DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

I. Demir, K. Koperski, D. Lindenbaum, G. Pang, J. Huang, S. Basu, F. Hughes, D. Tuia, R. Raska

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

43 Citations (Scopus)

Abstract

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images (Figure 1). Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2018. We observed that satellite imagery is a rich and structured source of information, yet it is less investigated than everyday images by computer vision researchers. However, bridging modern computer vision with remote sensing data analysis could have critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research. Keeping such bridging objective in mind, DeepGlobe aims to bring together researchers from different domains to raise awareness of remote sensing in the computer vision community and vice-versa. We aim to improve and evaluate state-of-the-art satellite image understanding approaches, which can hopefully serve as reference benchmarks for future research in the same topic. In this paper, we analyze characteristics of each dataset, define the evaluation criteria of the competitions, and provide baselines for each task.
Original languageEnglish
Title of host publicationProceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018
PublisherIEEE
Pages17200-17209
ISBN (Electronic)9781538661000
ISBN (Print)9781538661017
DOIs
Publication statusPublished - 2018
Event2018 Conference on Computer Vision and Pattern Recognition - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018

Conference

Conference2018 Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR2018
CountryUnited States
CitySalt Lake City
Period18/06/1822/06/18

Fingerprint

Computer vision
Earth (planet)
Satellites
Image understanding
Remote sensing
Urban planning
Satellite imagery
Climate change

Cite this

Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., ... Raska, R. (2018). DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. In Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018 (pp. 17200-17209). IEEE. https://doi.org/10.1109/CVPRW.2018.00031
Demir, I. ; Koperski, K. ; Lindenbaum, D. ; Pang, G. ; Huang, J. ; Basu, S. ; Hughes, F. ; Tuia, D. ; Raska, R. / DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018. IEEE, 2018. pp. 17200-17209
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Demir, I, Koperski, K, Lindenbaum, D, Pang, G, Huang, J, Basu, S, Hughes, F, Tuia, D & Raska, R 2018, DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. in Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018. IEEE, pp. 17200-17209, Salt Lake City, United States, 18/06/18. https://doi.org/10.1109/CVPRW.2018.00031

DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. / Demir, I.; Koperski, K.; Lindenbaum, D.; Pang, G.; Huang, J.; Basu, S.; Hughes, F.; Tuia, D.; Raska, R.

Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018. IEEE, 2018. p. 17200-17209.

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

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Demir I, Koperski K, Lindenbaum D, Pang G, Huang J, Basu S et al. DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. In Proceedings 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2018. IEEE. 2018. p. 17200-17209 https://doi.org/10.1109/CVPRW.2018.00031