Sentinel-2 Imagery for Detecting Slow-Moving Landslides via Surface Deformation

Datasets and code from an ESPL publication on automated satellite monitoring of surface deformation for landslide detection. The data is associated with research by Maximillian Van Wyk de Vries at the University of Oxford. It includes three zipped imagery folders and the full code in a .m file.

Use Cases

• Training machine learning models for landslide detection based on satellite-derived surface deformation.
• Validating automated monitoring algorithms using Sentinel-2 optical imagery.
• Studying the patterns and precursors of slow-moving landslides from satellite data.

Strengths

• Includes the full code for the published methodology in a .m file.
• Data is directly associated with a peer-reviewed ESPL publication.
• Imagery is provided in convenient zipped folders.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Oxford, associated with an ESPL publication.

Collection Method

Likely gathered from Sentinel-2 optical satellite imagery.