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The full loss of thrust of an aircraft requires fast and reliable decisionsof the pilot. If no published landing field is within reach, an emergencylanding field must be selected. The choice of a suitable emergency landingfield denotes a crucial task to avoid unnecessary damage of the aircraft, riskfor the civil population as well as the crew and all passengers on board.Especially in case of instrument meteorological conditions it is indispensableto use a database of suitable emergency landing fields. Thus, based on publicavailable digital orthographic photos and digital surface models, we createdvarious datasets with different sample sizes to facilitate training and testingof neural networks. Each dataset consists of a set of data layers. The bestcompositions of these data layers as well as the best performing transferlearning models are selected. Subsequently, certain hyperparameters of thechosen models for each sample size are optimized with Bayesian and Banditoptimization. The hyperparameter tuning is performed with a self-madeKubernetes cluster. The models outputs were investigated with respect to theinput data by the utilization of layer-wise relevance propagation. Withoptimized models we created an ensemble model to improve the segmentationperformance. Finally, an area around the airport of Arnsberg in NorthRhine-Westphalia was segmented and emergency landing fields are identified,while the verification of the final approach's obstacle clearance is leftunconsidered. These emergency landing fields are stored in a PostgreSQLdatabase.

Ensemble Transfer Learning for Emergency Landing Field Identification on Moderate Resource Heterogeneous Kubernetes Cluster