Automatic Quantification and Visualization of Street Trees

TL;DR

This paper presents a comprehensive framework for automatic street tree quantification and visualization using a carefully designed data collection setup, novel annotation procedures, and object detection techniques, achieving high accuracy and practical utility.

Contribution

It introduces a new dataset, annotation method, and a counting algorithm for street trees, along with a visualization framework and novel evaluation metrics.

Findings

Tree detection mAP of 83.74% on test images

Tree Count Density Classification Accuracy of 96.77% on test videos

Counting performance close to human level

Abstract

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.