# Quantifying Urban Canopy Cover with Deep Convolutional Neural Networks Source: [Quantifying Urban Canopy Cover with Deep Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:59:21.344Z Authors: Bill Cai, Xiaojiang Li, Carlo Ratti Published: 2019-12-03T17:14:53.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()