Impact Assessment of Missing Data in Model Predictions for Earth
  Observation Applications
Francisco Mena Diego Arenas Marcela Charfuelan Marlon Nuske Andreas Dengel
Earth observation (EO) applications involving complex and heterogeneous data
sources are commonly approached with machine learning models. However, there is
a common assumption that data sources will be persistently available. Different
situations could affect the availability of EO sources, like noise, clouds, or
satellite mission failures. In this work, we assess the impact of missing
temporal and static EO sources in trained models across four datasets with
classification and regression tasks. We compare the predictive quality of
different methods and find that some are naturally more robust to missing data.
The Ensemble strategy, in particular, achieves a prediction robustness up to
100%. We evidence that missing scenarios are significantly more challenging in
regression than classification tasks. Finally, we find that the optical view is
the most critical view when it is missing individually.