DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation

Toker A, Kondmann L, Weber M, Eisenberger M, Camero A, Hu J, Hoderlein AP, Senaras C, Davis T, Cremers D, Marchisio G, Zhu XX, Leal-Taixe L (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1999

Publisher: IEEE Computer Society

Book Volume: 2022-June

Pages Range: 21126-21135

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: New Orleans, LA, USA

ISBN: 9781665469463

DOI: 10.1109/CVPR52688.2022.02048

Abstract

Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires both time-series data and pixel-wise segmentations. To that end, we propose the DynamicEarthNet dataset that consists of daily, multi-spectral satellite observations of 75 selected areas of interest distributed over the globe with imagery from Planet Labs. These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes. DynamicEarthNet is the first dataset that provides this unique combination of daily measurements and high-quality labels. In our experiments, we compare several established baselines that either utilize the daily observations as additional training data (semi-supervised learning) or multiple observations at once (spatio-temporal learning) as a point of reference for future research. Finally, we propose a new evaluation metric SCS that addresses the specific challenges associated with time-series semantic change segmentation. The data is available at: https://mediatum.ub.tum.de/1650201.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) / German Aerospace Center DE Germany (DE) Planet Labs PBC US United States (USA) (US)

How to cite

APA:

Toker, A., Kondmann, L., Weber, M., Eisenberger, M., Camero, A., Hu, J.,... Leal-Taixe, L. (2022). DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 21126-21135). New Orleans, LA, USA: IEEE Computer Society.

MLA:

Toker, Aysim, et al. "DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA IEEE Computer Society, 2022. 21126-21135.

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