  With the emergence of deep learning in the last years, new opportunities
arose in Earth observation research. Nevertheless, they also brought with them
new challenges. The data-hungry training processes of deep learning models
demand large, resource expensive, annotated data sets and partly replaced
knowledge-driven approaches so that model behaviour and the final prediction
process became a black box. The proposed SyntEO approach enables Earth
observation researchers to automatically generate large deep learning ready
data sets by merging existing and procedural data. SyntEO does this by
including expert knowledge in the data generation process in a highly
structured manner to control the automatic image and label generation by
employing an ontology. In this way, fully controllable experiment environments
are set up, which support insights in the model training on the synthetic data
sets. Thus, SyntEO makes the learning process approachable, which is an
important cornerstone for explainable machine learning. We demonstrate the
SyntEO approach by predicting offshore wind farms in Sentinel-1 images on two
of the worlds largest offshore wind energy production sites. The largest
generated data set has 90,000 training examples. A basic convolutional neural
network for object detection, that is only trained on this synthetic data,
confidently detects offshore wind farms by minimising false detections in
challenging environments. In addition, four sequential data sets are generated,
demonstrating how the SyntEO approach can precisely define the data set
structure and influence the training process. SyntEO is thus a hybrid approach
that creates an interface between expert knowledge and data-driven image
analysis.
