This study introduces \textit{Landslide4Sense}, a reference benchmark for landslide detection from remote sensing. The repository features 3,799 image patches fusing optical layers from Sentinel-2 sensors with the digital elevation model and slope layer derived from ALOS PALSAR. The added topographical information facilitates an accurate detection of landslide borders, which recent researches have shown to be challenging using optical data alone. The extensive data set supports deep learning (DL) studies in landslide detection and the development and validation of methods for the systematic update of landslide inventories. The benchmark data set has been collected at four different times and geographical locations: Iburi (September 2018), Kodagu (August 2018), Gorkha (April 2015), and Taiwan (August 2009). Each image pixel is labelled as belonging to a landslide or not, incorporating various sources and thorough manual annotation. We then evaluate the landslide detection performance of 11 state-of-the-art DL segmentation models: U-Net, ResU-Net, PSPNet, ContextNet, DeepLab-v2, DeepLab-v3+, FCN-8s, LinkNet, FRRN-A, FRRN-B, and SQNet. All models were trained from scratch on patches from one quarter of each study area and tested on independent patches from the other three quarters. Our experiments demonstrate that ResU-Net outperformed the other models for the landslide detection task. We make the multi-source landslide benchmark data (Landslide4Sense) and the tested DL models publicly available at \url{www.landslide4sense.org}, establishing an important resource for remote sensing, computer vision, and machine learning communities in studies of image classification in general and applications to landslide detection in particular.

翻译：本研究引入了从遥感中探测滑坡的参考基准 。 存放处有3 799个图像补丁, 由Sentinel-2传感器和ALOS PALSAR产生的数字升降模型和斜坡层组成。 添加的地形信息有助于准确探测滑坡边界, 最近的研究显示, 仅使用光学数据就具有挑战性。 广泛的数据集支持了在山崩探测方面的深层次学习( DL)研究, 以及开发和验证系统更新滑坡清单的方法。 基准数据集是在四个不同的时间和地理位置上收集的: Iburi (2018年9月)、Kodagu (2018年8月)、Gorkha (2015年4月)和台湾 (2009年8月)。 每个图像被标为属于滑坡或不属于滑坡的, 包含各种来源和完整的手动注释。 然后, 我们评估了11个州级的DLSl=S-read 图像模型、 ResU-Net、 PSPNet、 CeepNet、 DeepLab- 8Net 和 Descrideal-Reval Ex数据库的另外三处的Seral- Rev+ 数据库数据库数据库数据库数据库的Serleval 数据库中, 的三部的Silder 数据库的Silding 和Sild