TY  - JOUR
T1  - A BIBLIOMETRIC ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR IMPROVING DIGITAL ELEVATION MODEL ACCURACY: TRENDS, GAPS, AND FUTURE DIRECTIONS
AU  - Kırtıloğlu, Osman Sami
AU  - Akyel, Elif
AU  - Koçak, Mehmet Güven
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.36306/konjes.1685083
JF  - Konya Journal of Engineering Sciences
JO  - KONJES
PB  - Konya Technical University
WT  - DergiPark
SN  - 2667-8055
SP  - 892
EP  - 909
VL  - 13
IS  - 3
LA  - en
AB  - This study presents a bibliometric and thematic analysis of research focused on improving the accuracy of digital elevation models (DEMs) using machine learning (ML) techniques between 2005 and 2025. Drawing from Scopus and Web of Science databases, complemented by manual reference chaining, approximately 250 publications were analyzed. Results show a notable increase in scholarly activity after 2018, linked to the release of enhanced DEM products such as CoastalDEM and FABDEM. Keyword co-occurrence and thematic coding revealed four conceptual pillars: models, methods, applications, and data sources. Ensemble algorithms like Random Forest and LightGBM dominate the methodological landscape, while deep learning methods such as Convolutional Neural Network (CNNs) and Generative Adversarial Network (GANs) are emerging. Despite advancements, methodological homogeneity, reliance on Root Mean Square Error (RMSE), and underutilization of data fusion and semi-supervised learning strategies remain significant limitations. Silent themes and regional gaps emphasize the need for methodological diversification and broader global integration. Future research should prioritize algorithmic diversity, standardized multi-metric validation frameworks, open science practices, and regional model applications. This study offers a structural mapping of DEM–ML research and proposes strategic directions for advancing the field through interdisciplinary collaboration and innovation.
KW  - Accuracy Improvement
KW  - Bibliometric Analysis
KW  - Data Fusion
KW  - Digital Elevation Model
KW  - Machine Learning
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UR  - https://doi.org/10.36306/konjes.1685083
L1  - https://dergipark.org.tr/en/download/article-file/4811681
ER  -