Unsupervised active–transfer learning for automated landslide mapping

Detailed landslide inventories are required for multiple purposes including disaster damage assessments, susceptibility mapping for spatial planning, and disaster risk reduction. Active learning is an artificial intelligence strategy that can achieve good performances in landslide mapping by training a machine-learning model with a reduced number of landslide/non-landslide observations, which can save time and effort in labeling training instances. Nevertheless, active-learning models are unstable at the beginning of sample selection due to the limited initial knowledge of landslide distribution. Transfer learning can help make the learner robust by transferring a landslide model trained on an existing landslide inventory from a different, but geographically similar source area, to the unseen target area. In order to adjust a transferred machine-learning model to the possibly unique environmental characteristics of the unseen area, we proposed a new framework called Unsupervised Active-Transfer Learning (UATL). This framework used a weight function to combine the landslide model transferred from the source area, with a model trained on a small, but increasing number of landslide/non-landslide observations from the target area to efficiently build a more robust learner. We examined two methods, adaptive UATL and regular UATL, which differed in the way they assign weights to the combined learners. We evaluated our proposed new methods by comparing them with three benchmark methods (active learning only, model transfer only, and the model trained in the unseen area itself) by means of the partial area under the receiver operating characteristic (ROC) curve (AUROC) as the evaluation criterion. The results showed that the new methods, and especially adaptive UATL, can achieve good predictive performances. With only about 235 training instances from the target area, the partial AUROC obtained from adaptive UATL was only 2% lower than that obtained from the model trained in the target area itself, and consistently outperformed the other two benchmarks. Overall, we suggest that the framework proposed can be applied to the natural hazards management workflow for assisting in emergency response, especially in data-scarce regions (e.g., mountainous areas and developing countries).