Bayesian Deep Learning Framework Integrating InSAR for Probabilistic Uplift, Subsidence, and Landslide Susceptibility Mapping

DN.shp files is training data as shapefile, and LAYERS_TO_TRAIN.tif is the rasterstack with covariables..... just modify the path of the code to train and predict, Consenseus is the shapefile with all risk areas (Cochabamba - Bolivia geohazard delimitation areas)

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-30
DOI
https://doi.org/10.5281/zenodo.16599144
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Bayesian Deep Learning Framework Integrating InSAR for Probabilistic Uplift, Subsidence, and Landslide Susceptibility Mapping

Zenodo (CERN European Organization for Nuclear Research)
Synthetic Aperture Radar (SAR) Applications and Techniques
article

Bayesian Deep Learning Framework Integrating InSAR for Probabilistic Uplift, Subsidence, and Landslide Susceptibility Mapping

article en

Abstract

DN.shp files is training data as shapefile, and LAYERS_TO_TRAIN.tif is the rasterstack with covariables..... just modify the path of the code to train and predict, Consenseus is the shapefile with all risk areas (Cochabamba - Bolivia geohazard delimitation areas)

Zenodo (CERN European Organization for Nuclear Research)
Beihang University (CN)
Climate action
Openalex Percentile: Top 44%
Synthetic Aperture Radar (SAR) Applications and Techniques
DOIOpen Access PDFOpenAlex
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Bayesian Deep Learning Framework Integrating InSAR for Probabilistic Uplift, Subsidence, and Landslide Susceptibility Mapping · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS