  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 the 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{https://www.iarai.ac.at/landslide4sense}, 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.
