Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery

Power Generation Data Set

This data set contains imaging data acquired by ESA's Sentinel-2
Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates  
provided by the European Pollutant Release and Transfer Register [2]. The images
contain scenes of power stations, some of which are actively
emitting smoke plumes.

This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images.

Description
 

Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)
.

This repository contains a total of 2131 images. This
repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation
labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images.

Content

The following files are contained in this repository:

• README.md - this file
• images.zip [2.0GB] - contains 2131 GeoTIFF images
• segmentation_labels.zip [1.5MB] - contains 2131 JSON files
• labels.csv [310KB] - contains additional labels for each image:
  • Generation output rate [4],[5]
  • Country
  • Type of fired fuel
  • Latitude and longitude of the power plant
  • Concurrent weather information (temperature, humidity and wind vector)

Acknowledgement

If you use this data set, please cite our publication:

    Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,
    "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",
    Tackling Climate Change with Machine Learning workshop at NeurIPS 2021.

Please refer to this publication for additional information on the data set.

The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation.

Author

Joëlle Hanna

University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch

References
 

[1]: https://earth.esa.int/web/sentinel/missions/sentinel-2
[2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial
[3]: https://labelstud.io/
[4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show
[5]: https://doi.org/10.5281/zenodo.3574566