{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:18:39Z","timestamp":1774621119807,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:00:00Z","timestamp":1640736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Address matching continues to play a central role at various levels, through geocoding and data integration from different sources, with a view to promote activities such as urban planning, location-based services, and the construction of databases like those used in census operations. However, the task of address matching continues to face several challenges, such as non-standard or incomplete address records or addresses written in more complex languages. In order to better understand how current limitations can be overcome, this paper conducted a systematic literature review focused on automated approaches to address matching and their evolution across time. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, resulting in a final set of 41 papers published between 2002 and 2021, the great majority of which are after 2017, with Chinese authors leading the way. The main findings revealed a consistent move from more traditional approaches to deep learning methods based on semantics, encoder-decoder architectures, and attention mechanisms, as well as the very recent adoption of hybrid approaches making an increased use of spatial constraints and entities. The adoption of evolutionary-based approaches and privacy preserving methods stand as some of the research gaps to address in future studies.<\/jats:p>","DOI":"10.3390\/ijgi11010011","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T08:12:15Z","timestamp":1640765535000},"page":"11","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Automatic Identification of Addresses: A Systematic Literature Review"],"prefix":"10.3390","volume":"11","author":[{"given":"Paula","family":"Cruz","sequence":"first","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}]},{"given":"Leonardo","family":"Vanneschi","sequence":"additional","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1136-3387","authenticated-orcid":false,"given":"Marco","family":"Painho","sequence":"additional","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6050-9958","authenticated-orcid":false,"given":"Paulo","family":"Rita","sequence":"additional","affiliation":[{"name":"NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Javidaneh, A., Karimipour, F., and Alinaghi, N. 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