  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.
