Using street view imagery and deep generative modeling for estimating the health of urban forests
This work addresses scalable urban forest management for city authorities, offering an alternative to labor-intensive traditional methods, though it is incremental as it builds on existing image-to-image translation techniques.
The authors tackled the problem of monitoring urban forest health by proposing a method that uses street view imagery, tree inventory data, and meteorological conditions with image-to-image translation networks to estimate NDVI and CTD parameters, achieving results comparable to ground truth data from an onsite campaign.
Healthy urban forests 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 cities which lack extensive resources. Recent approaches involving multi-spectral imaging 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 networks to estimate two urban forest health parameters, namely, NDVI and CTD. Finally, we aim to compare the generated 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 and Mapillary, this approach should enable effective management of urban forests for the authorities in cities at scale.