{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T17:27:36Z","timestamp":1793640456256,"version":"4.5.1"},"reference-count":15,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":33,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Data in Brief"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.dib.2026.113250","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T15:07:53Z","timestamp":1788793673000},"page":"113250","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["IRD-dataset: a multi-source Iraqi road defect dataset for detection, segmentation, and GPS-based mapping"],"prefix":"10.1016","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7403-2134","authenticated-orcid":false,"given":"Zainab J.","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1830-0568","authenticated-orcid":false,"given":"Hussein K.","family":"Khafaji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.dib.2026.113250_bib0001","doi-asserted-by":"crossref","first-page":"6112","DOI":"10.3390\/app15116112","article-title":"A review on automated detection and identification algorithms for highway pavement distress","volume":"15","author":"Lv","year":"2025","journal-title":"Appl. Sci."},{"key":"10.1016\/j.dib.2026.113250_bib0002","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11554-025-01683-1","article-title":"Advancements in real-time road damage detection: a comprehensive survey of methodologies and datasets","volume":"22","author":"Abdelwahed","year":"2025","journal-title":"J. Real-Time Image Process."},{"key":"10.1016\/j.dib.2026.113250_bib0003","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijtst.2023.11.005","article-title":"A state-of-the-art survey of deep learning models for automated pavement crack segmentation","volume":"13","author":"Gong","year":"2024","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"10.1016\/j.dib.2026.113250_bib0004","article-title":"RDD2020: An image dataset for smartphone-based road damage detection and classification","volume":"v1","author":"Arya","year":"2021","journal-title":"Mendeley Data"},{"key":"10.1016\/j.dib.2026.113250_bib0005","first-page":"846","article-title":"RDD2022: a multi-national image dataset for automatic road damage detection","volume":"11","author":"Arya","year":"2024","journal-title":"Geosci. Data J."},{"key":"10.1016\/j.dib.2026.113250_bib0006","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","article-title":"Automatic road crack detection using random structured forests","volume":"17","author":"Shi","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.dib.2026.113250_bib0007","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","article-title":"Feature pyramid and hierarchical boosting network for pavement crack detection","volume":"21","author":"Yang","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.dib.2026.113250_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2023.109642","article-title":"SUT-Crack: a comprehensive dataset for pavement crack detection across all methods","volume":"51","author":"Sabouri","year":"2023","journal-title":"Data Brief"},{"key":"10.1016\/j.dib.2026.113250_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2023.109692","article-title":"UAV-PDD2023: a benchmark dataset for pavement distress detection based on UAV images","volume":"51","author":"Yan","year":"2023","journal-title":"Data Brief"},{"key":"10.1016\/j.dib.2026.113250_bib0010","series-title":"IRD-Dataset","author":"Ahmed","year":"2026"},{"key":"10.1016\/j.dib.2026.113250_bib0011","unstructured":"Ultralytics, Object detection datasets overview. https:\/\/docs.ultralytics.com\/datasets\/detect\/(accessed 4 August 2026)."},{"key":"10.1016\/j.dib.2026.113250_bib0012","unstructured":"Ultralytics, Instance segmentation datasets overview. https:\/\/docs.ultralytics.com\/datasets\/segment\/(accessed 4 August 2026)."},{"key":"10.1016\/j.dib.2026.113250_bib0013","unstructured":"CVAT.ai Corporation, Computer Vision Annotation Tool (CVAT) [software], Zenodo. 10.5281\/zenodo.4009388."},{"key":"10.1016\/j.dib.2026.113250_bib0014","unstructured":"Folium, Python data, Leaflet.js maps. https:\/\/python-visualization.github.io\/folium\/(accessed 1 July 2026)."},{"key":"10.1016\/j.dib.2026.113250_bib0015","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/10867651.2004.10487596","article-title":"An image inpainting technique based on the fast marching method","volume":"9","author":"Telea","year":"2004","journal-title":"J. Graph. Tools"}],"container-title":["Data in Brief"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352340926007973?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352340926007973?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T16:47:59Z","timestamp":1793638079000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352340926007973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":15,"alternative-id":["S2352340926007973"],"URL":"https:\/\/doi.org\/10.1016\/j.dib.2026.113250","relation":{},"ISSN":["2352-3409"],"issn-type":[{"value":"2352-3409","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"IRD-dataset: a multi-source Iraqi road defect dataset for detection, segmentation, and GPS-based mapping","name":"articletitle","label":"Article Title"},{"value":"Data in Brief","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dib.2026.113250","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"113250"}}