Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery

Abstract

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.

4 pages, 4 figures, Submitted to Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022