  The death toll and monetary damages from landslides continue to rise despite
advancements in predictive modeling. The predictive capability of these models
is limited as landslide databases used in training and assessing the models
often have crucial information missing, such as underlying failure types. Here,
we present an approach for identifying failure types based on their movements,
e.g., slides and flows by leveraging 3D landslide topology. We observe
topological proxies reveal prevalent signatures of mass movement mechanics
embedded in the landslide's morphology or shape, such as detecting coupled
movement styles within complex landslides. We find identical failure types
exhibit similar topological properties, and by using them as predictors, we can
identify failure types in historic and event-specific landslide databases
(including multi-temporal) from various geomorphological and climatic contexts
such as Italy, the US Pacific Northwest region, Denmark, Turkey, and China with
80 to 94 % accuracy. To demonstrate the real-world application of the method,
we implement it in two undocumented datasets from China and publicly release
the datasets. These new insights can considerably improve the performance of
landslide predictive models and impact assessments. Moreover, our work
introduces a new paradigm for studying landslide shapes to understand
underlying processes through the lens of landslide topology.

Machine Learning