Law, Wai Pan Stephen;
Francis, John;
(2022)
Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery.
In:
Tackling Climate Change with Machine Learning: workshop at NeurIPS 2022.
(pp. p. 44).
NeurIPS
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery |
| Event: | Neural Information Processing Systems Climate Change Workshop 2022 |
| Location: | online |
| Dates: | 2 Dec 2022 - 16 Dec 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.climatechange.ai/papers/neurips2022/44 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Geography |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10183511 |
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