Remote Sensing and Deep Learning of Global Tree Resources (TreeSense)

The overarching ambition of the TreeSense is to revolutionize global tree monitoring using advanced satellite remote sensing technology and deep learning methods based on artificial intelligence.

We expect that TreeSense will enable a far more detailed assessment of global tree resources than the information currently available – both for trees within and outside forested areas. This includes key properties such as species identification, crown size and height, carbon stocks and sequestration rates, as well as local usage.

Importantly, we aim to develop new methods for detecting changes in tree resources over recent decades. This will allow us to gain a better understanding of the impacts of global warming and extreme weather events, as well as the extent of human-induced disturbances to forests. Ultimately, we seek to uncover the potential of tree-based production systems supporting livelihoods as a means of climate change mitigation and to enhance our understanding of the importance of tree resources for sustainable food systems.

TreeSense is an interdisciplinary research center that integrates elements from computer science, geoinformatics, ecology, and both physical and human geography. The University of Copenhagen (UCPH), Department of Geosciences and Natural Resource Management (IGN), is the host institution for the TreeSense Center of Excellence.