CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection
Description
In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.
If you use our data, please cite our work published in Scientific Data.
Xu, Y., Ouyang, C., Xu, Q. et al. CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection. Sci Data 11, 12 (2024). https://doi.org/10.1038/s41597-023-02847-z
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Additional details
Funding
- National Natural Science Foundation of China 42022054
- National Natural Science Foundation of China
- Youth Innovation Promotion Association of the Chinese Academy of Sciences Y201970
- Innovation Academy for Microsatellites of Chinese Academy of Sciences
- Sichuan Science and Technology Program 2022YFS0543, 2022YFG0140
- Science and Technology Department of Sichuan Province
- Strategy Priority Research Program XDA23090303
- Chinese Academy of Sciences
Dates
- Accepted
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2023-12