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A major challenge in training deep learning models is the lack of highquality and complete datasets. In the paper, we present a masking approach fortraining deep learning models from a publicly available but incomplete dataset.For example, city of Hamburg, Germany maintains a list of trees along theroads, but this dataset does not contain any information about trees in privatehomes and parks. To train a deep learning model on such a dataset, we mask thestreet trees and aerial images with the road network. Road network used forcreating the mask is downloaded from OpenStreetMap, and it marks the area wherethe training data is available. The mask is passed to the model as one of theinputs and it also coats the output. Our model learns to successfully predicttrees only in the masked region with 78.4% accuracy.

MaskIt: Masking for efficient utilization of incomplete public datasets for training deep learning models