  Rapid assessment after a natural disaster is key for prioritizing emergency
resources. In the case of landslides, rapid assessment involves determining the
extent of the area affected and measuring the size and location of individual
landslides. Synthetic Aperture Radar (SAR) is an active remote sensing
technique that is unaffected by weather conditions. Deep Learning algorithms
can be applied to SAR data, but training them requires large labeled datasets.
In the case of landslides, these datasets are laborious to produce for
segmentation, and often they are not available for the specific region in which
the event occurred. Here, we study how deep learning algorithms for landslide
segmentation on SAR products can benefit from pretraining on a simpler task and
from data from different regions. The method we explore consists of two
training stages. First, we learn the task of identifying whether a SAR image
contains any landslides or not. Then, we learn to segment in a sparsely labeled
scenario where half of the data do not contain landslides. We test whether the
inclusion of feature embeddings derived from stage-1 helps with landslide
detection in stage-2. We find that it leads to minor improvements in the Area
Under the Precision-Recall Curve, but also to a significantly lower false
positive rate in areas without landslides and an improved estimate of the
average number of landslide pixels in a chip. A more accurate pixel count
allows to identify the most affected areas with higher confidence. This could
be valuable in rapid response scenarios where prioritization of resources at a
global scale is important. We make our code publicly available at
https://github.com/VMBoehm/SAR-landslide-detection-pretraining.
