Journal
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)
Volume -, Issue -, Pages 21126-21135Publisher
IEEE COMPUTER SOC
DOI: 10.1109/CVPR52688.2022.02048
Keywords
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Funding
- Humboldt Foundation through the Sofja Kovalevskaja Award
- Helmholtz AI [ZT-I-PF-5-01]
- Helmholtz Association under the joint research school Munich School for Data Science -MUDS
- German Federal Ministry for Economic Affairs and Energy (BMWi) under the grant DynamicEarthNet [50EE2005]
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Researchers propose the DynamicEarthNet dataset, which contains daily satellite observations and monthly pixel-wise semantic segmentation labels for 75 selected areas of interest around the world. They compare different algorithms and introduce a new evaluation metric, SCS, for time-series semantic change segmentation.
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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