Information on urban tree canopies is fundamental to mitigating climate change as well as improving quality of life. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truthand then trains a multi-task machine learning model to generate reliable estimatesof tree cover and canopy height in urban areas using multi-source multi-spectralsatellite imagery for the case study of Chicago.