# Using Artificial Intelligence for Space Challenges: A Survey
## 5 Discussion  
The challenges in the space sector impact our lives and the perception of our future more than we can imagine. Nevertheless, at the same time, AI is enabling progress and innovation in several sectors. Notably, in the space sector, AI is helping scientists and industries provide robust solutions to the most relevant problems, but most of all, AI enables discoveries. The problems that AI addresses are categorized into three main areas: (i) mission design and planning, (ii) space exploration, and (iii) Earth observation, as shown in Figures 1 and 2. It is worth noting that we did not explore these categories as watertight compartments, because we found several studies that promote the exploitation of AI in new, overlapping, and disruptive areas [70, 73, 89, 90, 115, 117, 122, 123, 124, 125, 126, 127, 128, 129, 136]. Indeed, many of the solutions that can innovate one area also impact the results and possibilities of other areas. For example, improving health satellite monitoring systems is helping both space exploration and Earth observation applications [57]. Again, supporting knowledge management strategies can facilitate the planning of a space mission, but at the same time, it provides input for the challenges in data management for space situational awareness [32]. Moreover, new AI-based techniques used to map and investigate the lunar or Martian surfaces [82, 24, 88] can improve our knowledge of the health status of the Earth [21]. Therefore, the presented challenges and discoveries that we report here are all connected, as Figure 2 shows. Firstly, the diversified approach of reinforcement learning has applications in new frontiers of space exploration [38, 39, 40], where multi-robot systems on Mars [36, 152] or internet space things [55] can be trained to perform primary tasks by adapting to the complex environment as requested by the technique. At the same time, this approach is also exploited to automate processes in the areas of agriculture and farming [90, 118]. Deep learning has recently been widely used by researchers to respond to the space sector's countless challenges. For example, neural networks are used to improve the land use for agriculture [101, 109], mitigate the consequences of climate change [138, 140], and monitor water quality [147]. Another example is the use of CNNs [67, 68] to assist astronauts during a mission by facilitating the astronauts' work and lives through an intelligent assistant. Furthermore, RNNs can improve telemetry analysis of space operations [62] in order to temporally predict anomalies on board. GANs are also eing applied to improve the classification of hyperspectral images and to detect anomalies in them [97; 98; 99]. For the same aims, unsupervised learning has been already exploited with satellite telemetry data to produce an advanced health monitoring system and prevent unexpected failures [56; 59]. Another challenge pertains to information retrieval and knowledge reuse in space missions: there is a need to develop a design engineering assistant to design a space mission. Several approaches exploit expert systems to address this problem [52; 63], but also a semi-supervised technique [48]. In addition, semi-supervised learning has been exploited in remote sensing, particularly for high-resolution EO images [106; 107; 108] but also for the classification of Mars imagery [82]. The application of supervised learning covers the most disparate problems, starting from supporting the challenges of the disaster management phases [126; 127], to satellite fault diagnosis [61], to automation of agriculture[118], and environmental issues such as water quality prediction [146; 147] and mitigation of climate change's effects [138; 140; 150]. Furthermore, fuzzy logic solves similar issues related to climate change [140], water [147] and atmosphere [150] quality monitoring, but also to space exploration with multi-robot systems [78].