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Data · dataset · 2024

BCL2024_Inferring storefront vacancy using mobile sensing images and computer vision approaches

Listed in Teesside University Research Data Repository

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.

Description

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.

Links

Where it is published

Catalogue records · 1

Topics

Inferred from text
Image 65%
Provenance · 1 source records, 9 field assertions
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Teesside University Research Data Repositoryoai:data.mendeley.com/9v37g2y9fc.114 h agoJSON v1
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concepts[field].anzsrc:group:3304mapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Urban Planning']
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concepts[field].local:field:social-sciencemapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[modality].local:modality:imageenrichment · researchdata tees ac ukkeyword-concept-rules@1.0.0title+description (65%)
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