{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:55:22Z","timestamp":1760057722003,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T00:00:00Z","timestamp":1739750400000},"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>Point feature cartographic label placement is a key problem in the automatic configuration of map labeling. Prior research on it only addresses label conflict or overlap issues; it does not fully take into account and resolve both types of issues. In this study, we attempt to apply machine learning techniques to the automatic placement of point feature labels since label placement is a task that heavily relies on expert expertise, which is very congruent with neural networks\u2019 ability to mimic the human brain\u2019s thought process. We trained ResNet using large amounts of well-labeled picture data. The label\u2019s proper location for a given unlabeled point feature was then predicted by the trained model. We assessed the outcomes both quantitatively and qualitatively, contrasting the ResNet model\u2019s output with that of the expert manual placement approach and the conventional Maplex automatic placement method. According to the evaluation, the ResNet model\u2019s test set accuracy was 97.08%, demonstrating its ability to locate the point feature label in the right place. This study offers a workable solution to the label overlap and conflict issue. Simultaneously, it has significantly enhanced the map\u2019s esthetic appeal and the information\u2019s clarity.<\/jats:p>","DOI":"10.3390\/ijgi14020088","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T12:36:57Z","timestamp":1739795817000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Annotation of Map Point Features Based on Deep Learning ResNet Models"],"prefix":"10.3390","volume":"14","author":[{"given":"Yaolin","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Geographic Information Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwen","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Geographic Information Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingsong","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Geographic Information Science, Nanjing University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geographic Information Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geographic Information Science, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Weibel, R., Keller, S.F., and Reichenbacher, T. 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