Commercial Vehicle Traffic Detection from Satellite Imagery with Deep Learning

Commercial vehicle traffic is currently responsible for 7% of global CO2 emissions. While road freight will remain the dominant mode of surface freight transportation, its contribution to climate change is likely to increase in the short term. Therefore, the quantitative monitoring of commercial vehicle (CV) traffic is essential for implementing targeted road emission regulations. However, ground monitoring stations are costly and less than half of all countries worldwide collect road freight activity. In this work, we investigate the feasibility of detecting and monitoring CV traffic in freely available satellite imagery from ESA’s Sentinel-2 satellites. ...

November 17, 2021

Power Plant Classification from Remote Imaging with Deep Learning

The industrial and power-generating economic sectors emit more than half of the annually and globally released greenhouse gas emissions, strongly contributing to global warming effects. In our recent work (Mommert et al. 2020) we laid the foundation to estimating greenhouse gas emissions from industrial sites by characterizing industrial smoke plumes from remote imaging data only. That work is part of a bigger effort to estimate greenhouse gas emission rates from satellite imagery for individual industrial sites. In this work, we made one further step towards achieving this goal. ...

June 16, 2021

Characterization of Industrial Smoke Plumes from Remote Sensing Data

The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of these emissions is mandatory to fully understand their effect on the Earth’s climate and to enforce emission regulations on a large scale. In this work, we investigate the possibility to detect and quantify industrial smoke plumes from globally and freely available multi-band image data from ESA’s Sentinel-2 satellites. ...

December 7, 2020

Automated Cloud Detection with Machine Learning

Motivation Most (optical) telescopes have to be protected from precipitation to prevent damage to their optics and electronics. For this reason, most observatories use all-sky cameras - cheap, but very sensitive CMOS cameras equipped with fish-eye lenses - that monitor the night sky for incoming clouds. Telescope operators can observe live streams from these cameras to make informed decisions as to whether the current weather situation requires the closing of the telescope dome, or not. ...

April 22, 2020