Automatic Quantification and Visualization of Street Trees

by   Arpit Bahety, et al.

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 test images, which is a 2.73 Count Density Classification Accuracy (TCDCA) as an evaluation metric to measure tree density. We obtain TCDCA of 96.77 remarkable improvement of 22.58 counting module's performance is close to human level. Source code:


page 1

page 3

page 5

page 6

page 7

page 8


Geocoding of trees from street addresses and street-level images

We introduce an approach for updating older tree inventories with geogra...

Automatic Large Scale Detection of Red Palm Weevil Infestation using Aerial and Street View Images

The spread of the Red Palm Weevil has dramatically affected date growers...

From Google Maps to a Fine-Grained Catalog of Street trees

Up-to-date catalogs of the urban tree population are important for munic...

Learning To Count Everything

Existing works on visual counting primarily focus on one specific catego...

Towards automatic extraction and validation of on-street parking spaces using park-out events data

This article proposes two different approaches to automatically create a...

Advancing Early Detection of Virus Yellows: Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields

Aphids are efficient vectors to transmit virus yellows in sugar beet fie...

UPDExplainer: an Interpretable Transformer-based Framework for Urban Physical Disorder Detection Using Street View Imagery

Urban Physical Disorder (UPD), such as old or abandoned buildings, broke...

Please sign up or login with your details

Forgot password? Click here to reset