Reconstruction of Etna recent flank eruptions for assessing diversion barrier project

The thesis contributed to the volcanic hazard assessment through the reconstruction of some historical flank eruptions of Etna in order to obtain quantitative data (volumes, effusion rates, etc.) for characterizing the recent effusive activity, quantifying the impact on the territory and defining mitigation actions for reducing the volcanic risk as for example containment barriers. The reconstruction was based on a quantitative approach using data extracted from aerial photographs and topographic maps. The approach allows to obtain the temporal evolution of the lava flow field and estimating the Time Average Discharge Rate (TADR) by dividing the volume emplaced over a given time interval for the corresponding duration. The analysis concerned the 2001, 1981 and 1928 Etna eruptions. The choice of these events is linked to their impact on inhabited areas. The results of the analysis showed an extraordinarily high effusion rate for the 1981 and 1928 eruptions (over 600 m^3/s), unusual for Etna eruptions. For the 1981 Etna eruption an eruptive model was proposed to explain the high discharge rate. The obtained TADRs were used as input data for simulations of the propagation of the lava flows for evaluating different scenarios of volcanic hazard and analyse different mitigation actions against lava flow invasion. It was experienced how numerical simulations could be adopted for evaluating the effectiveness of barrier construction and for supporting their optimal design. In particular, the gabions were proposed as an improvement for the construction of barriers with respect to the earthen barriers. The gabion barriers allow to create easily modular structures reducing the handled volumes and the intervention time. For evaluating operational constrain an experimental test was carried out to test the filling of the gabions with volcanic rock and evaluating their deformation during transport and placement.

Scene Reconstruction And Understanding By RGB-D Sensors

This thesis investigates interactive scene reconstruction and understanding using RGB-D data only. Indeed, we believe that depth cameras will still be in the near future a cheap and low-power 3D sensing alternative suitable for mobile devices too. Therefore, our contributions build on top of state-of-the-art approaches to achieve advances in three main challenging scenarios, namely mobile mapping, large scale surface reconstruction and semantic modeling. First, we will describe an effective approach dealing with Simultaneous Localization And Mapping (SLAM) on platforms with limited resources, such as a tablet device. Unlike previous methods, dense reconstruction is achieved by reprojection of RGB-D frames, while local consistency is maintained by deploying relative bundle adjustment principles. We will show quantitative results comparing our technique to the state-of-the-art as well as detailed reconstruction of various environments ranging from rooms to small apartments. Then, we will address large scale surface modeling from depth maps exploiting parallel GPU computing. We will develop a real-time camera tracking method based on the popular KinectFusion system and an online surface alignment technique capable of counteracting drift errors and closing small loops. We will show very high quality meshes outperforming existing methods on publicly available datasets as well as on data recorded with our RGB-D camera even in complete darkness. Finally, we will move to our Semantic Bundle Adjustment framework to effectively combine object detection and SLAM in a unified system. Though the mathematical framework we will describe does not restrict to a particular sensing technology, in the experimental section we will refer, again, only to RGB-D sensing. We will discuss successful implementations of our algorithm showing the benefit of a joint object detection, camera tracking and environment mapping.