A simple, strong baseline for building damage detection on the xBD
  dataset
Sebastian Gerard Paul Borne-Pons Josephine Sullivan
We construct a strong baseline method for building damage detection by
starting with the highly-engineered winning solution of the xView2 competition,
and gradually stripping away components. This way, we obtain a much simpler
method, while retaining adequate performance. We expect the simplified solution
to be more widely and easily applicable. This expectation is based on the
reduced complexity, as well as the fact that we choose hyperparameters based on
simple heuristics, that transfer to other datasets. We then re-arrange the
xView2 dataset splits such that the test locations are not seen during
training, contrary to the competition setup. In this setting, we find that both
the complex and the simplified model fail to generalize to unseen locations.
Analyzing the dataset indicates that this failure to generalize is not only a
model-based problem, but that the difficulty might also be influenced by the
unequal class distributions between events.
  Code, including the baseline model, is available under
https://github.com/PaulBorneP/Xview2_Strong_Baseline