AN INVESTIGATION OF THE INFLUENCES OF TRAINING DATA PROPERTIES ON AERIAL IMAGE SEGMENTATION

Abstract. While numerous studies are being conducted to improve neural network performance for image segmentation, studies on the impact of training data in terms of data quality, bias and labeling noise are comparatively scarce. When opening state of the art algorithms to a large and varying dataset, they do not achieve the same results as under optimal and controlled conditions due to a mismatch of the data used for training and the data that is to be predicted. This paper presents an approach to show the influences of diverging image properties such as scale, contrast, brightness and saturation between the training data of a model and data that is to be predicted. For this purpose, a U-Net is trained to segment buildings in aerial images. It was found that while changes in brightness have a strong effect on precision, recall and F-score, a change in saturation does not have too much or even positive effect on segmentation. In general, however, it can be said that any differences between training and prediction data have a negative effect on segmentation results.