This AI Paper Deploys a Light-Weight Foundational Model in Outer Space for the First Time

Space Technology is advancing day by day. There have been efforts from different research groups to build Machine Learning and Artificial Intelligence models in outer space that would influence space research. The data that is collected provides us with information regarding aerial mapping, weather prediction, and deforestation. These satellites collect the data but cannot process the dataset through data processing techniques. Hence, these satellites are unable to fetch rapid events like natural disasters.

To have an approach in space technology to solve these problems, researchers trained the ML models in space that would process this data. The researchers trained the simpler models at an earlier stage that detected the cover on the clouds directly while training in space rather than training on the ground. The training approach is called few-shot learning or active learning. This approach takes the most important features required to train the model. Hence, It is called few-shot learning. The main advantage of this model over others is that the data that is being collected can be converted into smaller dimensions, making the model faster and more effective. This model falls under the category of Computer Vision models. The training part of this model consists of keeping the important values combined in the form of a vector. The aim of this model is to detect whether there is cloud cover present or not. This results in a classification model to train.

The model is broadly classified into two categories. The first part of the model is to collect the images and train them on the ground, while the second part of the model classifies the model based on binary classification, which gives us information regarding the cloud cover. The second part is trained on the satellite itself. The training requires several rounds of epochs to be trained. On the other hand, the team’s tiny model completed the training in one and a half seconds. Researchers also said that the model is automatically adaptable for all forms of data. Researchers are still working on different types of models that would work on different changes of interest. 

Researchers are still working on a model that would solve complex datasets consisting of images from hyperspectral satellites. In this research, the model performance parameters like recall, precision, and F1 score are quite high. These scenarios consist of increasing opportunities in the space research world which is around the Earth and also in deep space. Researchers are going into the deep space with the emerging technology of Artificial Intelligence, which helps to explore the deep space.

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