Meta-learning an Intermediate Representation for Few-shot Block-wise
Prediction of Landslide Susceptibility
- URL: http://arxiv.org/abs/2110.04922v1
- Date: Sun, 3 Oct 2021 05:40:50 GMT
- Title: Meta-learning an Intermediate Representation for Few-shot Block-wise
Prediction of Landslide Susceptibility
- Authors: Li Chen, Yulin Ding, Han Hu, Qing Zhu, Haowei Zeng, Haojia Yu, Qisen
Shang, Yongfei Song
- Abstract summary: Current methods generally apply a single global model to predict the landslide susceptibility map (LSM) for an entire target region.
We argue that, in complex circumstances, each part of the region holds different landslide-inducing environments, and therefore, should be predicted individually with respective models.
We train an intermediate representation by the meta-learning paradigm, which is superior for capturing information from LSM tasks in order to generalize proficiently.
- Score: 12.299036699550374
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting a landslide susceptibility map (LSM) is essential for risk
recognition and disaster prevention. Despite the successful application of
data-driven prediction approaches, current data-driven methods generally apply
a single global model to predict the LSM for an entire target region. However,
we argue that, in complex circumstances, especially in large-scale areas, each
part of the region holds different landslide-inducing environments, and
therefore, should be predicted individually with respective models. In this
study, target scenarios were segmented into blocks for individual analysis
using topographical factors. But simply conducting training and testing using
limited samples within each block is hardly possible for a satisfactory LSM
prediction, due to the adverse effect of \textit{overfitting}. To solve the
problems, we train an intermediate representation by the meta-learning
paradigm, which is superior for capturing information from LSM tasks in order
to generalize proficiently. We chose this based on the hypothesis that there
are more general concepts among LSM tasks that are sensitive to variations in
input features. Thus, using the intermediate representation, we can easily
adapt the model for different blocks or even unseen tasks using few exemplar
samples. Experimental results on 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.
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