巨袁臻, 许强, 金时超, 李为乐, 董秀军, 郭庆华. 使用深度学习方法实现黄土滑坡自动识别[J]. 武汉大学学报 ( 信息科学版), 2020, 45(11): 1747-1755. DOI: 10.13203/j.whugis20200132
引用本文: 巨袁臻, 许强, 金时超, 李为乐, 董秀军, 郭庆华. 使用深度学习方法实现黄土滑坡自动识别[J]. 武汉大学学报 ( 信息科学版), 2020, 45(11): 1747-1755. DOI: 10.13203/j.whugis20200132
JU Yuanzhen, XU Qiang, JIN Shichao, LI Weile, DONG Xiujun, GUO Qinghua. Automatic Object Detection of Loess Landslide Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1747-1755. DOI: 10.13203/j.whugis20200132
Citation: JU Yuanzhen, XU Qiang, JIN Shichao, LI Weile, DONG Xiujun, GUO Qinghua. Automatic Object Detection of Loess Landslide Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1747-1755. DOI: 10.13203/j.whugis20200132

使用深度学习方法实现黄土滑坡自动识别

Automatic Object Detection of Loess Landslide Based on Deep Learning

  • 摘要: 区域性滑坡识别是滑坡灾害风险管理的基础,传统的识别工作主要依靠人力完成。在已有的滑坡自动识别研究中,方法上以机器学习为主,数据源上对谷歌地球影像应用较少,识别对象上多以与环境差异较大的新滑坡为主。结合深度学习方法和谷歌地球影像数据对中国典型黄土地区历史滑坡进行自动识别。首先,基于开源谷歌地球影像建立了历史黄土滑坡样本数据库,包含黄土滑坡2 498处;然后,利用掩膜区域卷积神经网络(mask region-based convolutional networks,Mask R-CNN)目标检测模块进行黄土滑坡自动识别。识别的准确率为0.56,召回率为0.72,F1值为0.63。结果表明,Mask R-CNN是一种稳健性较好的方法,可以用于以谷歌地球影像为数据源的黄土滑坡自动识别,为快速准确地进行区域滑坡灾害调查提供了可能。

     

    Abstract:
      Objectives  The knowledge of regional landslides detection plays a fundamental role in the landslide risk management. However, most of that recognition was taken manually in the past, which is rather time- and labor- consuming. As the development of technologies of remote sensing and artificial intelligence, the automatic detection of landslides becomes possible. The previous researches relative to the automatic detection of landslides utilized the machine learning methods to detect these new landslides which were significantly distinguished from their context. Compared to those landslides, the detection of old loess landslides that are not distinct from their context is more challenged. We explore the deep learning to automatically detect the old loess landslides.
      Methods  Firstly, we build a loess landslide database consists of 2 498 which are interpreted from the Google Earth images by experts. Then, we divide the database into three datasets for training, validation and test. Finally, we train Mask R-CNN object detection module with the training dataset, choose the best model by the validation dataset, and apply the best model to the test dataset.
      Results  The test results of model performance show a precision of 0.56, a recall of 0.72, and a F1-score of 0.63.
      Conclusions  The results indicate that Mask R-CNN is a robust method even for the detection of loess landslides that are unapparent from the context, and deep learning can provide the possibility for rapid and accurate regional geo-hazard investigation.

     

/

返回文章
返回