A geospatial image based eye movement dataset for cartography and GIS

ABSTRACT

Eye movement is a new type of data for cartography and geographic information science (GIS) research. However, previous studies rarely built eye movement datasets with geospatial images. In this paper, we firstly proposed a geospatial image-based eye movement dataset called GeoEye, a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images. In addition, we used the dataset for geospatial image saliency prediction and map user identification. Results demonstrated the scientific benefits and applications of the proposed dataset. GeoEye dataset will not only promote the application of eye-tracking data in cartography and GIS research but also intelligence and customization of geographic information services.

KEYWORDS:

• Eye movement dataset
• geospatial image
• cartography
• GIS
• visual saliency detection

Acknowledgments

We acknowledge the contributions of Tong Qin, Lin Zhu, Siliang Tang, Tianyu Yang, Yilong Liu, and Yulin Wu to the preparation and conduct of the experiment. In addition, we wish to thank Bing Liu, Shengkai Wang and Zhicheng Zhan for their advice in writing the manuscript and analyzing the data.

Author contributions

Bing He: Experiment Design, Experiment execution, Data Analysis, Data Organization, Drafting of Manuscript. Weihua Dong: Experiment Design, Supervision, Manuscript Review. Hua Liao: Data Analysis, Manuscript Review. Qi Ying: Experiment Design, Participant recruitment, Experiment execution. Bowen Shi: Experiment Design, Experiment execution, Data Organization. Jiping Liu: Manuscript Review. Yong Wang: Manuscript Review.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

All dataset files can be accessed from the public repository Figshare (https://doi.org/10.6084/m9.figshare.14684214).

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [NSFC, Grant Nos. 41871366] and the National Key Research and Development Program of China [Grant No. 2017YFB0503602].