2022/12/09 by John Francis, Stephen Law, Francis, John +1
Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Land Use and Ecosystem Services #Remote Sensing in Agriculture #Urban Green Space and Health #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.05061
openalex publication_date 2022/12/09 · openalex created_date 2022/12/26 · openalex updated_date 2026/07/28
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.