# Using Artificial Intelligence for Space Challenges: A Survey
## 4 State of the Art
### Works on Earth Observation
A semi-supervised learning algorithm enables high-quality map classification. Nevertheless, the authors of [103; 104] analyze the negative effect of microscopic material mixtures on the processing of hyperspectral images. Another relevant property of these data is the non-local smoothness, which has been analyzed in [104]. Again, in [103], an innovative spectral mixture model is proposed. The authors demonstrate that their approaches can obtain a more accurate abundance estimation and a better reconstruction of abundance maps. The authors of [105] develop a neural network that is able to synthesize EO images. Notably, an energy-based model is applied, and datasets are adapted to semi-supervised settings. Furthermore, in [106; 107; 108], semi-supervised learning is evaluated due to the lack of labels and the speckle presence on raw images coming from EO data. Indeed, the authors of [107] propose the MiniFrance suite, a large-scale dataset for semi-supervised and unsupervised semantic segmentation in EO. Finally, in [106], DCNNs are coupled with semi-supervised learning to build a framework called Boundary-Aware Semi-Supervised Semantic Segmentation Network, which is able to obtain more accurate segmentation results. In [109], the combination of drone-borne thermal imaging with neural networks is produced to locate ground-nests of birds on agricultural land with high performance. Several studies [110; 111; 112] have addressed the need to review the application of AI to solve different agriculture-related problems [113] and improve decision support at the farm level [110; 114]. One of the most urgent needs is to respond to adversarial and life-critical situations such as pandemics, climate change, and resource scarcity [115; 116] with prompt and sustainable agricultural and food production and improved food supply systems. As suggested in [117; 118], automation of agriculture and farming is a solution for the increasing demands of food and nutrition, which are also aggravated by above-mentioned problems, such as pollution and climate change, resource depletion, population growth, and the need to control irrigation and harmful pesticides. Indeed, AI can contribute to improving and developing sustainable food systems [119] able to revolutionize current agricultural and supply chain systems. Biodiversity assessments can strengthen our environments and the entire human society. The development of innovative and high-quality models to also monitor the impact of human activities on ecosystems is explored in [120] with the support of neural network algorithms. Indeed, AI will improve the conservation of ecosystems and species. The authors of [121] propose CAPTAIN (Conservation Area Prioritization Through Artificial Intelligence Networks), a methodology based on reinforcement learning, exploring and analyzing multiple biodiversity metrics to protect species from extinction. Another application field of AI that has gained close attention is disaster management. The studies [122; 123; 124] collect different solutions exploiting AI techniques to support the challenges of the different phases of disaster management phases, i.e., mitigation, preparedness, response, and recovery. The methods are discussed and categorized according to these phases. Furthermore, public engagement in AI-driven applications for disaster management is analyzed in [125] thanks to data provided by Australian residents. The results confirm the benefits of using AI for mitigating and responding to a disaster, and they encourage public and private sectors to adopt AI in disaster-management applications. The authors of [126] analyzed reactions on social media during the 2017 Atlantic Hurricane season to develop a system able to improve situational awareness and help organizations handle crises. The information retrieved consists of textual and imagery content from several tweets posted during the disaster. The tweet classification was performed by developing a random forest learning scheme.