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

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.When using this work, users are required to comply with the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).If you intend to utilize our dataset, kindly acknowledge our research by citing our work in Scientific Data.

本研究推出CAS滑坡数据集（CAS Landslide Dataset）——一款面向基于深度学习的滑坡检测任务的大规模多传感器数据集，由中国科学院（Chinese Academy of Sciences，CAS）山地灾害与环境研究所人工智能团队研发。该数据集旨在解决滑坡识别领域现存的诸多挑战。随着气候变化与地震活动加剧导致滑坡事件频发，业界对于精准且全面的数据集的需求日益增长，以支撑快速高效的滑坡识别工作。相较于现有数据集在规模、覆盖范围、传感器类型与分辨率上存在的局限，本CAS滑坡数据集共包含20865张图像，整合了来自9个区域的卫星与无人机数据。为保障数据集的可靠性与适用性，本研究构建了一套鲁棒的评估方法以开展数据集质量评估。本研究提议将该滑坡数据集作为滑坡识别模型构建的基准数据集，以推动深度学习技术的发展。研究人员可借助该数据集提升滑坡预测、监测与分析能力，进而推动自动化滑坡检测技术的进步。使用本研究成果的用户需遵守知识共享署名-非商业性使用4.0国际许可协议（Creative Commons Attribution-NonCommercial 4.0 International License，CC BY-NC 4.0）的相关条款。若您计划使用本数据集，请通过在《Scientific Data》期刊中引用本研究成果的方式对我们的工作予以认可。