Urban Tree Detection and Species Classification Using Aerial Imagery

Maktab Dar Oghaz, Mahdi and Babu Saheer, Lakshmi and Zarrin, Javad (2022) Urban Tree Detection and Species Classification Using Aerial Imagery. In: SAI Computing Conference 2022, Online.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...

Abstract

Trees are essential for climate change adaptation or even mitigation to some extent. To leverage their potential, effective forest and urban tree management is required. Automated tree detection, localisation, and species classification are crucial to any forest and urban tree management plan. Over the last decade, many studies aimed at tree species classification using aerial imagery yet due to several environmental challenges results were sub-optimal. This study aims to contribute to this domain by first, generating a labelled tree species dataset using Google Maps static API to supply aerial images and Trees In Camden inventory to supply species information, GPS coordinates (Latitude and Longitude), and tree diameter. Furthermore, this study investigates how state-of-the-art deep Convolutional Neural Network models including VGG19, ResNet50, DenseNet121, and InceptionV3 can handle the species classification problem of the urban trees using aerial images. Experimental results show our best model, InceptionV3 achieves an average accuracy of 73.54 over 6 tree species.

Item Type: Conference or Workshop Item (Paper)
Keywords: Urban Tree Detection, Convolutional Neural Network, Aerial Imagery
Faculty: Faculty of Science & Engineering
Depositing User: Lisa Blanshard
Date Deposited: 13 Jun 2022 09:39
Last Modified: 28 Jul 2022 10:33
URI: https://arro.anglia.ac.uk/id/eprint/707681

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