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Updated: Jan 11, 2026

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A Spatio-Temporal Dataset for Satellite-Based Landslide Detection.

Paul Höhn1,2, Konrad Heidler3, Robert Behling4

  • 1Data Science in Earth Observation, Technical University of Munich (TUM), 80333, Munich, Germany. paul.hoehn@dlr.de.

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|November 11, 2025
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Summary
This summary is machine-generated.

A new dataset, Sen12Landslides, enhances landslide monitoring by integrating multi-modal, multi-temporal satellite data. This resource improves deep learning models for accurate landslide detection and spatio-temporal anomaly analysis.

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Area of Science:

  • Earth Observation
  • Geohazard Monitoring
  • Deep Learning

Background:

  • Accurate landslide detection and monitoring are crucial for risk reduction.
  • Current deep learning models struggle with temporal dynamics in satellite imagery for landslide analysis.

Purpose of the Study:

  • Introduce Sen12Landslides, a large-scale, multi-modal, multi-temporal dataset for satellite-based landslide monitoring.
  • Enable robust spatio-temporal anomaly detection in landslide events.

Main Methods:

  • Developed Sen12Landslides dataset with 75,000 annotations across 15 regions.
  • Integrated Sentinel-1 SAR, Sentinel-2 optical imagery, and Copernicus DEM data.
  • Provided pixel-level annotations with precise pre- and post-event timestamps.

Main Results:

  • Dataset supports advanced deep learning for capturing spatial and temporal landslide features.
  • Benchmark experiments showed model utility, with an F1-score over 83% on Sentinel-2 data.
  • Demonstrated improved model training and generalization across diverse regions.

Conclusions:

  • Sen12Landslides facilitates more reliable landslide detection and monitoring.
  • The dataset advances Earth observation research in geohazard assessment.
  • Enables development of more robust and generalizable deep learning models for landslide analysis.