Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

中文翻译：

---

MILL：用于滑坡识别的基于通道注意的深度多实例学习

滑坡识别广泛应用于自然灾害风险管理。传统的滑坡识别主要由地质学家进行，识别准确但效率低下。本文介绍了多实例学习 (MIL) 来执行自动滑坡识别。提出了一种端到端的深度卷积神经网络，称为基于多实例学习的滑坡分类（MILL）。首先，MILL 使用大规模遥感图像分类数据集构建滑坡特征提取的预训练网络。其次，MILL 提取实例并分配实例标签，无需像素级注释。第三，MILL 使用新的基于通道注意力的 MIL 池化功能将实例级标签映射到袋级标签。我们应用 MIL 来检测黄土地区的滑坡。