Using street view imagery and deep learning for estimating the health of urban forests

Healthy urban forest comprising of diverse trees and shrubs play a crucial role in mitigating climate change. They provide several key advantages such as providing shade for energy conservation, and intercepting rainfall to reduce flood runoff and soil erosion. Traditional approaches for monitoring the health of urban forests require instrumented inspection techniques often involving a high amount of human labor and subjective evaluations. As a result, they are not scalable for the cities which lack extensive resources. Recent approaches involving multi-spectral imagery data based on terrestrial sensing and satellites, are constrained respectively with challenges related to dedicated deployments and limited spatial resolutions. In this work, we propose an alternative approach for monitoring the urban forests using simplified inputs: street view imagery, tree inventory data and meteorological conditions. We propose to use image-to-image translation network to estimate two urban forest health parameters, namely, NDVI and CTD. Finally, we aim to compare the results with ground truth data using an onsite campaign utilizing handheld multi-spectral and thermal imaging sensors. With the advent and expansion of street view imagery platforms such as Google Street View, this approach should enable effective management of urban forests for the authorities in cities at scale.