Urban Tree Species Classification Using Aerial Imagery
This work addresses urban tree management for sustainability applications, but it is incremental as it applies existing methods to a new dataset.
This study tackled the problem of automated urban tree species classification from aerial imagery by developing a pipeline to create a labeled dataset using Google Maps images and testing deep CNN models like VGG and ResNet, achieving an average accuracy of 60% across 6 species.
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.