Supporting data for "CIM-WV: A 2D semantic segmentation dataset of rich window view contents in high-rise, high-density Hong Kong based on photorealistic City Information Models"
This is the official repository of the CIM-WV dataset. For technical details, please refer to:
Li, M., Yeh, A. G. & Xue, F. (2023). CIM-WV: A 2D semantic segmentation dataset of rich window view contents in high-rise, high-density Hong Kong based on photorealistic City Information Models. Urban Informatics, 1-24.
This study was supported in part by the Department of Science and Technology of Guangdong Province (GDST) (2020B1212030009, 2023A1515010757) and the University of Hong Kong (203720465).
Overview of CIM-WV
This paper presents a City Information Model (CIM)-generated Window View (CIM-WV) dataset comprising 2,000 annotated images collected in the high-rise, high-density urban areas of Hong Kong.
1) Window view images of CIM-WV depict diversified urban scenes of Hong Kong at different locations, elevations, and orientations
2) The CIM-WV includes seven semantic labels, i.e., building, sky, vegetation, road, waterbody, vehicle, and terrain.
In addition, we provide variants of DeepLab V3+ models trained on CIM-WV, real window view images, Google Earth CIM-generated window view images from New York, and Google Earth CIM-generated window view images from Singapore, respectively.
You can modify the source code here to use the trained DeepLab V3+ models.
Contribution
1) CIM-WV is the first public CIM-generated photorealistic window view dataset with rich semantics.
2) Comparative analysis shows a more accurate window view assessment using deep learning from CIM-WV than deep transfer learning from ground-level views.
3) For urban researchers and practitioners, our publicly accessible deep learning models trained on CIM-WV enable novel multi-source window view-based urban applications including precise real estate valuation, improvement of built environment, and window view-related urban analytics.
Please cite our paper and dataset, if you find our work useful for your research and practices. Many thanks.
For any inquiries, please feel free to contact Maosu at maosulee@connect.hku.hk or Dr. Frank at xuef@hku.hk.
