2025-09-02, 09:00–09:30 (Europe/Amsterdam), HugoTECH
The volume of unlabeled Earth observation (EO) data is huge. To interpret this vast amount of data, efficient modelling approaches are needed that can generalize to large geographic areas and are robust to inherent noise. Data-driven approaches promise great potential for interpreting and combining data from different space missions. In this talk, I will present our work on global canopy height mapping (https://langnico.github.io/globalcanopyheight/) with optical satellite images and sparse spaceborne lidar data and discuss a recent project called MMEarth (https://vishalned.github.io/mmearth/) that explored multi-modal pretext tasks for learning representations that are suitable for a range of downstream tasks with limited training data.
The volume of unlabeled Earth observation (EO) data is huge. To interpret this vast amount of data, efficient modelling approaches are needed that can generalize to large geographic areas and are robust to inherent noise. Data-driven approaches promise great potential for interpreting and combining data from different space missions. In this talk, I will present our work on global canopy height mapping (https://langnico.github.io/globalcanopyheight/) with optical satellite images and sparse spaceborne lidar data and discuss a recent project called MMEarth (https://vishalned.github.io/mmearth/) that explored multi-modal pretext tasks for learning representations that are suitable for a range of downstream tasks with limited training data.
https://drive.google.com/file/d/1GSwkCq9vMv1fIDnb-t5qeApa_amfH32F/view?usp=drive_link
Nico is an Assistant Professor at the University of Copenhagen associated with the Global Wetland Centre and the Pioneer Centre for AI. He is also a core member of the Climate AI Nordics network. Before moving to Denmark for a Postdoc, Nico has received a PhD from ETH Zurich. His research focuses on computer vision, machine learning, and remote sensing, and on developing new methods to support environmental sciences. More information can be found on his website: https://langnico.github.io/