Data for the findings presented in Hwang et al. 2023. “Curating Training Data for Reliable Large-Scale Visual Data Analysis: Lessons from Identifying Trash in Street View Imagery".

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Abstract/Contents

Abstract
This collection contains replication material for Hwang et al. 2023. “Curating Training Data for Reliable Large-Scale Visual Data Analysis: Lessons from Identifying Trash in Street View Imagery". Sociological Methods and Research. The input data for all tables and figures in the paper are stored here. Our analysis code is available at Github (https://github.com/Changing-Cities-Research-Lab/gsv-training-replication-repo).

Description

Type of resource Dataset, text
Publication date April 8, 2023

Creators/Contributors

Author Hwang, Jackelyn
Author Dahir, Nima
Author Sarukkai, Mayuka
Author Wright, Gabby

Subjects

Subject Computer vision
Subject Crowdsourcing
Subject Visual data
Subject Sociology, Urban
Subject Systematic social observation
Genre Data
Genre Documentation
Genre Tabular data
Genre Data sets
Genre Dataset
Genre Tables (data)

Bibliographic information

Related item
DOI https://doi.org/10.25740/xq145bk1280
Location https://purl.stanford.edu/xq145bk1280

Access conditions

Use and reproduction
User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
License
This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).

Preferred citation

Preferred citation
Hwang, J., Dahir, N., Sarukkai, M., and Wright, G. (2023). Data for the findings presented in Hwang et al. 2023. “Curating Training Data for Reliable Large-Scale Visual Data Analysis: Lessons from Identifying Trash in Street View Imagery". Stanford Digital Repository. Available at https://purl.stanford.edu/xq145bk1280. https://doi.org/10.25740/xq145bk1280.

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