Matching the building footprints of different vector spatial datasets at a similar scale based on one-class support vector machines

Abstract

Automatic matching of multisource data is an important technique for achieving change detection, fusion and updating spatial data. However, most current learning methods for building footprint matching require a large number of samples, and labeling these samples is costly in terms of labor and time. Moreover, multisource building footprint data are complex and diverse leading to recognizing the different matching relationships is a hard task. Thus, this study proposes a learning-based method for recognizing multisource building footprints matching relationships by using a one-class support vector machine (OCSVM). The OCSVM was trained using only positive samples. First, a set of geometric indicators was designed to train a model and realize initial matching recognition. Then, a contextual metric was calculated based on the rough matching results, and geometric and contextual metrics were combined to train the model and realize relaxed matching recognition. Relaxed matching is an optimization process implemented after initial matching to recognize more relaxed matching relationships. In relaxed matching, a convex hull is used to recognize matching relationships besides 1:1, such as 1:n, m:1 and m:n. The experimental results showed that the proposed method outperformed indicator-weighted (weighted average) and learning-based matching methods, such as traditional SVMs and decision trees (DTs). The precision scores of the proposed model were 97.1%, 95% and 97.2% for the Wuhan (China), Beijing (China) and Richmond Hill (Canada) datasets, respectively. Furthermore, the proposed model identified the matching relationships of buildings with complex geometric features and high-density spatial distributions.

Keywords:

• Data matching
• multisource data
• geometric features
• one-class classifier
• similarity measure

Acknowledgment(s)

We thank the editors and all the anonymous reviewers for their insightful comments and suggestions.

Disclosure statement

The authors report there are no competing interests to declare.

Data and codes availability statement

The data and codes that support the findings of this study are available at figshare.com: https://doi.org/10.6084/m9.figshare.21269649.

Additional information

Funding

This study is funded by National Key Research and Development Program of China (NO. 22021YFB2600300, 2021YFB2600303), State Key Laboratory of Geo-Information Engineering (NO.SKLGIE2021-M-4-1) and National Natural Science Foundation of China (NO. 42001340 and 42371454).

Notes on contributors

Yongyang Xu

Yongyang Xu is currently an Associate Professor with the School of Computer Science, China University of Geosciences. He is also a Project Director of the State Key Laboratory of Geo-Information Engineering. His main research interests include deep learning, and vector data rendering and processing.

Jun Li

Jun Li is currently an engineer candidate at the Central and Southern China Municipal Engineering Design and Research Institute Co., Ltd. His research interests focus on spatial data fusion.

Xuejing Xie

Xuejing Xie is currently pursuing the Ph.D. degree in Surveying and Mapping Science and Technology. Her research interests include deep learning and vector data rendering and processing.

Zhong Xie

Zhong Xie is currently a Professor with the School of Geography and Information Engineering, China University of Geosciences (Wuhan). His research interests include deep learning, 3-D rebuilding and spatial analysis, and image processing.