用新网络自动识别遥感图像中的滑坡，精度超93%。
RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images
- 设计残差多头注意力U-Net架构，融合多尺度特征
- 在三个数据集上检测F1最高达98.23%，分割mIoU达76.88%
- 适合灾害监测、地理信息与遥感应用开发者
近年来，干旱、洪水、风暴等极端天气事件或滥伐森林、过度开发资源等人造活动导致滑坡灾害频发。然而，由于观测区域广、地形崎岖（如山地或高原），实现滑坡的自动监测极具挑战。为此，我们提出一种基于深度学习的端到端模型，利用遥感图像实现滑坡的自动检测与分割。通过将遥感图像作为输入，可低成本、大范围、持续地监测复杂地形。我们构建了一种新型神经网络架构，同时支持滑坡检测与分割任务。在LandSlide4Sense、Bijie和Nepal三个基准数据集上进行大量实验，检测任务F1得分分别为98.23（LandSlide4Sense）、93.83（Bijie）；分割任务mIoU得分分别为63.74（LandSlide4Sense）、76.88（Nepal）。结果表明该模型具备集成至实际滑坡监测系统的潜力。
原文摘要 · Abstract (English)
In recent years, landslide disasters have reported frequently due to the extreme weather events of droughts, floods , storms, or the consequence of human activities such as deforestation, excessive exploitation of natural resources. However, automatically observing landslide is challenging due to the extremely large observing area and the rugged topography such as mountain or highland. This motivates us to propose an end-to-end deep-learning-based model which explores the remote sensing images for automatically observing landslide events. By considering remote sensing images as the input data, we can obtain free resource, observe large and rough terrains by time. To explore the remote sensing images, we proposed a novel neural network architecture which is for two tasks of landslide detection and landslide segmentation. We evaluated our proposed model on three different benchmark datasets of LandSlide4Sense, Bijie, and Nepal. By conducting extensive experiments, we achieve F1 scores of 98.23, 93.83 for the landslide detection task on LandSlide4Sense, Bijie datasets; mIoU scores of 63.74, 76.88 on the segmentation tasks regarding LandSlide4Sense, Nepal datasets. These experimental results prove potential to integrate our proposed model into real-life landslide observation systems.
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