Information on urban tree canopies is fundamental to mitigating climate
change [1] as well as improving quality of life [2]. 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-truth and then trains a multi-task machine learning model
to generate reliable estimates of tree cover and canopy height in urban areas
using multi-source multi-spectral satellite imagery for the case study of
Chicago.Comment: 4 pages, 4 figures, Submitted to Tackling Climate Change with Machine
Learning: workshop at NeurIPS 202