The remote sensing field witnesses an explosion in the amount of available data, with petabytes of data being gathered by single satellites every year. Such data allow the identification of fine details in the landscape and the recent breakthroughs in artificial intelligence (AI) facilitate application areas such as agricultural monitoring, infrastructure management, mapping forest development, and many others. Applying AI models on a global scale can become extremely time-consuming with analyses potentially taking weeks, months, or even years. This project aims at the development of highly-efficient parallel implementations for AI methods that allow to detect and monitor “changes” visible in time series satellite data. Collaboration with the University of Copenhagen (Cosmin Oancea and Marcos Vaz Sallies). Supported by the Independent Research Fund Denmark (DFF).
|Project status||in progress|
|Project time||since 01.10.2020|
|Keywords||remote sensing; artificial intelligence; parallel implementations; satellite data|