This is the dataset repository of the paper: Toker, A.*, Kondmann, L.*, Weber, M., Eisenberger, M., Camero, A., Hu, J., Pregel Hoderlein, A., Senaras, C., Davis, T., Cremers, D., Marchisio, G°., Zhu, X.X.°, Leal-Taixé, L.°: DynamicEarthNet: Daily multi-spectral satellite dataset for semantic change segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
The DynamicEarthNet dataset contains daily Planet Fusion imagery with monthly land cover classes for 75 areas across the globe over two years. The seven land cover classes were manually annotated in a temporally consistent way. Sentinel 2 imagery is also provided. This dataset is the first large-scale multi-class and multi-temporal change detection benchmark and we hope it will foster a new wave of multi-temporal research in Earth Observation as well as Computer Vision. A detailed description is available in the paper.      «

This is the dataset repository of the paper: Toker, A.*, Kondmann, L.*, Weber, M., Eisenberger, M., Camero, A., Hu, J., Pregel Hoderlein, A., Senaras, C., Davis, T., Cremers, D., Marchisio, G°., Zhu, X.X.°, Leal-Taixé, L.°: DynamicEarthNet: Daily multi-spectral satellite dataset for semantic change segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
The DynamicEarthNet dataset contains daily Planet Fusion imagery with monthly land cover...     »