# Deep Learning Methods for Space Situational Awareness
## 1 Introduction  
Artificial Intelligence (AI) is now a part of the daily life of the majority of people. AI is based on the idea that computers can (be programmed to) think just like humans, creating analyses, reasoning, understanding, and getting answers for and from different situations. The big step within the studies of AI is the development of systems capable of learning and developing on their own, or of creating new deductions from the junction of various fragmented information, just as happens within the neurological system of human beings. Therefore, AI is the technology that provides intelligent machines to solve problems, increase productivity, and improve areas such as health, finance, marketing, sales, customer service, and agriculture, among many other fields of application [1; 2; 3]. The Internet of Things (IoT), on the other hand, is a world with smart technology that impacts almost every aspect of our society. When we think about IoT, we think about technological tools used in everyday life, such as connected thermostats, home security systems, and cars [4; 5; 6]. The Artificial Intelligence of Things (AIoT) is the sweet spot that combines the best of both worlds: it leverages the technical capabilities of the IoT and makes the best use of the data processing and interpretation capabilities of AI to deliver advanced solutions to users, meeting their specific needs and use cases [7; 8]. One of the main benefits of AI in action in an IoT environment is real-time decision-making. The results are obtained instantaneously. For example, an AI-based surveillance system with objectdetection technology can instantly detect work or environmental hazards, such as sparks from equipment or an employee's fall, and immediately report notifications. Advanced systems can even make decisions and trigger incident responses on their own [9; 10; 11]. The IoT technology is as promising as it can be, but it is not without its fair share of implementation shortcomings and challenges. Simplifying data from multiple sources is challenging; one needs to work on infrastructure requirements to examine data security and privacy, etc. The recent scientific and technological advances led by AIoT have also allowed for many opportunities in the space industry, which is a type of field that has been increasingly developed over the last decade, and very significant benefits are expected soon in the field of Satellite for IoT (SIoT). In the harsh industrial environment, the necessity for joining IoT and satellite communications is needed [12]. Many studies have been carried out, especially for low-orbit satellites, due to their interesting characteristics, such as low latency and large capacity [13; 14; 15; 16; 17]. As a consequence, IoT applications through satellite systems are developing quickly and promise to be the biggest prospect for the satellite market of the future. IoT applications will be very well supported by satellite mega-constellations. These are systems with thousands, and even tens of thousands, of satellites in low Earth orbit (LEO), and they are nowadays becoming a reality [18; 19; 20]. Among all of Earth's orbital regimes, LEO, generally defined as the region in space between \(\sim\)160 to 2000 km in altitude [21], is by far the most congested (Figure 1). As the closest to Earth and, therefore, the cheapest to reach, LEO is the most popular orbital regime for satellite deployments [22]. Companies are placing satellites into orbit at an unprecedented frequency to build mega-constellations of communications satellites in LEO. In two years, the number of active and defunct satellites in LEO has increased by over 50% to about 5000. SpaceX alone is on track to add 11,000 more as it builds its Starlink mega-constellation and has already filed for permission with the Federal Communications Commission (FCC) for another 30,000 satellites [23]. Others have similar plans, including OneWeb, Amazon, Telesat, and GW, a Chinese state-owned company.