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Water View Imagery Dataset

Introduction

We capture and segment a new dataset of on-water perspectives, which is designed for semantic segmentation of water level scenarios and can assist waterscape-related segmentation applications. We name it Water View Imagery dataset, and it contains 500 high-resolution images and each segmented imagery with a size of 2048 x 1536 pixels. These images in this dataset come from the Mapillary platform, and these are from 8 cities' water areas: Amsterdam, Bangkok, Chicago, Istanbul, Japan, London, Paris, and Venice. A total of 15 categories are selected for annotation, namely:

  • water
  • sky
  • terrain
  • traditional building
  • modern building
  • revetment
  • bridge
  • car
  • truck
  • bicycle
  • boat
  • tree
  • grass
  • people
  • void

Example

Access

This dataset is available for download on Google Drive.

Reference, citation, and documentation

A paper about the work was published in Ecological Indicators and it is available open access.

If you use this work in a scientific context, please cite this article.

Luo J, Zhao T, Cao L, Biljecki F (2022): Water View Imagery: Perception and evaluation of urban waterscapes worldwide. Ecological Indicators, 145: 109615. doi:10.1016/j.ecolind.2022.109615

@article{2022_ei_water_view_imagery,
  author = {Luo, Junjie and Zhao, Tianhong and Cao, Lei and Biljecki, Filip},
  doi = {10.1016/j.ecolind.2022.109615},
  journal = {Ecological Indicators},
  pages = {109615},
  title = {Water View Imagery: Perception and evaluation of urban waterscapes worldwide},
  volume = {145},
  year = {2022}
}

License

This dataset is released under the CC BY-NC-SA 4.0 license.

Acknowledgements

We thank the members of the NUS Urban Analytics Lab for the discussions. This research is part of the projects (i) Research on the theory and digital technologies of the Grand Canal's cultural heritage protection, which is supported by the National Social Science Foundation of China (19ZDA193); (ii) The Technical Key Project of Shenzhen Science and Technology Innovation Commission Grant JSGG20201103093401004; (iii) Large-scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the Start Up Grant R-295-000-171-133; and (iv) Multi-scale Digital Twins for the Urban Environment: From Heartbeats to Cities, which is supported by the Singapore Ministry of Education Academic Research Fund Tier 1.

Contact

For comments and feedback, contact the lead researcher Junjie Luo at [email protected] or the principal investigator Filip Biljecki at [email protected].

For more information about our research, please visit the website of our Urban Analytics Lab at the National University of Singapore.