Urban tree canopy (UTC) is commonly used to assess urban forest extent and has traditionally been estimated using photointerpretation and human intelligence (HI). Artificial intelligence (AI) models may provide a less labor-intensive method to estimate urban tree canopy. However, studies on how human intelligence and artificial intelligence estimation methods compare are limited. We investigated how human intelligence and artificial intelligence compare with estimates of urban tree canopy and other landcovers. Change in urban tree canopy between two time periods and an assessment agreement accuracy also occurred. We found a statistically significant (p p = 0.72) was found between the two methods, suggesting other regional factors are important for training the AI system. Urban tree canopy also increased (p < 0.001) between two time periods (2013 to 2018) and two assessors could detect the same sample point over 90 % of the time.
