A systematic review and meta-analysis of Digital Elevation Model (DEM) fusion: pre-processing, methods and applications

TL;DR

This paper systematically reviews and meta-analyzes Digital Elevation Model (DEM) fusion techniques, covering pre-processing, methods, applications, challenges, and future research directions in remote sensing.

Contribution

It provides the first comprehensive review and meta-analysis of DEM fusion, highlighting current methods, challenges, and future research avenues.

Findings

Identified key challenges in DEM fusion methods.

Compared various DEM fusion techniques and their effectiveness.

Proposed future research directions for improved DEM fusion.

Abstract

The remote sensing community has identified data fusion as one of the key challenging topics of the 21st century. The subject of image fusion in two-dimensional (2D) space has been covered in several published reviews. However, the special case of 2.5D/3D Digital Elevation Model (DEM) fusion has not been addressed till date. DEM fusion is a key application of data fusion in remote sensing. It takes advantage of the complementary characteristics of multi-source DEMs to deliver a more complete, accurate and reliable elevation dataset. Although several methods for fusing DEMs have been developed, the absence of a well-rounded review has limited their proliferation among researchers and end-users. It is often required to combine knowledge from multiple studies to inform a holistic perspective and guide further research. In response, this paper provides a systematic review of DEM fusion: the pre-processing workflow, methods and applications, enhanced with a meta-analysis. Through the discussion and comparative analysis, unresolved challenges and open issues were identified, and future directions for research were proposed. This review is a timely solution and an invaluable source of information for researchers within the fields of remote sensing and spatial information science, and the data fusion community at large.