GEO ReSeT

This research proposal aims to develop a versatile foundation model for geo-spatial data that can be used for any task and with any data modality. By using location on the Earth's surface as the common link between different modalities, the model will be able to incorporate a variety of data sources, including remote sensing imagery, textual descriptions of places, and features in maps. Through self-supervised learning methods such as contrastive learning or multi-modal masked autoencoders, the model will leverage the large amounts of unlabeled geo-spatial data from these different sources to learn a better representation of any geo-spatial location and convey a semantic representation of the information. The proposed foundation model has the potential to revolutionise Earth observation by allowing for few or zero-shot solutions to classical problems such as land-cover and land-use mapping, target detection, and Visual Question Answering. It will also be useful for a wide range of applications with a geo-spatial component, including environmental monitoring, urban planning and agriculture. By leveraging several data modalities, the foundation model will provide a more comprehensive and accurate understanding of the Earth's surface, enabling more informed decisions and actions. This will be particularly valuable for new potential users in sectors such as journalism, social sciences or environmental monitoring, who may not have the resources or expertise to collect their own training datasets and develop their own methods, thus moving beyond open Earth observation data and democratising the access to Earth observation information.