The solar influence on space weather and terrestrial environment is substantial. Strong geomagnetic storm activity can significantly affect astronauts in orbit, communications and GPS systems and disrupt Earth’s power distribution networks, making continuous monitoring and forecasting of solar activity vital. Sunspots are magnetic disturbances in the photosphere characterized by their dark appearance in the solar disk, being directly related to phenomena that contribute to these intense storms, namely solar flares and coronal mass ejections. This article lies at the intersection between solar surveillance and computer vision by applying state-of-the-art deep learning algorithms in the automatic detection of sunspots and sunspot groups. Based on two techniques, semantic segmentation and instance segmentation, two algorithms are implemented to tackle both purposes, U-Net and Mask R-CNN respectively. The ground-truth dataset was built from the available Debrecen Heliographic Observatory (DHO) space-borne sunspot catalogues from 2010 to 2014. The best U-Net implemented model presented a 74.2% IoU, surpassing the detection results evidenced by the Automated Solar Activity Prediction System (ASAP). The instance segmentation approach, a novelty application technique for sunspot group detection and still a challenging task in computer vision, achieved 51.7 bounding box AP and 78.6% accuracy in predicting the number of sunspot groups.

The usage of aerial drones has become more popular as they also become more accessible, both in economic and usability terms. Nowadays, these vehicles can present reduced dimensions and a good cost-benefit ratio, which makes it possible for several services and applications supported by aerial drone networks to emerge. Taking into account the enormous diversity of use cases, many of the existing solutions for autonomous control focus on specific scenarios. Generic mission planning platforms also exist, but most of them only allow missions consisting of linear waypoints to be traversed. These situations translate into a mission support that is not very flexible. This paper proposes a modular infrastructure that can be used in various scenarios, enabling the autonomous control and monitoring of a fleet of aerial drones in a mission context. The platform allows the communication with the flight controller so that it can collect telemetry data and send movement instructions to the drone, and to monitor this data and send the commands remotely, also enabling robust mission planning with multiple drones, and enabling the interaction with internal and external sensors. The real tests performed through the platform show that the planned missions are executed exactly as they are planned in the platform.

Human populations and natural ecosystems are bound to be exposed to ionizing radiation from the deposition of artificial radionuclides resulting from nuclear accidents, nuclear devices or radiological dispersive devices (“dirty bombs”). On the other hand, Naturally Occurring Radioactive Material industries such as phosphate production or uranium mining, contribute to the on site storage of residuals with enhanced concentrations of natural radionuclides. Therefore, in the context of the European agreements concerning nuclear energy, namely the European Atomic Energy Community Treaty, monitoring is an essential feature of the environmental radiological surveillance. In this work, we obtain 3D maps from outdoor scenarios, and complete such maps with measured radiation levels and with its radionuclide signature. In such scenarios, we face challenges such as unknown and rough terrain, limited number of sampled locations and the need for different sensors and therefore different tasks. We propose a radiological solution for scouting, monitoring and inspecting an area of interest, using a fleet of drones and a controlling ground station. First, we scout an area with a Light Detection and Ranging sensor onboard a drone to accurately 3D-map the area. Then, we monitor that area with a Geiger–Müller Counter at a low-vertical distance from the ground to produce a radiological (heat)map that is overlaid on the 3D map of the scenario. Next, we identify the hotspots of radiation, and inspect them in detail using a drone by landing on them, to reveal its radionuclide signature using a Cadmium–Zinc–Telluride detector. We present the algorithms used to implement such tasks both at the ground station and on the drones. The three mission phases were validated using actual experiments in three different outdoor scenarios. We conclude that drones can not only perform the mission efficiently, but in general they are faster and as reliable as personnel on the ground.