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
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
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.
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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