Landsifier: A python library to estimate likely triggers and types of landslides

The accuracy of landslide hazard models depends on landslide databases for model training and testing. Landslide databases frequently lack information on the underlying triggering mechanism (i.e., earthquake, rainfall), rendering them nearly useless in hazard models.

We created Landsifier, a Python-based unique library with three different machine-Learning frameworks for assessing the likely triggering mechanisms of individual landslides or entire inventories based on landslide 2D platforms and 3D shapes relying on an underlying digital elevation model (DEM). The base method extracts landslide planform properties as a feature space for the shallow learner-random forest (RF). An alternative approach uses 2D landslide images as input for the convolutional neural network deep learning algorithm (CNN). The final framework uses topological data analysis (TDA) to extract features from 3D landslide surfaces, which are then fed into the random forest classifier as a feature space. We tested the developed methods on six inventories spread over Japan. We achieved mean accuracy ranging from 70% to 98%.

Advancing this trigger classifier, we are working on the next generation to classify also the landslide types (i.e., flows, slides, falls, complex) similarly.