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CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection.

Yulin Xu1,2, Chaojun Ouyang3, Qingsong Xu4

  • 1Key laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610299, China.

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The CAS Landslide Dataset offers 20,865 images for deep learning landslide detection. This multisensor resource aims to improve recognition accuracy and speed, addressing limitations of current datasets.

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Area of Science:

  • Geosciences
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Landslide occurrences are increasing due to climate change and seismic activity.
  • Accurate and efficient landslide recognition is crucial for disaster management.
  • Existing datasets have limitations in size, coverage, sensor diversity, and resolution.

Purpose of the Study:

  • To introduce the CAS Landslide Dataset, a large-scale, multisensor resource for deep learning-based landslide detection.
  • To address the limitations of existing landslide datasets.
  • To provide a benchmark for developing and evaluating advanced landslide identification models.

Main Methods:

  • Compilation of 20,865 images from satellite and unmanned aerial vehicle (UAV) data.
  • Integration of data from nine distinct geographical regions.
  • Establishment of a robust quality evaluation methodology for dataset reliability.

Main Results:

  • The CAS Landslide Dataset provides a comprehensive and high-resolution resource exceeding the scope of previous datasets.
  • The dataset integrates diverse data sources, enhancing its applicability for various landslide detection scenarios.
  • A rigorous quality assessment ensures the dataset's reliability for research and development.

Conclusions:

  • The CAS Landslide Dataset serves as a valuable benchmark for advancing deep learning techniques in landslide identification.
  • It enables enhanced prediction, monitoring, and analysis capabilities for researchers.
  • The dataset facilitates the development of automated landslide detection systems, contributing to improved hazard mitigation.