  Predicting a landslide susceptibility map (LSM) is essential for risk
recognition and disaster prevention. Despite the successful application of
data-driven approaches for LSM prediction, most methods generally apply a
single global model to predict the LSM for an entire target region. However, in
large-scale areas with significant environmental change, various parts of the
region hold different landslide-inducing environments, and therefore, should be
predicted with respective models. This study first segmented target scenarios
into blocks for individual analysis. Then, the critical problem is that in each
block with limited samples, conducting training and testing a model is
impossible for a satisfactory LSM prediction, especially in dangerous
mountainous areas where landslide surveying is expensive. To solve the problem,
we trained an intermediate representation by the meta-learning paradigm, which
is superior for capturing information valuable for few-shot adaption from LSM
tasks. We hypothesized that there are more general and vital concepts
concerning landslide causes and are sensitive to variations in input features.
Thus, we can quickly few-shot adapt the models from the intermediate
representation for different blocks or even unseen tasks using very few
exemplar samples. Experimental results on the two study areas demonstrated the
validity of our block-wise analysis in large scenarios and revealed the top
few-shot adaption performances of the proposed methods.
