its terrain or road network. In all eight settings with a low interaction gap, computable from biased records, neutralisation was at least as accurate, including a prespecified test on Haiti; a high gap did not guarantee an advantage. The real Gorkha inventories are slope-biased, not road-biased, and there the model brings no meaningful gain. Report-based catalogues remain the main untested case.