基于深度学习的黄土滑坡自动识别

Automatic Recognition of Loess Landslide Based on Deep Learning

对黄土滑坡的自动识别可以有效地帮助滑坡灾害的风险管理。传统的滑坡识别方法主要依赖人工操作,效率较低。为此,探索深度学习方法,利用遥感图像进行黄土滑坡的自动识别。采用谷歌地图开源数据集,并使用深度学习模型来实现滑坡的自动检测。实验结果表明,所提的方法在准确率和召回率上取得显著提高,为黄土滑坡的自动识别提供了有力支持。

The automatic identification of loess landslide is an important work,which can effectively help the risk management of landslide disaster.Traditional landslide identification methods mainly rely on manual operation,and the efficiency is low.Therefore,this paper explores deep learning method and uses remote sensing image to automatically identify loess landslide.Using Google maps open source dataset and using deep learning model to achieve automatic detection of landslides.The experimental results show that the proposed method can significantly improve the accuracy and recall rate,which provides strong support for the automatic recognition of loess landslides.