A Machine Learning Approach to Recognize Environmental Features Associated with Social Factors

Simple item page
dc.contributor.authorDiaz-Ramos, Jonathanen
dc.contributor.committeechairJones, Creed Farrisen
dc.contributor.committeememberAbbott, Amos L.en
dc.contributor.committeememberGohlke, Julia M.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2024-06-12T08:00:33Zen
dc.date.available2024-06-12T08:00:33Zen
dc.date.issued2024-06-11en
dc.description.abstractIn this thesis we aim to supplement the Climate and Economic Justice Screening Tool (CE JST), which assists federal agencies in identifying disadvantaged census tracts, by extracting five environmental features from Google Street View (GSV) images. The five environmental features are garbage bags, greenery, and three distinct road damage types (longitudinal, transverse, and alligator cracks), which were identified using image classification, object detection, and image segmentation. We evaluate three cities using this developed feature space in order to distinguish between disadvantaged and non-disadvantaged census tracts. The results of the analysis reveal the significance of the feature space and demonstrate the time efficiency, detail, and cost-effectiveness of the proposed methodology.en
dc.description.abstractgeneralIn this thesis we aim to supplement the Climate and Economic Justice Screening Tool (CE JST), which assists federal agencies in identifying disadvantaged census tracts, by extracting five environmental features from Google Street View (GSV) images. The five environmental features are garbage bags, greenery, and three distinct road damage types (longitudinal, transverse, and alligator cracks), which were identified using image classification, object detection, and image segmentation. We evaluate three cities using this developed feature space in order to distinguish between disadvantaged and non-disadvantaged census tracts. The results of the analysis reveal the significance of the feature space and demonstrate the time efficiency, detail, and cost-effectiveness of the proposed methodology.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:40960en
dc.identifier.urihttps://hdl.handle.net/10919/119394en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectcomputer visionen
dc.subjectobject detectionen
dc.subjectimage segmentationen
dc.subjectdeep learningen
dc.titleA Machine Learning Approach to Recognize Environmental Features Associated with Social Factorsen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Diaz-Ramos_J_T_2024.pdf
Size:
7.81 MB
Format:
Adobe Portable Document Format
Download

Collections

Masters Theses