  The full loss of thrust of an aircraft requires fast and reliable decisions
of the pilot. If no published landing field is within reach, an emergency
landing field must be selected. The choice of a suitable emergency landing
field denotes a crucial task to avoid unnecessary damage of the aircraft, risk
for the civil population as well as the crew and all passengers on board.
Especially in case of instrument meteorological conditions it is indispensable
to use a database of suitable emergency landing fields. Thus, based on public
available digital orthographic photos and digital surface models, we created
various datasets with different sample sizes to facilitate training and testing
of neural networks. Each dataset consists of a set of data layers. The best
compositions of these data layers as well as the best performing transfer
learning models are selected. Subsequently, certain hyperparameters of the
chosen models for each sample size are optimized with Bayesian and Bandit
optimization. The hyperparameter tuning is performed with a self-made
Kubernetes cluster. The models outputs were investigated with respect to the
input data by the utilization of layer-wise relevance propagation. With
optimized models we created an ensemble model to improve the segmentation
performance. Finally, an area around the airport of Arnsberg in North
Rhine-Westphalia was segmented and emergency landing fields are identified,
while the verification of the final approach's obstacle clearance is left
unconsidered. These emergency landing fields are stored in a PostgreSQL
database.
