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

• URL: http://arxiv.org/abs/2203.15026v1
• Date: Mon, 28 Mar 2022 18:39:14 GMT
• Title: A systematic review and meta-analysis of Digital Elevation Model (DEM) fusion: pre-processing, methods and applications
• Authors: Chukwuma Okolie and Julian Smit
• Abstract summary: 2.5D/3D Digital Elevation Model (DEM) fusion is a key application of data fusion in remote sensing. DEM fusion takes advantage of the complementary characteristics of multi-source DEMs to deliver a more complete, accurate and reliable dataset. This paper provides a systematic review of DEM fusion: the pre-processing workflow, methods and applications, enhanced with a meta-analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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