A systematic review and meta-analysis of Digital Elevation Model (DEM) fusion: pre-processing, methods and applications
It offers a timely and invaluable resource for researchers in remote sensing, spatial information science, and data fusion, addressing a previously unaddressed gap in 2.5D/3D DEM fusion.
This paper provides a systematic review and meta-analysis of Digital Elevation Model (DEM) fusion, addressing the lack of comprehensive coverage in this area by analyzing pre-processing, methods, and applications to identify unresolved challenges and propose future research directions.
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.