  Healthy urban greenery is a fundamental asset to mitigate climate change
phenomena such as extreme heat and air pollution. However, urban trees are
often affected by abiotic and biotic stressors that hamper their functionality,
and whenever not timely managed, even their survival. While the current
greenery inspection techniques can help in taking effective measures, they
often require a high amount of human labor, making frequent assessments
infeasible at city-wide scales. In this paper, we present GreenScan, a
ground-based sensing system designed to provide health assessments of urban
trees at high spatio-temporal resolutions, with low costs. The system utilises
thermal and multi-spectral imaging sensors fused using a custom computer vision
model in order to estimate two tree health indexes. The evaluation of the
system was performed through data collection experiments in Cambridge, USA.
Overall, this work illustrates a novel approach for autonomous mobile
ground-based tree health monitoring on city-wide scales at high temporal
resolutions with low-costs.
