Spatial Height Prediction of ICESat-2 Data using Random Forest Regression
L.W.L. Kan (TU Delft - Architecture and the Built Environment)
H. Ledoux – Mentor (TU Delft - Urban Data Science)
Maarten Pronk – Graduation committee member (Deltares)
A. Rafiee – Coach (TU Delft - Digital Technologies)
H.W. de Wolff – Coach (TU Delft - Urban Development Management)
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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.