Articles | Volume 19
Adv. Sci. Res., 19, 13–27, 2022
https://doi.org/10.5194/asr-19-13-2022
Adv. Sci. Res., 19, 13–27, 2022
https://doi.org/10.5194/asr-19-13-2022
 
02 May 2022
02 May 2022

Using machine learning to produce a cost-effective national building height map of Ireland to categorise local climate zones

Eoghan Keany et al.

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Cited articles

Alcoforado, M. J., Lopes, A., Alves, E. D. L., and Canário, P.: Lisbon Heat Island, Finisterra, 49, 61–80, https://doi.org/10.18055/Finis6456, 2014. a
Alexander, P. J. and Mills, G.: Local climate classification and Dublin's urban heat island, Atmosphere, 5, 755–774, https://doi.org/10.3390/atmos5040755, 2014. a
Bessardon, G. and Gleeson, E.: Using the best available physiography to improve weather forecasts for Ireland, in: Challenges in High Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, Lyngby, https://presentations.copernicus.org/EMS2019/EMS2019-702_presentation.pdf (last access: 28 March 2022), 2019. a
Cai, M., Ren, C., Xu, Y., Lau, K. K. L., and Wang, R.: Investigating the relationship between local climate zone and land surface temperature using an improved WUDAPT methodology – A case study of Yangtze River Delta, China, Urban Clim., 24, 485–502, https://doi.org/10.1016/J.UCLIM.2017.05.010, 2018. a
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Short summary
This work used machine learning to produce the first open source building height map of Ireland. This map is intended to more accurately determine Local Climate Zones for use in the underlying physiography dataset in the HARMONIE AROME numerical weather prediction model.