Integrating topographic knowledge into point cloud simplification for terrain modelling

Abstract

Terrain models are widely used to depict the shape of the Earth’s surface. With the development of photogrammetric methods, point cloud data have become one of the most popular data sources for terrain modelling. However, the obtained point clouds are of high density, which often increases redundancy rather than improving accuracy. Therefore, point cloud simplification should be a core component of terrain modelling. This paper proposes a point cloud simplification method by integrating topographic knowledge into terrain modelling (TKPCS). The method contains two steps: (1) topographic knowledge recognition and construction and (2) point cloud simplification using this topographic knowledge for terrain modelling. The proposed approach is benchmarked against improved versions of existing methods to validate its capability and accuracy in digital elevation model construction and terrain derivative extraction. The results show that the simplified points of the TKPCS method can generate finer resolution terrain models with higher accuracy and greater information entropy. The good performance of the TKPCS method is also stable at different scales. This work endeavours to transform perceptive topographic knowledge into a process of point cloud simplification and can benefit future research related to terrain modelling.

Keywords:

• Terrain modelling
• topographic knowledge
• point cloud simplification
• DEM

Acknowledgments

The authors would take to thank editors and two anonymous reviewers for the useful comments on the manuscript.

Authors’ contribution

Jun Chen contributed to the idea, methodology, implementation, and writing. Liyang Xiong contributed to the idea, methodology, study design, and writing. Bowen Yin contributed to the methodology and implementation. Guanghui Hu contributed to the idea and writing. Guoan Tang contributed to the idea and methodology.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The test data and codes that support this work are available in ‘figshare’ repository with the private link ‘https://doi.org/10.6084/m9.figshare.22047824’.

Additional information

Funding

This work was supported by the National Science Foundation of China under Grant [41930102]; National Key Research and Development Program of China under Grant [2021YFB3900901]; Priority Academic Program Development of Jiangsu Higher Education Institutions under Grant [164320H116].

Notes on contributors

Jun Chen

Jun Chen is a student in Nanjing Normal University and interested in digital terrain analysis.

Liyang Xiong

Liyang Xiong is an associate professor of Nanjing Normal University and interested in GIScience & Geomorphology.

Bowen Yin

Bowen Yin is a student in Nanjing Normal University and interested in space-time GIS.

Guanghui Hu

Guanghui Hu is a student in Nanjing Normal University and interested in digital terrain analysis.

Guoan Tang

Guoan Tang is a professor of Nanjing Normal University and interested in GIScience & Geomorphology.