  Assessing the number of street trees is essential for evaluating urban
greenery and can help municipalities employ solutions to identify tree-starved
streets. It can also help identify roads with different levels of deforestation
and afforestation over time. Yet, there has been little work in the area of
street trees quantification. This work first explains a data collection setup
carefully designed for counting roadside trees. We then describe a unique
annotation procedure aimed at robustly detecting and quantifying trees. We work
on a dataset of around 1300 Indian road scenes annotated with over 2500 street
trees. We additionally use the five held-out videos covering 25 km of roads for
counting trees. We finally propose a street tree detection, counting, and
visualization framework using current object detectors and a novel yet simple
counting algorithm owing to the thoughtful collection setup. We find that the
high-level visualizations based on the density of trees on the routes and
Kernel Density Ranking (KDR) provide a quick, accurate, and inexpensive way to
recognize tree-starved streets. We obtain a tree detection mAP of 83.74% on the
test images, which is a 2.73% improvement over our baseline. We propose Tree
Count Density Classification Accuracy (TCDCA) as an evaluation metric to
measure tree density. We obtain TCDCA of 96.77% on the test videos, with a
remarkable improvement of 22.58% over baseline, and demonstrate that our
counting module's performance is close to human level. Source code:
https://github.com/iHubData-Mobility/public-tree-counting.
