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

• URL: http://arxiv.org/abs/2203.12560v1
• Date: Wed, 23 Mar 2022 17:22:22 GMT
• Title: DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation
• Authors: Aysim Toker, Lukas Kondmann, Mark Weber, Marvin Eisenberger, Andr\'es Camero, Jingliang Hu, Ariadna Pregel Hoderlein, \c{C}a\u{g}lar \c{S}enaras, Timothy Davis, Daniel Cremers, Giovanni Marchisio, Xiao Xiang Zhu, Laura Leal-Taix\'e
• Abstract summary: We propose the DynamicEarthNet dataset that consists of daily, multi-spectral satellite observations of 75 selected areas of interest. These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover classes.
• Score: 43.72597365517224
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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