Fig. 3 (IMAGE)
Caption
Qualitative results for different types and sizes of buildings when Mask R-CNN is trained using HSRBFIA (Hybrid Collection of Synthetic and Real-world Building Facade Images and Annotations) datasets with different ratios of synthetic to real data: (a) low-rise houses in Osaka; (b) low-rise houses in Los Angeles; (c) high-rise houses in New York City; (d) complex facades in Shanghai. (The red dashed rectangles highlight parts of the street-view images that were prone to failure during facade instance segmentation.)
Credit
2022 Jiaxin Zhang et al., Automatic generation of synthetic datasets from a city digital twin for use in the instance segmentation of building facades, Journal of Computational Design and Engineering
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License
CC BY