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Language resources and evaluation, 54:683\u2013712, 2020."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3276591","article-title":"Segmentation is not the end of road extraction: An all-visible denoising autoencoder for connected and smooth road reconstruction","volume":"61","author":"Han L.","year":"2023","unstructured":"L. Han, L. Hou, X. Zheng, Z. Ding, H. Yang, and K. Zheng. Segmentation is not the end of road extraction: An all-visible denoising autoencoder for connected and smooth road reconstruction. IEEE Transactions on Geoscience and Remote Sensing, 61:1\u201318, 2023.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_25_1","first-page":"577","volume-title":"ECCV","author":"He Y.","year":"2022","unstructured":"Y. He, R. Garg, and A. R. Chowdhury. Td-road: Top-down road network extraction with holistic graph construction. In ECCV 2022, pages 562\u2013577. Springer, 2022."},{"key":"e_1_3_2_1_26_1","volume-title":"Evaluation of automatic road extraction. IAPRS, 32(3 SECT 4W2):151\u2013160","author":"Heipke C.","year":"1997","unstructured":"C. Heipke, H. Mayer, C. Wiedemann, and O. Jamet. Evaluation of automatic road extraction. IAPRS, 32(3 SECT 4W2):151\u2013160, 1997."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12610"},{"key":"e_1_3_2_1_28_1","first-page":"2566","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Hetang C.","year":"2024","unstructured":"C. Hetang, H. Xue, C. Le, T. Yue, W. Wang, and Y. He. Segment anything model for road network graph extraction. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 2556\u20132566, 2024."},{"key":"e_1_3_2_1_29_1","first-page":"543","volume-title":"Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","author":"Howe N. R.","year":"2019","unstructured":"N. R. Howe, J. Weinman, J. Gouwar, and A. Shamji. Deformable part models for automatically georeferencing historical map images. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 540\u2013543, 2019."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oregeorev.2019.03.016"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi12030128"},{"key":"e_1_3_2_1_32_1","first-page":"4091","volume-title":"Proceedings of the 30th ACM international conference on multimedia","author":"Huang Y.","year":"2022","unstructured":"Y. Huang, T. Lv, L. Cui, Y. Lu, and F. Wei. Layoutlmv3: Pre-training for document ai with unified text and image masking. In Proceedings of the 30th ACM international conference on multimedia, pages 4083\u20134091, 2022."},{"key":"e_1_3_2_1_33_1","volume-title":"Opencv: Open source computer vision library","year":"2015","unstructured":"Itseez. Opencv: Open source computer vision library. In OpenCV Library, 2015."},{"key":"e_1_3_2_1_34_1","volume-title":"Yolov8 - ultralytics. https:\/\/github.com\/ultralytics\/ultralytics","author":"Jocher G.","year":"2023","unstructured":"G. Jocher, A. Chaurasia, Laughing-Q, J. Fang, and A. V. Yolov8 - ultralytics. https:\/\/github.com\/ultralytics\/ultralytics, 2023. Accessed: 2025-05-11."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589132.3625579"},{"key":"e_1_3_2_1_36_1","volume-title":"Segment anything. arXiv preprint arXiv:2304.02643","author":"Kirillov A.","year":"2023","unstructured":"A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. Segment anything. arXiv preprint arXiv:2304.02643, 2023."},{"key":"e_1_3_2_1_37_1","volume-title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","author":"Lewis M.","year":"2019","unstructured":"M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461, 2019."},{"key":"e_1_3_2_1_38_1","first-page":"8","volume-title":"2024 International Joint Conference on Neural Networks (IJCNN)","author":"Li T.","unstructured":"T. Li, R. Li, S. Ye, Z. Zhang, Z. Yin, S. Li, and Z. Pan. Crtgan: Controllable road network graphs generation via transformer based gan. In 2024 International Joint Conference on Neural Networks (IJCNN), pages 1\u20138. IEEE, 2024."},{"key":"e_1_3_2_1_39_1","first-page":"3298","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Li Z.","year":"2020","unstructured":"Z. Li, Y.-Y. Chiang, S. Tavakkol, B. Shbita, J. H. Uhl, S. Leyk, and C. A. Knoblock. An automatic approach for generating rich, linked geo-metadata from historical map images. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3290\u20133298, 2020."},{"key":"e_1_3_2_1_40_1","first-page":"1724","volume-title":"ICCV","author":"Li Z.","year":"2019","unstructured":"Z. Li, J. D. Wegner, and A. Lucchi. Topological map extraction from overhead images. In ICCV, pages 1715\u20131724, 2019."},{"key":"e_1_3_2_1_41_1","volume-title":"Geolm: Empowering language models for geospatially grounded language understanding. arXiv preprint arXiv:2310.14478","author":"Li Z.","year":"2023","unstructured":"Z. Li, W. Zhou, Y.-Y. Chiang, and M. Chen. Geolm: Empowering language models for geospatially grounded language understanding. arXiv preprint arXiv:2310.14478, 2023."},{"key":"e_1_3_2_1_42_1","first-page":"12","volume-title":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","author":"Lin F.","year":"2023","unstructured":"F. Lin, C. A. Knoblock, B. Shbita, B. Vu, Z. Li, and Y.-Y. Chiang. Exploiting polygon metadata to understand raster maps-accurate polygonal feature extraction. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, pages 1\u201312, 2023."},{"issue":"13","key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","first-page":"3367","DOI":"10.3390\/rs15133367","article-title":"Ms-agan: Road extraction via multi-scale information fusion and asymmetric generative adversarial networks from high-resolution remote sensing images under complex backgrounds","volume":"15","author":"Lin S.","year":"2023","unstructured":"S. Lin, X. Yao, X. Liu, S. Wang, H.-M. Chen, L. Ding, J. Zhang, G. Chen, and Q. Mei. Ms-agan: Road extraction via multi-scale information fusion and asymmetric generative adversarial networks from high-resolution remote sensing images under complex backgrounds. Remote Sensing, 15(13):3367, 2023.","journal-title":"Remote Sensing"},{"key":"e_1_3_2_1_44_1","first-page":"5397","volume-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Lin Y.","year":"2024","unstructured":"Y. Lin and Y.-Y. Chiang. Hyper-local deformable transformers for text spotting on historical maps. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 5387\u20135397, 2024."},{"key":"e_1_3_2_1_45_1","first-page":"17638","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Lindenberger P.","year":"2023","unstructured":"P. Lindenberger, P.-E. Sarlin, and M. Pollefeys. Lightglue: Local feature matching at light speed. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, pages 17627\u201317638, 2023."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12794"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2023.3310915"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3117076"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2613409"},{"key":"e_1_3_2_1_50_1","first-page":"194","volume-title":"Companion of the 2023 International Conference on Management of Data","author":"Musleh M.","year":"2023","unstructured":"M. Musleh and M. Mokbel. A demonstration of kamel: A scalable bert-based system for trajectory imputation. In Companion of the 2023 International Conference on Management of Data, pages 191\u2013194, 2023."},{"key":"e_1_3_2_1_51_1","volume-title":"let machine learning models and llms collaborate for enhanced in-context learning in healthcare. arXiv preprint arXiv:2405.06270","author":"Nazary F.","year":"2024","unstructured":"F. Nazary, Y. Deldjoo, T. Di Noia, and E. Di Sciascio. Xai4llm. let machine learning models and llms collaborate for enhanced in-context learning in healthcare. arXiv preprint arXiv:2405.06270, 2024."},{"key":"e_1_3_2_1_52_1","volume-title":"Gpt-4o technical report. https:\/\/openai.com\/research\/gpt-4o","author":"AI.","year":"2024","unstructured":"OpenAI. Gpt-4o technical report. https:\/\/openai.com\/research\/gpt-4o, 2024. Accessed: YYYY-MM-DD."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3268647","article-title":"A semantics-geometry framework for road extraction from remote sensing images","volume":"20","author":"Qiu L.","year":"2023","unstructured":"L. Qiu, D. Yu, C. Zhang, and X. Zhang. A semantics-geometry framework for road extraction from remote sensing images. IEEE Geoscience and Remote Sensing Letters, 20:1\u20135, 2023.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"e_1_3_2_1_54_1","volume-title":"Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714","author":"Ravi N.","year":"2024","unstructured":"N. Ravi, V. Gabeur, Y.-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R\u00e4dle, C. Rolland, L. Gustafson, et al. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714, 2024."},{"key":"e_1_3_2_1_55_1","volume-title":"You only look once: Unified, real-time object detection","author":"Redmon J.","year":"2016","unstructured":"J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. You only look once: Unified, real-time object detection, 2016."},{"key":"e_1_3_2_1_56_1","first-page":"146","volume-title":"Document Analysis and Recognition-ICDAR 2021:  16th International Conference","author":"Shen Z.","year":"2021","unstructured":"Z. Shen, R. Zhang, M. Dell, B. C. G. Lee, J. Carlson, and W. Li. Layoutparser: A unified toolkit for deep learning based document image analysis. In Document Analysis and Recognition-ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5\u201310, 2021, Proceedings, Part I 16, pages 131\u2013146. Springer, 2021."},{"key":"e_1_3_2_1_57_1","first-page":"439","volume-title":"ECCV","author":"Shit S.","unstructured":"S. Shit, R. Koner, B. Wittmann, J. Paetzold, I. Ezhov, H. Li, J. Pan, S. Sharifzadeh, G. Kaissis, V. Tresp, et al. Relationformer: A unified framework for image-to-graph generation. In ECCV, pages 422\u2013439. Springer, 2022."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"crossref","first-page":"102219","DOI":"10.1016\/j.compenvurbsys.2024.102219","article-title":"Estimating the density of urban trees in 1890s leeds and edinburgh using object detection on historical maps. Computers","volume":"115","author":"Smith E. S.","year":"2025","unstructured":"E. S. Smith, C. Fleet, S. King, W. Mackaness, H. Walker, and C. E. Scott. Estimating the density of urban trees in 1890s leeds and edinburgh using object detection on historical maps. Computers, Environment and Urban Systems, 115:102219, 2025.","journal-title":"Environment and Urban Systems"},{"key":"e_1_3_2_1_59_1","volume-title":"Citeseer","author":"Subcommittee G. D.","year":"2006","unstructured":"G. D. Subcommittee. Fgdc digital cartographic standard for geologic map symbolization. Technical report, Citeseer, 2006."},{"key":"e_1_3_2_1_60_1","first-page":"19264","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"Tang Z.","year":"2023","unstructured":"Z. Tang, Z. Yang, G. Wang, Y. Fang, Y. Liu, C. Zhu, M. Zeng, C. Zhang, and M. Bansal. Unifying vision, text, and layout for universal document processing. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pages 19254\u201319264, 2023."},{"key":"e_1_3_2_1_61_1","first-page":"155","article-title":"Spatial information inference net: Road extraction using road-specific contextual information","volume":"158","author":"Tao C.","year":"2019","unstructured":"C. Tao, J. Qi, Y. Li, H. Wang, and H. Li. Spatial information inference net: Road extraction using road-specific contextual information. 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In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pages 14176\u201314186, 2022."},{"key":"e_1_3_2_1_66_1","volume-title":"Harnessing the power of artificial intelligence and machine learning in mineral exploration\u2014opportunities and cautionary notes. SEG Discovery, (127):19\u201331","author":"Woodhead J.","year":"2021","unstructured":"J. Woodhead and M. Landry. Harnessing the power of artificial intelligence and machine learning in mineral exploration\u2014opportunities and cautionary notes. SEG Discovery, (127):19\u201331, 2021."},{"key":"e_1_3_2_1_67_1","first-page":"9","volume-title":"Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems","author":"Wu S.","year":"2023","unstructured":"S. Wu, Y. Chen, K. Schindler, and L. Hurni. Cross-attention spatio-temporal context transformer for semantic segmentation of historical maps. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, pages 1\u20139, 2023."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1080\/15481603.2021.2023840"},{"issue":"1","key":"e_1_3_2_1_69_1","doi-asserted-by":"crossref","first-page":"2494883","DOI":"10.1080\/15481603.2025.2494883","article-title":"Mapsam: adapting segment anything model for automated feature detection in historical maps","volume":"62","author":"Xia X.","year":"2025","unstructured":"X. Xia, D. Zhang, W. Song, W. Huang, and L. Hurni. Mapsam: adapting segment anything model for automated feature detection in historical maps. GIScience & Remote Sensing, 62(1):2494883, 2025.","journal-title":"GIScience & Remote Sensing"},{"key":"e_1_3_2_1_70_1","first-page":"1","article-title":"Rngdet: Road network graph detection by transformer in aerial images","volume":"60","author":"Xu Z.","year":"2022","unstructured":"Z. Xu, Y. Liu, L. Gan, Y. Sun, X. Wu, M. Liu, and L. Wang. Rngdet: Road network graph detection by transformer in aerial images. IEEE Trans. Geosci. Remote Sens., 60:1\u201312, 2022.","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"e_1_3_2_1_71_1","volume-title":"Elevating legal llm responses: Harnessing trainable logical structures and semantic knowledge with legal reasoning. arXiv preprint arXiv:2502.07912","author":"Yao R.","year":"2025","unstructured":"R. Yao, Y. Wu, C. Wang, J. Xiong, F. Wang, and X. Liu. Elevating legal llm responses: Harnessing trainable logical structures and semantic knowledge with legal reasoning. arXiv preprint arXiv:2502.07912, 2025."},{"issue":"6","key":"e_1_3_2_1_72_1","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1016\/j.gsf.2020.05.016","article-title":"A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence","volume":"11","author":"Yeomans C.","year":"2020","unstructured":"C. Yeomans, R. Shail, S. Grebby, V. Nyk\u00e4nen, M. Middleton, and P. Lusty. A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidence. Geoscience Frontiers, 11(6):2067\u20132081, 2020.","journal-title":"Geoscience Frontiers"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3241679"},{"key":"e_1_3_2_1_74_1","first-page":"130","volume-title":"Document Analysis and Recognition-ICDAR 2021:  16th International Conference","author":"Zhang P.","year":"2021","unstructured":"P. Zhang, C. Li, L. Qiao, Z. Cheng, S. Pu, Y. Niu, and F. Wu. Vsr: a unified framework for document layout analysis combining vision, semantics and relations. In Document Analysis and Recognition-ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5\u201310, 2021, Proceedings, Part I 16, pages 115\u2013130. Springer, 2021."},{"key":"e_1_3_2_1_75_1","volume-title":"Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159","author":"Zhu X.","year":"2020","unstructured":"X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai. 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