Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide Susceptibility
This work addresses the challenge of limited landslide survey data in dangerous mountainous areas by enabling block-wise prediction with few samples, though it is incremental as it builds on existing meta-learning and segmentation approaches.
The study tackled the problem of predicting landslide susceptibility maps (LSM) in large-scale areas with environmental variations by segmenting regions into blocks and using meta-learning to train an intermediate representation for few-shot adaptation, achieving top few-shot adaptation performances in experiments on two study areas.
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.