Landslide detection is concerned with delineating the extent of landslides. Most of existing works on landslide detection have limited geographical extents. Therefore, the models developed out of these studies might perform poorly when applied to regions with different characteristics. This study investigates an Object-Based Image Analysis methodology built on unsupervised and supervised Machine Learning to detect the location of landslides occurred in multiple regions across the world. The utilized data includes Sentinel-2 multi-spectral satellite imagery and ALOS Digital Elevation Model. In the segmentation stage, pre and post-landslide images undergo segmentation using K-means clustering. Following the segmentation stage and dataset preparation and removing highly-correlated features from the dataset, two Random Forest classifiers (RF1 and RF2) are trained and tested on two different datasets to measure the generalization level of the algorithms with RF1 dataset spanning over more geographical diversities than RF2 dataset. The results show that the RF models can successfully detect landslide segments with test precision = 0.96 and recall = 0.96 for RF1 and test precision = 0.90 and recall = 0.87 for RF2. Further validation shows that, compared to RF2, RF1 results in less mislabelled non-landslide segments.

Diamantidou, E.; Santinelli, G.; Giardino, A.; Stronkhorst, J., and de Vries, S., 2020. An automatic procedure for dune foot position detection: Application to the Dutch coast. Journal of Coastal Research, 36(3), 668-675. Coconut Creek (Florida), ISSN 0749-0208. Coastal indicators are a useful proxy in coastal zone management to describe the status of a physical system and to assess the effectiveness of possible interventions. They can be used as a basis to implement and evaluate coastal erosion policies, as it is done, for example, in The Netherlands. One often used coastal indicator is the position of the dune foot. In the current definition used in The Netherlands to describe the dune foot position, the actual geometry of the profile is, however, not accounted for, but this is simply based on one reference value for the entire coastline. In the present study, an automatic procedure for the detection of the dune foot position is proposed based on the actual shape of the cross-shore profile and on the evaluation of the first and second derivatives of the cross-shore topography. The methodology is compared to visual observations as well as satellite images for case studies in The Netherlands and Portugal, hence showing that the methodology is generally applicable. The algorithm to derive the dune foot position in a cross-shore profile and the database derived from this study are publicly available.

This paper presents an application of the Bayesian belief network for coastal erosion management at the regional scale. A "Bayesian erosion management network" (BERM-N) is developed and trained based on yearly cross-shore profile data available along the Holland coast. Profiles collected for over 50 years and at 604 locations were combined with information on different sand nourishment types (i.e., beach, dune, and shoreface) and volumes implemented during the analyzed time period. The network was used to assess the effectiveness of nourishments in mitigating coastal erosion. The effectiveness of nourishments was verified using two coastal state indicators, namely the momentary coastline position and the dune foot position. The network shows how the current nourishment policy is effective in mitigating the past erosive trends. While the effect of beach nourishment was immediately visible after implementation, the effect of shoreface nourishment reached its maximum only 5-10 years after implementation of the nourishments. The network can also be used as a predictive tool to estimate the required nourishment volume in order to achieve a predefined coastal erosion management objective. The network is interactive and flexible and can be trained with any data type derived from measurements as well as numerical models.