{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:08:32Z","timestamp":1781298512655,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China\u2019s National Key R&amp;D Program","award":["2017YFB0503500"],"award-info":[{"award-number":["2017YFB0503500"]}]},{"name":"China\u2019s National Key R&amp;D Program","award":["41901335"],"award-info":[{"award-number":["41901335"]}]},{"name":"National Natural Science Foundation of China","award":["2017YFB0503500"],"award-info":[{"award-number":["2017YFB0503500"]}]},{"name":"National Natural Science Foundation of China","award":["41901335"],"award-info":[{"award-number":["41901335"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The geographical feature extraction of historical maps is an important foundation for realizing the transition from human map reading to machine map reading. The current methods for building block extraction from historical maps have many problems, such as low accuracy and poor scalability. Moreover, the high cost of annotating historical maps further limits its applications. In this study, a method for extracting building blocks from historical maps is proposed based on the deep object attention network. Based on the OCRNet framework, multiple attention mechanisms were used to improve the ability of the network to extract the contextual information of the target. Moreover, through the optimization of the feature extraction network structure, the impact of the down-sampling process on local information and boundary contours was reduced, in order to improve the network\u2019s ability to capture boundary information. Subsequently, the transfer learning method was used to jointly train the network model on both remote sensing datasets and few-shot historical map datasets to further improve the feature learning ability of the network, which overcomes the constraints of small sample sizes. The experimental results show that the proposed method can effectively improve the extraction accuracy of building blocks from historical maps.<\/jats:p>","DOI":"10.3390\/ijgi11110572","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T03:27:44Z","timestamp":1668655664000},"page":"572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Building Block Extraction from Historical Maps Using Deep Object Attention Networks"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5402-8533","authenticated-orcid":false,"given":"Yao","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"},{"name":"Speed China Technology Co., Ltd., Nanjing 210046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangxia","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2373-8799","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lantian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing Information, Beijing 100011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofei","family":"Qi","sequence":"additional","affiliation":[{"name":"Xi\u2019an Surveying & Mapping Institute, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.apgeog.2015.12.003","article-title":"Broad Scale Forest Cover Reconstruction from Historical Topographic Maps","volume":"67","author":"Dominik","year":"2016","journal-title":"Appl. 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