Complexity-based matching between image resolution and map scale for multiscale image-map generation

ABSTRACT

An image-map is a compromise between an image and a map. The quality of such maps is affected by several factors, such as (a) the matching between the features on images and the graphic symbols from maps, (b) the complexity of background images, and (c) the representation of graphic and text symbols on the images. This project deals with the first issue. The current solution is that the accuracy of images should satisfy the accuracy standard of maps. However, images with different resolutions can satisfy the standard for a specific map scale. This may lead to a situation in which the levels of detail (LoD) in images may not match the complexity of map features although the planimetric accuracy is matched. To solve this problem, we developed a complexity-based matching between the image resolution and map scale. More precisely, the matching is based on the complexity of line features. Experimental evaluations were conducted in 15 representative areas in Hong Kong using maps at seven scales and eight image resolutions. Results show that the proposed complexity-based method is capable of obtaining good matching between image resolution and map scale in terms of both accuracy and users’ preference.

KEYWORDS:

• Image resolution
• map scale
• line network complexity
• line complexity
• image-map

Data and codes availability statement

The data and codes that support the findings of this study are available in figshare.com with the links: https://doi.org/10.6084/m9.figshare.12570248 (data) and https://doi.org/10.6084/m9.figshare.12568796 (code).

Acknowledgments

The authors would like to express their thanks to the editors and anonymous reviewers for their insightful comments and valuable suggestions which have led to significant improvement in the quality of this article. The authors also thank Dr. Tian LAN at the Hong Kong Polytechnic University for his programming in fractal analysis, and Dr. Jicheng WANG at Southwest Jiaotong University and Dr. Peichao GAO at Beijing Normal University for their helpful discussions and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Research Grants Council, University Grants Committee [PolyU 152672/16E] and the National Natural Science Foundation of China [41930104].

Notes on contributors

Qian Peng

Qian Peng has received her Bachelor degree in remote sensing science and technology at Wuhan University and Master degree in signal and information processing at University of Chinese Academy of Sciences. Now she is a PhD candidate in the Department of Land Surveying and Geo-Informatics at the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. Her research interests include geospatial data processing and digital cartography.

Zhilin Li

Zhilin Li, Professor in the Faculty of Geosciences and Environmental Engineering & State-Province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, Chengdu, China; Adjunct Professor in the Department of Land Surveying and Geo-Informatics at the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. His research interests include digital cartography, scale driven spatial data modelling and analysis, and feature extraction from remote sensing images.

Jun Chen

Jun Chen, Professor in National Geomatics Center of China, Beijing, China. He specializes in the theories and methods of land cover mapping, updating and validation.

Wanzeng Liu

Wanzeng Liu, PhD, Senior Engineer in National Geomatics Center of China, Beijing, China. He mainly engaged in the research of GIS spatial relationship, database updating and emergency mapping technology.