  Recent advances in deep learning have made it possible to quantify urban
metrics at fine resolution, and over large extents using street-level images.
Here, we focus on measuring urban tree cover using Google Street View (GSV)
images. First, we provide a small-scale labelled validation dataset and propose
standard metrics to compare the performance of automated estimations of street
tree cover using GSV. We apply state-of-the-art deep learning models, and
compare their performance to a previously established benchmark of an
unsupervised method. Our training procedure for deep learning models is novel;
we utilize the abundance of openly available and similarly labelled
street-level image datasets to pre-train our model. We then perform additional
training on a small training dataset consisting of GSV images. We find that
deep learning models significantly outperform the unsupervised benchmark
method. Our semantic segmentation model increased mean intersection-over-union
(IoU) from 44.10% to 60.42% relative to the unsupervised method and our
end-to-end model decreased Mean Absolute Error from 10.04% to 4.67%. We also
employ a recently developed method called gradient-weighted class activation
map (Grad-CAM) to interpret the features learned by the end-to-end model. This
technique confirms that the end-to-end model has accurately learned to identify
tree cover area as key features for predicting percentage tree cover. Our paper
provides an example of applying advanced deep learning techniques on a
large-scale, geo-tagged and image-based dataset to efficiently estimate
important urban metrics. The results demonstrate that deep learning models are
highly accurate, can be interpretable, and can also be efficient in terms of
data-labelling effort and computational resources.
