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Uncertainty Analysis of Non-Landslide Sample Selection in Landslide Susceptibility Prediction Using Slope Unit-Based Machine Learning Models
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Training Sample Selection Method Based on Model-Data-Driven Maritime Radar Target Detection
inferred← citeshttps://doi.org/10.1109/jstars.2024.3370218openalex-snapshot
A Deep Neural Network Framework for Landslide Susceptibility Mapping by Considering Time-Series Rainfall
inferred← citeshttps://doi.org/10.1109/jstars.2024.3522995openalex-snapshot
Mapping Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China
inferred← citeshttps://doi.org/10.1109/jstars.2025.3541638openalex-snapshot
A Synergistic CNN-DF Method for Landslide Susceptibility Assessment
inferred← citeshttps://doi.org/10.1109/jstars.2026.3656940openalex-snapshot
Edge-Aware Superpixel Dual-Graph GCN for Topographically Heterogeneous Landslide Susceptibility Assessment
inferred← citeshttps://doi.org/10.1109/tai.2025.3543822openalex-snapshot
Adaptive Multiparticle Swarm Neural Architecture Search for High-Incidence Cancer Prediction
inferred← citeshttps://doi.org/10.1111/tgis.70255openalex-snapshot
Spatial Prediction of Landslide Susceptibility Using a Deep Learning and Partition Membership Hybrid Model
inferred← citeshttps://doi.org/10.15625/2615-9783/20706openalex-snapshot
Assessing the relationship between landslide susceptibility and land cover change using machine learning
inferred← citeshttps://doi.org/10.17268/sci.agropecu.2024.025openalex-snapshot
Influence of high Andean grasslands on landslide reduction in Peru
inferred← citeshttps://doi.org/10.21203/rs.3.rs-5684743/v1openalex-snapshot
Advanced Landslide Early Warning System Based on a Semi-supervised Model in Highly Urbanized Areas across China's Greater Bay Area
inferred← citeshttps://doi.org/10.2139/ssrn.4950059openalex-snapshot
Xgboost and Shap-Based Precipitation Threshold Identification for Hazard Assessment Slope Unit
inferred← citeshttps://doi.org/10.2174/0122103279381613250707181318openalex-snapshot
Geospatial Deep Learning Model for Early Landslide Prediction Using Multispectral Remote Sensing Data
inferred← citeshttps://doi.org/10.3389/feart.2024.1516615openalex-snapshot
Spatial distribution prediction of landslide susceptibility based on integrated particle swarm optimization
inferred← citeshttps://doi.org/10.3389/feart.2024.1519771openalex-snapshot
A comparative study of intelligent prediction models for landslide susceptibility: random forest and support vector machine
inferred← citeshttps://doi.org/10.3390/app15041843openalex-snapshot
Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
inferred← citeshttps://doi.org/10.3390/app15148068openalex-snapshot
Regional Landslide Hazard and Risk Assessment Considering Landslide Spatial Aggregation and Hydrological Slope Units
inferred← citeshttps://doi.org/10.3390/app152011242openalex-snapshot
Ecoregion-Based Landslide Susceptibility Mapping: A Spatially Partitioned Modeling Strategy for Oregon, USA
inferred← citeshttps://doi.org/10.3390/atmos17080771openalex-snapshot
Hydrometeorological Control Sampling for Rainfall Induced Landslide Susceptibility Modelling Using Multi Source Data and Advanced Learning
inferred← citeshttps://doi.org/10.3390/fractalfract10060413openalex-snapshot
Fractal-Based Characterization of Topographic Features to Enhance AI-Driven Landslide Susceptibility Mapping
inferred← citeshttps://doi.org/10.3390/geosciences16050188openalex-snapshot
Multi-Scale Attention Network for Landslide Susceptibility Assessment
inferred← citeshttps://doi.org/10.3390/geosciences16090352openalex-snapshot
Rainfall-Induced Landslide Hazard Assessment Considering Multi-Temporal-Scale Rainfall Factors in Jiangwan Town, Shaoguan, Guangdong Province, China
inferred← citeshttps://doi.org/10.3390/ijgi13090306openalex-snapshot
Landslide Recognition Based on Machine Learning Considering Terrain Feature Fusion
inferred← citeshttps://doi.org/10.3390/land12061125openalex-snapshot
Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization
inferred← citeshttps://doi.org/10.3390/land14010034openalex-snapshot
Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples
inferred← citeshttps://doi.org/10.3390/land14071472openalex-snapshot
Application of Semi-Supervised Clustering with Membership Information and Deep Learning in Landslide Susceptibility Assessment
inferred← citeshttps://doi.org/10.3390/rs15133345openalex-snapshot
Enhanced Absence Sampling Technique for Data-Driven Landslide Susceptibility Mapping: A Case Study in Songyang County, China
inferred← citeshttps://doi.org/10.3390/rs15215132openalex-snapshot
Experimental Investigation on Fragmentation Identification in Loose Slope Landslides by Infrared Emissivity Variability Features
inferred← citeshttps://doi.org/10.3390/rs16060988openalex-snapshot
Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan
inferred← citeshttps://doi.org/10.3390/rs16183374openalex-snapshot
Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review
inferred← citeshttps://doi.org/10.3390/rs17030381openalex-snapshot
Landslide Susceptibility Mapping Considering Landslide Spatial Aggregation Using the Dual-Frequency Ratio Method: A Case Study on the Middle Reaches of the Tarim River Basin
inferred← citeshttps://doi.org/10.3390/rs18091374openalex-snapshot
MPES-YOLO: A Multi-Scale Lightweight Framework with Selective Edge Enhancement for Loess Landslide Detection
inferred← citeshttps://doi.org/10.3390/rs18111854openalex-snapshot
Landslide Mapping and Susceptibility Assessment in the Middle and Lower Reaches of the Nujiang River (2017–2025) Using Satellite Embedding and Multidimensional Environmental Factors
inferred← citeshttps://doi.org/10.3390/rs18152437openalex-snapshot
Task-Specific Negative Sample Selection for Multi-Hazard Susceptibility Mapping of Slope-Instability Hazards
inferred← citeshttps://doi.org/10.3390/s25082504openalex-snapshot
Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection
inferred← citeshttps://doi.org/10.3390/su18189288openalex-snapshot
Hybrid Ensemble Machine Learning for Landslide Susceptibility Mapping and Management Planning in the Kamchik Pass, Uzbekistan
inferred← citeshttps://doi.org/10.48084/etasr.14523openalex-snapshot
Improving Landslide Susceptibility Mapping via Non-Landslide Sampling Strategies
inferred← citeshttps://doi.org/10.5194/nhess-26-611-2026openalex-snapshot
From typhoon rainfall to slope failure: optimizing susceptibility models and dynamic thresholds for landslide warnings in Zixing City, China
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