CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection
Abstract 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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02847-z |
| _version_ | 1797363647117262848 |
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| author | Yulin Xu Chaojun Ouyang Qingsong Xu Dongpo Wang Bo Zhao Yutao Luo |
| author_facet | Yulin Xu Chaojun Ouyang Qingsong Xu Dongpo Wang Bo Zhao Yutao Luo |
| author_sort | Yulin Xu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-08T16:24:05Z |
| format | Article |
| id | doaj.art-326c604614234ac6aabb17eeac334a9f |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-08T16:24:05Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-326c604614234ac6aabb17eeac334a9f2024-01-07T12:10:27ZengNature PortfolioScientific Data2052-44632024-01-0111111110.1038/s41597-023-02847-zCAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide DetectionYulin Xu0Chaojun Ouyang1Qingsong Xu2Dongpo Wang3Bo Zhao4Yutao Luo5Key laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of SciencesKey laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of SciencesData Science in Earth Observation, Technical University of MunichState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of TechnologyKey laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of SciencesKey laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of SciencesAbstract 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.https://doi.org/10.1038/s41597-023-02847-z |
| spellingShingle | Yulin Xu Chaojun Ouyang Qingsong Xu Dongpo Wang Bo Zhao Yutao Luo CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection Scientific Data |
| title | CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection |
| title_full | CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection |
| title_fullStr | CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection |
| title_full_unstemmed | CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection |
| title_short | CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection |
| title_sort | cas landslide dataset a large scale and multisensor dataset for deep learning based landslide detection |
| url | https://doi.org/10.1038/s41597-023-02847-z |
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