# Deep Learning Methods for Space Situational Awareness
## 1 Introduction
One critical task is to classify space objects according to their properties. Unfortunately, the information on space objects is often limited. Typically, the visual magnitude and the radar cross section (RCS) of space objects can be obtained via optical and radar sensors, respectively. Artificial Intelligence (AI) and machine learning (ML) systems appear to be very promising for detecting and classifying these kinds of objects. Methods for assessing behavioral analysis [32] and autonomy [33; 34], reported in the literature, include Artificial Neural Networks (ANN) [35; 36], Support Vector Machines (SVM) [37], reinforcement learning [38; 39], and deep neural networks [31]. These ML methods, as well as deep learning (DL) methods [40], support evidence-based knowledge for SDA [41] of space-domain sensor fusion programs such as DARPA Hallmark [42]. SSA also includes the understanding of mission policies, technical aims, and orbital mechanics [43; 44; 45]. In addition, SSA benefits from game-theory studies in supporting pursuit-evasion analysis [46; 47; 48; 49; 50] and from gathering data to track satellites, debris, and natural phenomena [51; 52; 53; 54; 55; 56; 57]. Then, in order to have effective SSA results, tracking efforts must coordinate with detection policies [58; 59], waveform selection [60], and attack mitigation [61; 62; 63; 64]. Other recent methods for effective SSA include time-delay Neural networks (TDNN) [65], ML and CNN networks [66; 67; 68; 69; 70], clustering [71], orbital control theory [72], a game theoretic approach, namely Adaptive Markov Inference Game Optimization (AMIGO) engine [73; 74; 75; 76], and Deep Reinforcement Learning [77; 78; 79]. Even more recently, new trends in the literature are focusing on agile, intelligent, and efficient computer vision architectures operating on quantum neuromorphic computing as part of an SSA network [80]. Quantum neuromorphic vision, in conjunction with polarimetric Dynamic Vision Sensors p(DVS) principles, represent the SSA of tomorrow, working at very high speeds with low requirements for bandwidth, power, and memory. In this paper, we will focus on and review methods belonging to a specific subgroup of SSA, namely on space debris automatic detection, since millimeter-sized orbital debris poses the highest end-of-mission risk for most spacecraft roboticists operating in LEO [81; 82]. In this context, this work aims at providing an overview of recent AI and AIoT for space applications by examining the current state of the art and discussing future opportunities to support efforts in space exploration. The rest of our paper is organized as follows. In Abbreviations, a list of the used acronyms is reported. Section 2 is divided into three sub-sections and illustrates SSA space debris applications, with and without the use of deep learning methods. The last part of Section 2 shows some interesting discussions about the use of new types of neural networks. Subsequently, in Section 3, we will discuss the case study and the experimental setup. The results, used as a proof of concept of some of the reviewed DL methods for space debris detection, are then shown and discussed in Section 4, while Section 5 briefly concludes our work, outlining the strong and weak points of these technologies.