Inferring storefront vacancy using mobile sensing images and computer vision approaches

Type & Source

This dataset functions as supplementary material for the paper entitled 'Inferring Storefront Vacancy Using Mobile Sensing Images and Computer Vision Approaches,' which has been published in the Journal Computers, Environment, and Urban Systems. The dataset comprises the pre-trained Faster RCNN model, meticulously crafted for the recognition of vacant shops (located in the modal_data folder), along with the corresponding training data formatted in VOC within the VOCdevkit folder. Additionally, the GIS results of identified stores and aggregated outcomes at the street level are stored in Results_Xining.rar. For a comprehensive understanding of usage guidelines, please refer to the detailed operational instructions outlined in the README.

Citation

Li, Y., & Long, Y. (2024). Inferring storefront vacancy using mobile sensing images and computer vision approaches. Computers, Environment and Urban Systems, 108, 102071. https://doi.org/10.1016/j.compenvurbsys.2023.102071

Access & Document

https://data.mendeley.com/datasets/9v37g2y9fc/1