Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery
This provides essential data for urban tree planting initiatives to mitigate climate change and improve quality of life in cities, but it is incremental as it applies existing methods to a new case study.
The authors tackled the problem of lacking up-to-date data on urban tree canopies by developing a pipeline that uses LiDAR as ground-truth and a multi-task machine learning model to estimate tree cover and canopy height in Chicago from multi-spectral satellite imagery, achieving reliable estimates.
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.