Classification

This research consists of two parts that will be presented in the following. Contrastive Self-supervised Data Fusion for Satellite Imagery Supervised learning of any task requires large amounts of labeled data. Especially in the case of satellite imagery, unlabeled data is ubiquitous, while the labeling process is often cumbersome and expensive. Therefore, it is highly worthwhile to leverage methods to minimize the amount of labeled data that is required to obtain a good performance of the given down-stream task. In our first work, we leverage contrastive learning in a multi-modal setup to achieve this result. ...

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. ...