BCL2024_Inferring storefront vacancy using mobile sensing images and computer vision approaches

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.本数据集为发表于《计算机、环境与城市系统》（Computers, Environment, and Urban Systems）期刊的论文《基于移动感知影像与计算机视觉方法推断商铺空置状态》的补充材料。该数据集包含专为空商铺识别任务精心构建的预训练Faster RCNN（Faster Region-based Convolutional Neural Networks）模型（存放于modal_data文件夹），以及VOCdevkit文件夹中格式为VOC的对应训练数据。此外，已识别商铺的地理信息系统（GIS, Geographic Information System）结果与街道级聚合统计结果存储于Results_Xining.rar压缩包中。如需全面了解使用规范，请参阅README文件中详述的操作指南。

搜集汇总

背景与挑战

背景概述

该数据集是用于支持一篇关于使用移动感知图像和计算机视觉方法推断店铺空置情况的研究论文的补充材料。它包含预训练的Faster RCNN模型、VOC格式的训练数据，以及识别出的店铺和街道级别汇总的GIS结果，旨在辅助计算机视觉在空置店铺识别领域的应用。