Multi-criterion methods to extract topographic feature lines from contours on different topographic gradients

Abstract

The existing methods of the automatic extraction of topographic feature lines from terrain representation either have too high sensitivity to terrain noise or lose significant branches. In this study, we present new multi-criterion methods to extract topographic feature lines from contours on different topographic gradients according to the negative or positive signs of the curvature and neighboring feature points on the contours, the hierarchical structure of these feature points, and the spatial relationships between topographic feature lines and contours. First, the digitization directions of source contours were automatically detected and adjusted (when necessary) to establish the spatial relationships among the contours before we extract and group the feature points in the terrain. Second, we determine the ‘mainstreams’ and ‘tributaries’ of the topological structure trees according to the relationships among the previously identified feature point groups. Finally, a key aspect of our paper is the proposition of multi-criterion methods to extract topographic feature lines. Compared with the regular square grids (RSG)-based and Voronoi skeleton-based methods, the proposed methods can extract topographic feature lines with higher accuracy, better continuity, lower spatial logical conflicts between topographic feature lines and contours.

Keywords:

• Contours
• multi-criterion methods
• topographic feature lines

Acknowledgements

The authors sincerely thank the editor and anonymous reviewers for their valuable comments and suggestions, which improved the quality of this article.

Data and codes availability statement

The datasets and codes that support the findings of this study are available in figshare.com through the following link: https://doi.org/10.6084/m9.figshare.15170058.

Disclosure statement

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

Additional information

Funding

Funding was provided by the National Natural Science Foundation of China [Grant No. 41871378].

Notes on contributors

Lu Cheng

Lu Cheng is a PhD student at School of Resource and Environmental Sciences of Wuhan University. Her research interest is 3D geographic information generalization.

Qingsheng Guo

Qingsheng Guo is a professor at School of Resource and Environmental Sciences and State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing of Wuhan University. His research interests are cartographic generalization, intelligent processing and visualization of geographic information.

Lifan Fei

Lifan Fei is a professor at School of Resource and Environmental Sciences of Wuhan University. His research interest is the generalization of geomorphology and river system.

Zhiwei Wei

Zhiwei Wei is a research assistant at Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences. His research interests are map spatial cognition and knowledge mining, intelligent cartography, network geographic information service integration.

Guifang He

Guifang He is a lecturer at School of Geographic Information and Tourism of Chuzhou University. Her research is land informatization and cartography.

Yang Liu

Yang Liu is a PhD student at School of Resource and Environmental Sciences of Wuhan University. His research interest is a progressive generalization of roads and buildings.