Active-Learning Approaches for Landslide Mapping Using Support Vector Machines

Zhihao Wang,Alexander Brenning

doi:10.3390/rs13132588

Abstract

Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models. However, the manual labelling of instances for training machine learning models is time-consuming given the data requirements of flexible data-driven algorithms and the small percentage of area covered by landslides. Active learning aims to reduce labelling costs by selecting more informative instances. In this study, two common active-learning strategies, uncertainty sampling and query by committee, are combined with the support vector machine (SVM), a state-of-the-art machine-learning technique, in a landslide mapping case study in order to assess their possible benefits compared to simple random sampling of training locations. By selecting more “informative” instances, the SVMs with active learning based on uncertainty sampling outperformed both random sampling and query-by-committee strategies when considering mean AUROC (area under the receiver operating characteristic curve) as performance measure. Uncertainty sampling also produced more stable performances with a smaller AUROC standard deviation across repetitions. In conclusion, under limited data conditions, uncertainty sampling reduces the amount of expert time needed by selecting more informative instances for SVM training. We therefore recommend incorporating active learning with uncertainty sampling into interactive landslide modelling workflows, especially in emergency response settings, but also in landslide susceptibility modelling.

Highlights

Active learning using margin sampling outperformed query by committee andand are strongly correlated, we used green chromatic coordinate (GCC) as a vegetarandom sampling after only four epochs, i.e., starting with a learning instance size of tion310 index well as red chromatic coordinate (RCC)
Considering importance of the cost and gamma hyperparameters for the flexibility of the support vector machine (SVM), we examined the variability in optimal hyperparameters for different activelearning epochs in margin sampling
Considering the importance of the cost and gamma hyperpara ens. 2021, 13, 2588 parameters across the repetitions were increasingly concentrated around the 20 gion and the optimal γ to around 2−5, the optimal region extended di towards higher cost values when combined with larger γ values (Figure 5)

Summary

Introduction

Despite significant progress in landslide hazard assessment and mitigation, these hazards still present a major challenge for policymakers to reduce monetary losses and casualties. The occurrence probability of landslides, which broadly include a large variety of downslope movement processes on hillslopes under the effects of gravity [1], varies greatly in space and time as a result of complex patterns of predisposing factors and temporal variation in triggering factors. Considering the ongoing global trends of urbanization, deforestation, and climate change, landslide science faces the growing challenge of having to update landslide hazard assessments and provide rapid post-disaster information in the event of regional triggering events such as rainstorms and earthquakes [2,3,4]. An earthquake in Tomakomai, Japan triggered about 10,000 landslides causing 36 deaths [3], and in 2018, landslides triggered by seasonal heavy precipitation caused approximately

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