  We introduce a novel deep learning method for detection of individual trees
in urban environments using high-resolution multispectral aerial imagery. We
use a convolutional neural network to regress a confidence map indicating the
locations of individual trees, which are localized using a peak finding
algorithm. Our method provides complete spatial coverage by detecting trees in
both public and private spaces, and can scale to very large areas. We performed
a thorough evaluation of our method, supported by a new dataset of over 1,500
images and almost 100,000 tree annotations, covering eight cities, six climate
zones, and three image capture years. We trained our model on data from
Southern California, and achieved a precision of 73.6% and recall of 73.3%
using test data from this region. We generally observed similar precision and
slightly lower recall when extrapolating to other California climate zones and
image capture dates. We used our method to produce a map of trees in the entire
urban forest of California, and estimated the total number of urban trees in
California to be about 43.5 million. Our study indicates the potential for deep
learning methods to support future urban forestry studies at unprecedented
scales.
