Over the last decade, machine learning and deep learning paradigms have experienced an astonishing development in different domains. This development has led to a new data-driven era of science and technology. Following this trend, deep learning has also revolutionized the field of remote sensing. Deep learning has been successfully applied to several Earth observation tasks, e.g., land cover classification, semantic segmentation, change detection, and disaster mapping. However, most of the deep learning-based methods developed for remote sensing are supervised. A major pitfall of deep learning based supervised techniques is their high dependence on a large and well-representative corpus of labelled data. It is expensive and time-consuming to obtain such labels in Earth observation. Thanks to the Copernicus program by the European Space Agency, a massive amount of unlabeled Earth observation data is currently available. Supervised methods do not effectively exploit this abundant pool of unlabeled data.
In the computer vision literature, different paradigms that rely on less/zero labels have experienced a fast development, e.g.,
● Unsupervised learning,
● Transfer learning,
● Self-supervised,
● Semi-supervised learning,
● Weakly supervised learning,
● Meta learning.
The main technical objective of this project is to harness the power of artificial intelligence and Earth observation by exploiting the above non-supervised learning paradigms. Towards this it is crucial to come up with non-supervised learning-based solutions for impactful use-cases that leverage unlabeled EO data.
The project further aims towards devising methods that can provide suitable generalization by being easily adaptable to different EO tasks and across different geographies.
The above-mentioned technical objectives will be fulfilled by defining suitable use cases [link to next sub page] from different thematic areas, e.g., agriculture and forestry and by further defining test sites from different locations. The non-supervised learning-based method will be evaluated based on usual quantitative performance indices along with qualitative analyses commenting on their generalization capability and versatility on different EO tasks.