  Climate change has increased the severity and frequency of weather disasters
all around the world. Flood inundation mapping based on earth observation data
can help in this context, by providing cheap and accurate maps depicting the
area affected by a flood event to emergency-relief units in near-real-time.
Building upon the recent development of the Sen1Floods11 dataset, which
provides a limited amount of hand-labeled high-quality training data, this
paper evaluates the potential of five traditional machine learning approaches
such as gradient boosted decision trees, support vector machines or quadratic
discriminant analysis. By performing a grid-search-based hyperparameter
optimization on 23 feature spaces we can show that all considered classifiers
are capable of outperforming the current state-of-the-art neural network-based
approaches in terms of total IoU on their best-performing feature spaces. With
total and mean IoU values of 0.8751 and 0.7031 compared to 0.70 and 0.5873 as
the previous best-reported results, we show that a simple gradient boosting
classifier can significantly improve over deep neural network based approaches,
despite using less training data. Furthermore, an analysis of the regional
distribution of the Sen1Floods11 dataset reveals a problem of spatial
imbalance. We show that traditional machine learning models can learn this bias
and argue that modified metric evaluations are required to counter artifacts
due to spatial imbalance. Lastly, a qualitative analysis shows that this
pixel-wise classifier provides highly-precise surface water classifications
indicating that a good choice of a feature space and pixel-wise classification
can generate high-quality flood maps using optical and SAR data. We make our
code publicly available at:
https://github.com/DFKI-Earth-And-Space-Applications/Flood_Mapping_Feature_Space_Importance
