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Article summary:
1. This article proposes a virtual reality panoramic image-based deep learning framework for measuring visual walkability perception (VWP).
2. A VWP classification deep multitask learning model was developed and trained on human ratings of panoramic SVIs in virtual reality to predict VWP in six categories.
3. An interpretable deep learning model was used to assist in identifying and visualizing elements that contribute to VWP.
Article analysis:
The article is generally reliable and trustworthy, as it provides a detailed description of the proposed method for measuring visual walkability perception (VWP) using virtual reality panoramic images and deep learning. The authors provide evidence for their claims by citing relevant research studies, and they also present the results of their experiments in detail. Furthermore, the authors discuss potential limitations of their approach, such as the need for further research into how different types of street environments affect VWP.
However, there are some areas where the article could be improved upon. For example, while the authors discuss potential biases in their approach, they do not provide any evidence or examples to support this claim. Additionally, while the authors mention possible risks associated with using VR technology for measuring VWP, they do not explore these risks in depth or provide any recommendations on how to mitigate them. Finally, while the authors present both sides of the argument regarding VWP measurement using VR technology, they do not provide an equal amount of detail or evidence for each side.