Machine Learning Algorithms with Enhanced Features for Reconstructing High-Resolution Urban DEMs

Abstract

Machine Learning (ML) is extensively used in diverse topic domains, including geographical information. Despite its limitations, using Digital Elevation Models (DEMs) is gradually being considered in various operational applications. This study explores the application of machine learning algorithms, Shuttle Radar Topography Mission (SRTM) data with different resolutions, and data collected by Unmanned Aerial Vehicle (UAV) technology to produce high-resolution DEMs. The proposed construction approach is based on eight algorithms Linear Regression, Decision Tree, Random Forest, Ridge, Lasso, SVR, K-Neighbors Regressor, and XGB Regressor to deal with the complex features of urban topography to reconstruct high-resolution urban DEMs. The proposed algorithms were applied to two different study areas in Egypt. The results were contrasted with a reference DEM obtained from the ground measurement data (UAV). The numerical accuracy and terrain feature preserving effects of the Linear Regression algorithm in the first study area can generate reconstructed DEMs that better match the Reference DEMs, show lower mean absolute error (MAE), mean square error (MSE), and improve the accuracy of the terrain parameters by the overall fitness R2 of .971. The results showed that the Linear Regression algorithm is the most accurate with an R2 of .985. Compared to other commonly used methods, the current proposed approach offers a cost-effective and innovative method for acquiring high-resolution DEMs in other data-scarce regions, resulting in superior results. Our research is a comprehensive examination of geographical artificial intelligence (Geo AI), which is a term that refers to the processing of geographic information.