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The effects of climate change are causing an increase in the frequency and extent of natural disasters. Because of their morphological characteristics, rivers can cause major flooding events. Indeed, they can be subjected to variations in discharge in response to heavy rainfall and riverbank failures. Among the emerging methodologies that address the monitoring of river flooding, those that include the combination of Unmanned Aerial Vehicle (UAV) and photogrammetric techniques (i.e., Structure from Motion-SfM) ensure the high-frequency acquisition of high-resolution spatial data over wide areas and so the generation of orthomosaics, useful for automatic feature extraction. Trainable Weka Segmentation (TWS) is an automatic feature extraction open-source tool. It was developed to primarily fulfill supervised classification purposes of biological microscope images, but its usefulness has been demonstrated in several image pipelines. At the same time, there is a significant lack of published studies on the applicability of TWS with the identification of a universal and efficient combination of machine learning classifiers and segmentation approach, in particular with respect to classifying UAV images of riverine environments. In this perspective, we present a study comparing the accuracy of nine combinations, classifier plus image segmentation filter, using TWS, also with respect to human photo-interpretation, in order to identify an effective supervised approach for automatic river features extraction from UAV multi-temporal orthomosaics. The results, which are very close to human interpretation, indicate that the proposed approach could prove to be a valuable tool to support and improve the hydro-geomorphological and flooding hazard assessments in riverine environments.

Digital elevation models (DEMs) represent a fundamental resource in geomorphological analysis. The increasing availability of open-access DEMs over wide areas is advantageous, but requires an evaluation of DEM quality and errors. This work applies a hierarchical assessment of global, continental and national DEMs in Italy in order to explore

The processing of aerial high-resolution images is key for territorial mapping and change detection analysis in hydro-geomorphological high-risk areas. A new method has been developed in the context of CLOSE (Close to the Earth) project, resulting in a workflow based on open source MicMac photogrammetric suite and on High-Performance Computing. The workflow allowed to process a sequence of more than 1000 drone images captured along a reach belonging to the Basento River in Basilicata (Italy) during one single run.The workflow optimisation aims to extract the orthophotomosaic, the point cloud and the Digital Surface Model (DSM) of selected areas. The high quality of the image details can be used for land cover classification and extrapolating features useful to mitigate the hydro-geomorphological hazard, through Machine Learning models trained with satellite public data. Several Convolutional Neural Networks have been tested using progressively more complex layer sequences, data augmentation and callback techniques for training procedures. The results are given in terms of model accuracy and loss.