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