{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:58:55Z","timestamp":1775325535072,"version":"3.50.1"},"reference-count":88,"publisher":"Association for Computing Machinery (ACM)","issue":"8","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:p>\n            Electronic maps consist of diverse entities, such as points of interest (POIs), road segments, and land parcels, playing a vital role in applications like ITS and LBS. Map entity representation learning (MapRL) generates versatile and reusable data representations, providing essential tools for efficiently managing and utilizing map entity data. Despite the progress in MapRL, two key challenges constrain further development. First, existing research is fragmented, with models classified by the type of map entity, limiting the reusability of techniques across different tasks. Second, the lack of unified benchmarks makes systematic evaluation and comparison of models difficult. To address these challenges, we propose a novel taxonomy for MapRL that organizes models based on functional modules\u2014such as encoders, pre-training tasks, and downstream tasks\u2014rather than by entity type. Building on this taxonomy, we present a taxonomy-driven library,\n            <jats:italic toggle=\"yes\">VecCity<\/jats:italic>\n            , which offers easy-to-use interfaces for encoding, pre-training, fine-tuning, and evaluation. The library integrates datasets from nine cities and reproduces 21 mainstream MapRL models, establishing the first standardized benchmarks for the field. VecCity also allows users to modify and extend models through modular components, facilitating seamless experimentation. Our comprehensive experiments cover multiple types of map entities and evaluate 21\n            <jats:italic toggle=\"yes\">VecCity<\/jats:italic>\n            pre-built models across various downstream tasks. Experimental results demonstrate the effectiveness of\n            <jats:italic toggle=\"yes\">VecCity<\/jats:italic>\n            in streamlining model development and provide insights into the impact of various components on performance. By promoting modular design and reusability,\n            <jats:italic toggle=\"yes\">VecCity<\/jats:italic>\n            offers a unified framework to advance research and innovation in MapRL. The code is available at https:\/\/github.com\/Bigscity-VecCity\/VecCity.\n          <\/jats:p>","DOI":"10.14778\/3742728.3742749","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T13:32:53Z","timestamp":1756906373000},"page":"2575-2588","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["<i>VecCity<\/i>\n            : A Taxonomy-Guided Library for Map Entity Representation Learning"],"prefix":"10.14778","volume":"18","author":[{"given":"Wentao","family":"Zhang","sequence":"first","affiliation":[{"name":"SCSE, Beihang University, Engineering Research Center of Advanced Computer Application Technology, Ministry of Education"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"SCSE, Beihang University, MIIT Key Laboratory of Data Intelligence and Management, SEM, Beihang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Yang","sequence":"additional","affiliation":[{"name":"SCSE, Beihang University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leong Hou","family":"U","sequence":"additional","affiliation":[{"name":"University of Macau"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2022. 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