Machine learning algorithms for building height estimations using ICESat-2/ATLAS and Airborne LiDAR data
Author:
Funder
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12145-024-01429-w.pdf
Reference53 articles.
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2. Alahmadi M, Atkinson P, Martin D (2013) Estimating the spatial distribution of the population of Riyadh, Saudi Arabia using remotely sensed built land cover and height data. Comput Environ Urban Syst 41:167–176. https://doi.org/10.1016/j.compenvurbsys.2013.06.002
3. Antonarakis AS, Richards KS, Brasington J (2008) Object-based land cover classification using airborne LiDAR. Remote Sens Environ 112(6):2988–2998. https://doi.org/10.1016/j.rse.2008.02.004
4. Aydınoglu AÇ, Bovkır R, Çölkesen İ (2023) Using machine learning algorithms in mass valuation and comparing the effects of geographical/nongeographical features on prediction accuracy. J Geodesy Geoinformation 10(1):63–83. https://doi.org/10.9733/JGG.2023R0005.T
5. Bonczak B, Kontokosta CE (2019) Large-scale parameterization of 3D building morphology in complex urban landscapes using aerial LiDAR and city administrative data. Comput Environ Urban Syst 73:126–142. https://doi.org/10.1016/j.compenvurbsys.2018.09.004
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