Estimating Chicago’s tree cover and canopy height using multi-spectral satellite imagery (Papers Track)

John Francis (University College London)

Paper PDF Slides PDF Recorded Talk NeurIPS 2022 Poster Topia Link Cite
Cities & Urban Planning Ecosystems & Biodiversity Public Policy Computer Vision & Remote Sensing

Abstract

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-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.

Recorded Talk (direct link)

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