Monitoring Changes in Big Satellite Data via Massively-Parallel Artificial Intelligence

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).

DeepCrop: Quantification of Carbon Stocks via Deep Learning

Recent technological developments in deep learning and drone-borne Lidar scanners pave the way for constraining the uncertainty inherent to quantify and project ecosystems' carbon (C) stocks. With a rising demand for biomass, DeepCrop aims to precisely measure above ground biomass and to estimate C sinks in croplands and forests. The ambition is to bridge expertise of experimental and computer scientists to develop novel tools for the automated processing of Lidar data utilizing deep learning and drones. Joint work with the University of Copenhagen (Katerina Trepekli, Thomas Friborg, Christian Igel). This project is, in part, supported by the Villum Foundation and the Data+ program of the University of Copenhagen.