Road traffic noise is a global issue that can lead to severe health effects. Despite the ubiquity of traffic noise, its quantification or estimation is complicated and detailed road traffic maps are only available for select countries or areas. We investigate whether it is possible to train a segmentation model to esimate road traffic noise from satellite imagery.

Self-supervised learning provides a powerful means to pretrain models based on un-labeled data. Un-labeled Earth observation data are abundant: this circumstance combined with the availability of multi modal data makes Earth observation a perfect playground for self-supervised learning. Our early results are very promising…

Greenhouse gas emissions from the industrial economic sector are a major driver of the currently observed climate change. We developed a deep learning approach to identify and characterize industrial smoke plumes. In the future, we will utilize this approach to estimate greenhouse gas emissions from remote sensing data on a global scale.