{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/21573"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/21573","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Leveraging Autonomous Vehicles In Transportation Asset Management","abstract":"Traffic sign inventory generation is important for transportation asset management because accurate sign records support roadway safety, maintenance planning, and asset-level decision making. However, current field inventory, image-log, street-view, mobile mapping, and computer vision methods may be limited when agencies need to update sign records across roadway networks under practical data collection and processing requirements. This limitation makes it difficult to convert ordinary roadway observations into map-based traffic sign inventory records in a transportation asset management setting. To address this problem, this thesis proposes an integrated video-plus-GPS workflow for traffic sign inventory generation. The proposed workflow connects traffic sign detection with sign-region segmentation and combines monocular depth evidence with GPS-and-bearing coordinate projection so that duplicate-filtered traffic sign inventory records can be obtained. A case study using annotated road sign imagery and vehicle-mounted roadway video with GPS data is used to demonstrate and evaluate the proposed workflow. The results indicate that the detection stage can identify traffic signs across varied roadway scenes and that the combined segmentation, depth, coordinate prediction, and duplicate grouping stages can convert image observations into map-based inventory records. These findings suggest that the proposed workflow can support traffic sign inventory updating and maintenance planning for transportation agencies.","abstract_html":"Traffic sign inventory generation is important for transportation asset management because accurate sign records support roadway safety, maintenance planning, and asset-level decision making. However, current field inventory, image-log, street-view, mobile mapping, and computer vision methods may be limited when agencies need to update sign records across roadway networks under practical data collection and processing requirements. This limitation makes it difficult to convert ordinary roadway observations into map-based traffic sign inventory records in a transportation asset management setting. To address this problem, this thesis proposes an integrated video-plus-GPS workflow for traffic sign inventory generation. The proposed workflow connects traffic sign detection with sign-region segmentation and combines monocular depth evidence with GPS-and-bearing coordinate projection so that duplicate-filtered traffic sign inventory records can be obtained. A case study using annotated road sign imagery and vehicle-mounted roadway video with GPS data is used to demonstrate and evaluate the proposed workflow. The results indicate that the detection stage can identify traffic signs across varied roadway scenes and that the combined segmentation, depth, coordinate prediction, and duplicate grouping stages can convert image observations into map-based inventory records. These findings suggest that the proposed workflow can support traffic sign inventory updating and maintenance planning for transportation agencies.","abstract_has_math":false,"creators":["Veeramachaneni, Mahitha 2003-"],"institution":"University of Houston","degree_name":"Master of Science","degree_level":null,"degree_discipline":"Engineering Data Science","degree_department":null,"school":null,"contributors":[],"advisors":["Gao, Lu"],"committee_chairs":[],"committee_members":["Kulkarni, Yashashree","Mo, Yi-Lung"],"year":2026,"date_issued":"2026-05","date_published":"2026-05","updated_at":"2026-07-24T02:32:12Z","subjects":["Transportation asset management","Traffic sign detection","Monocular depth estimation","Segment Anything Model","RT-DETR","Raffic sign inventory","Vehicle-mounted video","GPS-based geolocation"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/21573","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gao, Lu"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kulkarni, Yashashree","Mo, Yi-Lung"]},{"key":"dc:creator","label":"Author","values":["Veeramachaneni, Mahitha 2003-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-07-16T17:47:25Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-05"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering Data Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Transportation asset management","Traffic sign detection","Monocular depth estimation","Segment Anything Model","RT-DETR","Raffic sign inventory","Vehicle-mounted video","GPS-based geolocation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/21573"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Traffic sign inventory generation is important for transportation asset management because accurate sign records support roadway safety, maintenance planning, and asset-level decision making. However, current field inventory, image-log, street-view, mobile mapping, and computer vision methods may be limited when agencies need to update sign records across roadway networks under practical data collection and processing requirements. This limitation makes it difficult to convert ordinary roadway observations into map-based traffic sign inventory records in a transportation asset management setting. To address this problem, this thesis proposes an integrated video-plus-GPS workflow for traffic sign inventory generation. The proposed workflow connects traffic sign detection with sign-region segmentation and combines monocular depth evidence with GPS-and-bearing coordinate projection so that duplicate-filtered traffic sign inventory records can be obtained. A case study using annotated road sign imagery and vehicle-mounted roadway video with GPS data is used to demonstrate and evaluate the proposed workflow. The results indicate that the detection stage can identify traffic signs across varied roadway scenes and that the combined segmentation, depth, coordinate prediction, and duplicate grouping stages can convert image observations into map-based inventory records. These findings suggest that the proposed workflow can support traffic sign inventory updating and maintenance planning for transportation agencies."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Leveraging Autonomous Vehicles In Transportation Asset Management"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gao, Lu"],"dc:contributor.committeemember":["Kulkarni, Yashashree","Mo, Yi-Lung"],"dc:creator":["Veeramachaneni, Mahitha 2003-"],"dc:date.accessioned":["2026-07-16T17:47:25Z"],"dc:date.issued":["2026-05"],"dc:description.abstract":["Traffic sign inventory generation is important for transportation asset management because accurate sign records support roadway safety, maintenance planning, and asset-level decision making. However, current field inventory, image-log, street-view, mobile mapping, and computer vision methods may be limited when agencies need to update sign records across roadway networks under practical data collection and processing requirements. This limitation makes it difficult to convert ordinary roadway observations into map-based traffic sign inventory records in a transportation asset management setting. To address this problem, this thesis proposes an integrated video-plus-GPS workflow for traffic sign inventory generation. The proposed workflow connects traffic sign detection with sign-region segmentation and combines monocular depth evidence with GPS-and-bearing coordinate projection so that duplicate-filtered traffic sign inventory records can be obtained. A case study using annotated road sign imagery and vehicle-mounted roadway video with GPS data is used to demonstrate and evaluate the proposed workflow. The results indicate that the detection stage can identify traffic signs across varied roadway scenes and that the combined segmentation, depth, coordinate prediction, and duplicate grouping stages can convert image observations into map-based inventory records. These findings suggest that the proposed workflow can support traffic sign inventory updating and maintenance planning for transportation agencies."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/21573"],"dc:language.iso":["English"],"dc:subject":["Transportation asset management","Traffic sign detection","Monocular depth estimation","Segment Anything Model","RT-DETR","Raffic sign inventory","Vehicle-mounted video","GPS-based geolocation"],"dc:title":["Leveraging Autonomous Vehicles In Transportation Asset Management"],"dc:type":["Thesis"],"thesis:degree_discipline":["Engineering Data Science"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:12Z"}