Urban tree detection and species classification using aerial imagery

Mahdi Maktab Dar Oghaz*, Lakshmi Babu Saheer, Javad Zarrin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationIntelligent Computing
Subtitle of host publicationProceedings of the 2022 Computing Conference
EditorsKohei Arai
Place of PublicationCham
PublisherSpringer International Publishing AG
Number of pages15
ISBN (Electronic)9783031104640
ISBN (Print)9783031104633
Publication statusPublished - 7 Jul 2022
Externally publishedYes
EventComputing Conference 2022 - Virtual, United Kingdom
Duration: 14 Jul 202215 Jul 2022

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceComputing Conference 2022
Country/TerritoryUnited Kingdom
Internet address


  • Urban tree detection
  • Convolutional neural network
  • Aerial imagery


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