  In this letter, we use deep-learning convolution neural networks (CNNs) to
assess the landslide mapping and classification performances on optical images
(from Sentinel-2) and SAR images (from Sentinel-1). The training and test zones
used to independently evaluate the performance of the CNNs on different
datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at
03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered
about 8000 coseismic landslides. We analyzed the conditions before and after
the earthquake exploiting multi-polarization SAR as well as optical data by
means of a CNN implemented in TensorFlow that points out the locations where
the Landslide class is predicted as more likely. As expected, the CNN run on
optical images proved itself excellent for the landslide detection task,
achieving an overall accuracy of 99.20% while CNNs based on the combination of
ground range detected (GRD) SAR data reached overall accuracies beyond 94%. Our
findings show that the integrated use of SAR data may also allow for rapid
mapping even during storms and under dense cloud cover and seems to provide
comparable accuracy to classical optical change detection in landslide
recognition and mapping.
