Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Operational Design Domain Monitoring and Augmentation for Autonomous Driving

Recent technological advances in Autonomous Driving Systems (ADS) show promise in increasing traffic safety. One of the critical challenges in developing ADS with higher levels of driving automation is to derive safety requirements for its components and monitor the system's performance to ensure safe operation. The Operational Design Domain (ODD) for ADS confines ADS safety to the context of its function. The ODD represents the operating environment within which an ADS operates and satisfies the safety requirements. To reach a state of "informed safety", the system's ODD must be explored and well-tested in the development phase. At the same time, the ADS must monitor the operating conditions and corresponding risks in real-time. Existing research and technologies do not directly express the ODD quantitatively, nor do they have a general monitoring strategy designed to handle the learning-based system, which is heavily used in the recent ADS technologies. The safety-critical nature of the ADS requires us to provide thorough validation, continual improvement, and safety monitoring of these data-driven dependent modules. In this dissertation, the ODD extraction, augmentation, and real-time monitoring of the ADS with machine learning components are investigated. There are three major components for the ODD of the ADS with machine learning components for general safety issues. In the first part, we propose a framework to systematically specify and extract the ODD, including the environment modeling and formal and quantitative safety specifications for models with machine learning parts. An empirical demonstration of the ODD extraction process based on predefined specifications is presented with the proposed environment model. In the second part, the ODD augmentation in the development phase is modelled as an iterative engineering problem solved by robust learning to handle unseen future natural variations. The vision tasks in ADS are the major focus here, and the effectiveness of model-based robustness training is demonstrated, which can improve model performance and the application of extracting edge cases during the iterative process. Furthermore, the testing procedure provides us with valuable priors on the probability of failures in the known testing environment, which can be further utilized in the real-time monitoring procedure. Finally, a solution for online ODD monitoring that utilizes the knowledge from the offline validation process as Bayesian graphical models to improve safety warning accuracy is provided. While the algorithms and techniques proposed in this dissertation can be applied to many safety-critical robotic systems with machine learning components, in this dissertation the main focus lies on the implications for autonomous driving

Challenges of bridge maintenance inspection

Bridges are amongst the largest, most expensive and complex structures, which makes them crucial and valuable transportation asset for modern infrastructure. Bridge inspection is a crucial component of monitoring and maintaining these complex structures. It provides a safety assessment and condition documentation on a regular basis, noting maintenance actions needed to counteract defects like cracks, corrosion and spalling. This paper presents the challenges with existing bridge maintenance inspection as well as an overview on proposed methods to overcome these challenges by automating inspection using computer vision methods. As a conclusion, existing methods for automated bridge inspection are able to detect one class of damage type based on images. A multiclass approach that also considers the 3D geometry, as inspectors do, is missing