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

arXiv: https://arxiv.org/abs/2203.15026 · HTML: https://mlsanity.com/paper/2203.15026/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 197 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Void Filling of Digital Elevation Models with Deep Generative Models](https://mlsanity.com/paper/1811.12693/) (2018)
- [Fusion of TanDEM-X and Cartosat-1 Elevation Data Supported by NeuralNetwork-Predicted Weight Maps](https://mlsanity.com/paper/1810.11415/) (2018)
- [Fusion of Urban TanDEM-X raw DEMs using variational models](https://mlsanity.com/paper/1810.11314/) (2018)

## Built on by (most cited first, 2 of 2)

- [Generation of High Spatial Resolution Terrestrial Surface from Low Spatial Resolution Elevation Contour Maps via Hierarchical Computation of Median Elevation Regions](https://mlsanity.com/paper/2307.09239/) (2023)
- [Design of UAV flight state recognition and trajectory prediction system based on trajectory feature construction](https://mlsanity.com/paper/2401.15564/) (2024)

## Abstract (arXiv)

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.