Characterization of Industrial Smoke Plumes from Remote Sensing Data

<p><strong>Characterization of Industrial Smoke Plumes from Remote Sensing Data</strong><br> &nbsp;</p> <p>This data set contains imaging data acquired by ESA&#39;s <a href="https://earth.esa.int/web/sentinel/missions/sentinel-2">Sentinel-2 Earth-observing satellite constellation</a> for a sample of industrial sites that were picked based on emission information provided by the <a href="https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial">European Pollutant Release and Transfer Register</a>. The images contain scenes of mainly industrial sites, some of which are actively emitting smoke plumes.</p> <p>This data set was created to investigate whether it would be possible to train a deep learning model to automatically identify and segment smoke plumes from remote sensing image data. Please refer to the acknowledgements section for more on information on this project.</p> <p><br> <strong>Description</strong></p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands and georeferencing information, and has a shape of 120 x 120 pixels (corresponding to a square area with an edge length of 1.2 km on the ground). The bands are extracted from Sentinel-2 Level-2A products, except for band 10, which has been extracted from the<br> corresponding Level-1C product (this band has not been utilized in the underlying work).</p> <p>This repository contains a total of 21,350 images. Based on manual annotation, the image sample was split into a sample of 3,750 <em>positive</em> images that contain industrial smoke plumes, and 17,600 <em>negative</em> images that do not contain smoke plumes. Furthermore, this repository contains a collection of JSON files that hold manual segmentation labels for smoke plumes present in 1,437 images. Segmentation labels were generated using <a href="http://https://labelstud.io/">label-studio</a>. Please note that polygon edge coordinates have to be scaled by a factor of 1.2 to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following tarballs are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.tar.gz [6.0GB] - contains 21,350 GeoTIFF images</li> <li>segmentation_labels.tar.gz [350KB] - contains 1,437 JSON files</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Mommert, M., Sigel, M., Neuhausler, M., Scheibenreif, L., Borth, D., &quot;Characterization of Industrial Smoke Plumes from Remote Sensing Data&quot;, Tackling Climate Change with Machine Learning workshop at NeurIPS 2020.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at <a href="https://github.com/HSG-AIML/IndustrialSmokePlumeDetection">github</a>.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2019, processed by ESA.</p> <p>&nbsp;</p> <p><strong>Responsible Author</strong></p> <p>Michael Mommert<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> michael.mommert ( at ) unisg.ch</p>