Predicting urban tree cover from incomplete point labels and limited
  background information
Hui Zhang Ankit Kariryaa Venkanna Babu Guthula Christian Igel Stefan Oehmcke
Trees inside cities are important for the urban microclimate, contributing
positively to the physical and mental health of the urban dwellers. Despite
their importance, often only limited information about city trees is available.
Therefore in this paper, we propose a method for mapping urban trees in
high-resolution aerial imagery using limited datasets and deep learning. Deep
learning has become best-practice for this task, however, existing approaches
rely on large and accurately labelled training datasets, which can be difficult
and expensive to obtain. However, often noisy and incomplete data may be
available that can be combined and utilized to solve more difficult tasks than
those datasets were intended for. This paper studies how to combine accurate
point labels of urban trees along streets with crowd-sourced annotations from
an open geographic database to delineate city trees in remote sensing images, a
task which is challenging even for humans. To that end, we perform semantic
segmentation of very high resolution aerial imagery using a fully convolutional
neural network. The main challenge is that our segmentation maps are sparsely
annotated and incomplete. Small areas around the point labels of the street
trees coming from official and crowd-sourced data are marked as foreground
class. Crowd-sourced annotations of streets, buildings, etc. define the
background class. Since the tree data is incomplete, we introduce a masking to
avoid class confusion. Our experiments in Hamburg, Germany, showed that the
system is able to produce tree cover maps, not limited to trees along streets,
without providing tree delineations. We evaluated the method on manually
labelled trees and show that performance drastically deteriorates if the open
geographic database is not used.