# Using Artificial Intelligence for Space Challenges: A Survey
## 4 State of the Art
### Works on Earth Observation  
The survey [89] reviews machine learning models used to predict and understand components of the Earth observation system and divides the addressed problems into four categories: (1) classification of land cover types, (2) modeling of land-atmosphere and ocean-atmosphere, (3) detection of anomalies and extreme events, and (4) causal discovery. AI and Earth observations (EO) can meet the sustainable development goals (SDGs) and specifically contribute to them. Indeed, in [19] the authors demonstrate how a convolutional neural network, U-net with SE blocks, can perform an efficient segmentation of satellite imagery for crop detection. The study [90] reviews machine learning techniques applied to monitor SDGs and proposes explore furthering random forest, support vector machine, and neural networks to analyze EO data. Finally, in [91; 92], deep neural networks are exploited for on-board spacecraft. The main motivations of this approach concern the full use of real-time sensing data of multiple satellites from a smart constellation and therefore the on-the-edge processing of data and the resulting benefits in terms of the imaging product value. AI algorithms can be considered for consuming data at the source rather than on the ground, lightening the down-link load, and allowing the development of value-added applications in space. The studies in [19; 93] present a Copernicus Access Platform Intermediate Layers SmallScale Demonstrator, where AI methods are used to analyze and interpret Earth observation satellite images. As a primary step, the authors of [19] include the instantiation of a deep neural network able to extract meaningful information from the archived images and create a semantic scheme with several EO sensors (e.g., TerraSAR-X, WorldView, Sentinel-1, and Sentinel-2). The authors of [94] review the efficiency of Gaussian processes (GPs) in solving EO problems. GPs can accurately estimate parameters in acquired images at local and global scales. Furthermore, these processes can quickly adapt to multimodal data from different sensors and multitemporal acquisitions. In [95], the Google Earth Engine and Google Cloud Platform are used to generate a feature set where dimensionality reduction methods are tested for object-based land cover classification with SVM, with the support of Sentinel-1 and Sentinel-2 data. The authors of [96] apply dimensionality reduction to classify hyperspectral images and generate low-dimensional embedding features of these images. Generative adversarial networks (GANs) and CNNs are coupled in [97] for hyperspectral image classification. The study [97] presents several opportunities for the application of GANs in remote sensing [98]. In addition, noticed anomalies on hyperspectral images increase the possibilities of errors and decrease the estimation accuracy. In [99], a framework based on semi-supervised spectral learning and GANs is proposed for hyperspectral image anomaly detection. The authors of [94] review the efficiency of Gaussian processes (GPs) in solving EO problems. GPs can accurately estimate parameters in acquired images at local and global scales. Furthermore, these processes can quickly adapt to multimodal data from different sensors and multitemporal acquisitions. Convolutional neural networks are exploited in [100] to classify and detect land use and land cover from Sentinel-2 satellite images. The dataset EuroSAT contains 27,000 labeled and geo-referenced images made public. Furthermore, the study proposed in [101] exploits deep learning neural networks for remote sensing to improve the production of land cover maps and land use. In particular, the authors of [101] consider the southern agricultural region of Manitoba in Canada. In recent years, deep networks such as convolutional and recurrent neural networks have been broadly used to classify hyperspectral images. In [102], the authors build a network able to analyze the whole image with the help of a non-local graph using convolutional layers to extract features.