Spatial Height Prediction of ICESat-2 Data using Random Forest Regression

Master thesis (2024)

Authors

L.W.L. Kan Architecture and the Built Environment

Contributors

H. Ledoux Urban Data Science - Architecture and the Built Environment (mentor)
Maarten Pronk Deltares (graduation committee member)
Azarakhsh Rafiee Vrije Universiteit Amsterdam (coach)
H.W. de Wolff Urban Development Management - Architecture and the Built Environment (coach)
Faculty
Architecture and the Built Environment, Architecture and the Built Environment
Copyright: © 2024 Leo Kan
More Info
expand_more

Published Date

17-01-2024

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Architecture and the Built Environment

Abstract

The Earth's surface is a complex landscape that is essential for a wide range of applications, from urban planning to environmental monitoring. Digital models of the Earth's surface are generated through mathematical calculations using elevation data collected from various sources and the Digital Terrain Model (DTM) which captures the bare earth's surface topography in 2.5D. The creation of DTM is an approximation of terrain in unsample locations, by using x-y coordinates and one z value. Traditionally, terrain interpolation uses deterministic or geo-statistical methods to calculate elevation. This research would use random forest regression as an alternative method and to compare the results against traditional interpolation. Comparing different locations against traditional interpolation yields similar results overall. Feature importance, within the the points that are closest to the sampled ICESat-2 data point are more significant than other features used in Random Forest model. The correlation between these datasets and the spatial relationship established would impact on the results of the elevation. The improvement overall of using traditional interpolation compared to random forest regression is limited depending on the location and using model trained with local datasets. For model trained on other geographical locations, which shows similar differences.