Urban forests provide significant benefits to urban societies. However, planning and maintaining these forests is expensive, and monitoring efforts are currently based on costly and infrequent tree censuses built by human experts. To explore automating urban forest mapping we introduce a new large-scale dataset that joins public tree censuses from 23 cities with a large collection of street level and aerial imagery. Our Auto Arborist dataset contains over 2.5M trees and >300 genera. We introduce baseline results on our dataset across modalities as well as metrics for the detailed analysis of generalization with respect to geographic distribution shifts.
