Urban Tree Species Classification Using Aerial Imagery

Waters, Emily and Oghaz, Mahdi M. and Babu Saheer, Lakshmi (2021) Urban Tree Species Classification Using Aerial Imagery. In: International Conference on Machine Learning 2021, Online.

[img]
Preview
Text
Published Version
Available under the following license: Creative Commons Attribution Non-commercial No Derivatives.

Download (5MB) | Preview
Official URL: https://www.climatechange.ai/papers/icml2021/3

Abstract

Urban trees help regulate temperature, reduce energy consumption, improve urban air quality, reduce wind speeds, and mitigating the urban heat island effect. Urban trees also play a key role in climate change mitigation and global warming by capturing and storing atmospheric carbon-dioxide which is the largest contributor to greenhouse gases. Automated tree detection and species classification using aerial imagery can be a powerful tool for sustainable forest and urban tree management. Hence, This study first offers a pipeline for generating labelled dataset of urban trees using Google Map's aerial images and then investigates how state of the art deep Convolutional Neural Network models such as VGG and ResNet handle the classification problem of urban tree aerial images under different parameters. Experimental results show our best model achieves an average accuracy of 60% over 6 tree species.

Item Type: Conference or Workshop Item (Paper)
Faculty: Faculty of Science & Engineering
Depositing User: Lisa Blanshard
Date Deposited: 05 Oct 2021 11:29
Last Modified: 26 Apr 2022 11:33
URI: https://arro.anglia.ac.uk/id/eprint/707005

Actions (login required)

Edit Item Edit Item