  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.
