The use of Remotely Piloted Aircraft Systems (also called UAS) for natural hazard characterization and hazard assessment has sharply increased in the last years. Nowadays, the massive diffusion of mini- and micro-RPAS is becoming a valuable alternative to the traditional monitoring and surveying techniques, opening novel and exciting viewpoints. The advantages of the use of RPAS are particularly crucial in areas characterized by hazardous natural processes, where the acquisition of high resolution remotely sensed data could be a powerful instrument to quickly assess the damages and plan effective rescues without any risk for operators.
In general, the primary goal of these systems is the collection of different data (e.g., images, LiDAR point clouds, gas or radioactivity concentrations) and the delivery of various products (e.g., 3D models, hazard maps, high-resolution orthoimages).
The use of RPAS has promising perspectives not only for natural hazards, but also in other fields of geosciences, to support a high-resolution geological or geomorphological mapping, or to study the evolution of active processes. The high repeatability of RPAS flights and their limited costs allows the multi-temporal analysis of a studied area. However, methodologies, best practices, advantages and limitations of this kind of applications are yet unclear and/or poorly shared by the scientific community.
This session aims at exploring the open research issues and possible applications of RPAS in particular for natural hazard but also for geosciences in general, collecting experiences, case studies, and results, as well as defining methodologies and best practices for their practical use. The session will concern the contributions aiming at i) introducing new methods for using RPAS in natural hazard scenarios, including autonomous flights ii) describing the development of new onboard sensors and fusion methodologies for the acquisition data with RPAS, iii) reporting new data processing methods, e.g. based on machine learning, for image or point cloud segmentation and classification, and iv) presenting original case studies that can be considered an excellent example for the scientific community.