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

• Published in: arXiv.org
• Main Authors: Okolie, Chukwuma, Smit, Julian
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 08.04.2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer vision; Data integration; Digital Elevation Models; End users; Image processing; Multisensor fusion; Remote sensing; Spatial data; Systematic review; Three dimensional models; Workflow
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2203.15026

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.