Quantifying Urban Canopy Cover with Deep Convolutional Neural Networks
This work addresses the need for scalable and accurate urban greenery estimation, enabling analysis across 22 cities for public policy and research.
The paper tackled the problem of quantifying urban canopy cover by training deep convolutional neural networks on self-driving car datasets, achieving a reduction in mean absolute error for the Green View Index from 10.1% to 4.67%.
Urban canopy cover is important to mitigate the impact of climate change. Yet, existing quantification of urban greenery is either manual and not scalable, or use traditional computer vision methods that are inaccurate. We train deep convolutional neural networks (DCNNs) on datasets used for self-driving cars to estimate urban greenery instead, and find that our semantic segmentation and direct end-to-end estimation method are more accurate and scalable, reducing mean absolute error of estimating the Green View Index (GVI) metric from 10.1% to 4.67%. With the revised DCNN methods, the Treepedia project was able to scale and analyze canopy cover in 22 cities internationally, sparking interest and action in public policy and research fields.