{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:43:50Z","timestamp":1764225830200,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["41901379","42071384","AR2205"],"award-info":[{"award-number":["41901379","42071384","AR2205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Scientific Research Foundation of the Chinese Academy of Surveying and Mapping","award":["41901379","42071384","AR2205"],"award-info":[{"award-number":["41901379","42071384","AR2205"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There is a critical need for detection of administrative regions through network map pictures in map censorship tasks, which can be implemented by target detection technology. However, on map images there tend to be numerous administrative regions overlaying map annotations and symbols, thus making it difficult to accurately detect each region. Using a RetinaNet-based target detection model integrating ResNet50 and a feature pyramid network (FPN), this study built a multi-target model and a single-target cascading model from three single-target models by taking Taiwan, Tibet, and the Chinese mainland as target examples. Two models were evaluated both in classification and localization accuracy to investigate their administrative region detection performance. The results show that the single-target cascading model was able to detect more administrative regions, with a higher f1_score of 0.86 and mAP of 0.85 compared to the multi-target model (0.56 and 0.52, respectively). Furthermore, location box size distribution from the single-target cascading model looks more similar to that of manually annotated box sizes, which signifies that the proposed cascading model is superior to the multi-target model. This study is promising in providing support for computer map reading and intelligent map censorship.<\/jats:p>","DOI":"10.3390\/s22197594","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:12:21Z","timestamp":1665378741000},"page":"7594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Comparison of RetinaNet-Based Single-Target Cascading and Multi-Target Detection Models for Administrative Regions in Network Map Pictures"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9117-9250","authenticated-orcid":false,"given":"Kaixuan","family":"Du","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China"},{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"given":"Xianghong","family":"Che","sequence":"additional","affiliation":[{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"given":"Jiping","family":"Liu","sequence":"additional","affiliation":[{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"given":"An","family":"Luo","sequence":"additional","affiliation":[{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"given":"Ruiyuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9275-3250","authenticated-orcid":false,"given":"Shenghua","family":"Xu","sequence":"additional","affiliation":[{"name":"Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"ref_1","first-page":"237","article-title":"Discussions on the Attributes of Cartography and the Value of Map","volume":"44","author":"Jiayao","year":"2015","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_2","first-page":"92","article-title":"Research on the method of fast mining internet problem map picture","volume":"40","author":"Huang","year":"2017","journal-title":"Geomatrics Spat. Inf. Technol."},{"key":"ref_3","first-page":"41","article-title":"Problem Map Picture current Situation Analysis and Countermeasures","volume":"20","author":"Zhou","year":"2018","journal-title":"Geomat. Technol. Equip."},{"key":"ref_4","first-page":"570","article-title":"Intelligent Detection of \u201cProblematic Map\u201d Using Convolutional Neural Network","volume":"46","author":"Ren","year":"2021","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_5","unstructured":"Roberts, L.G. (1963). Machine Perception of Three-Dimensional Solids. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_6","unstructured":"Lienhart, R., and Maydt, J. (2002, January 22\u201325). An Extended Set of Haar-like Features for Rapid Object Detection. Proceedings of the International Conference on Image Processing, Rochester, NY, USA."},{"key":"ref_7","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008, January 23\u201328). A Discriminatively Trained, Multiscale, Deformable Part Model. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587597"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","article-title":"A Comparison of Methods for Multiclass Support Vector Machines","volume":"13","author":"Hsu","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1023\/B:VISI.0000011202.85607.00","article-title":"Object Detection Using the Statistics of Parts","volume":"56","author":"Schneiderman","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"104384","DOI":"10.1016\/j.compbiomed.2021.104384","article-title":"Automatic Tip Detection of Surgical Instruments in Biportal Endoscopic Spine Surgery","volume":"133","author":"Cho","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_12","unstructured":"Cao, G., Xie, X., Yang, W., Liao, Q., Shi, G., and Wu, J. (2018, January 10). Feature-Fused SSD: Fast Detection for Small Objects. Proceedings of the Ninth International Conference on Graphic and Image Processing (ICGIP 2017), Qingdao, China."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tripathi, S., Dane, G., Kang, B., Bhaskaran, V., and Nguyen, T. (2017). Lcdet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, 411\u2013420.","DOI":"10.1109\/CVPRW.2017.56"},{"key":"ref_14","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected Crfs. arXiv."},{"key":"ref_15","first-page":"1137","article-title":"Faster R-Cnn: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100301","DOI":"10.1016\/j.cosrev.2020.100301","article-title":"A Comprehensive and Systematic Look up into Deep Learning Based Object Detection Techniques: A Review","volume":"38","author":"Sharma","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_18","first-page":"379","article-title":"R-Fcn: Object Detection via Region-Based Fully Convolutional Networks","volume":"29","author":"Dai","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 8\u201316). Ssd: Single Shot Multibox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intel."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","article-title":"A Survey of Deep Learning-Based Object Detection","volume":"7","author":"Jiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent Advances in Deep Learning for Object Detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2015.03.015","article-title":"A Survey on Face Detection in the Wild: Past, Present and Future","volume":"138","author":"Zafeiriou","year":"2015","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neucom.2017.05.013","article-title":"Facial Feature Point Detection: A Comprehensive Survey","volume":"275","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1109\/TPAMI.2014.2366765","article-title":"Text Detection and Recognition in Imagery: A Survey","volume":"37","author":"Ye","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.1109\/TIP.2016.2554321","article-title":"Text Detection, Tracking and Recognition in Video: A Comprehensive Survey","volume":"25","author":"Yin","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TITS.2013.2266661","article-title":"Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis","volume":"14","author":"Sivaraman","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1109\/TITS.2012.2209421","article-title":"Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey","volume":"13","author":"Mogelmose","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1109\/TPAMI.2009.122","article-title":"Survey of Pedestrian Detection for Advanced Driver Assistance Systems","volume":"32","author":"Geronimo","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.neucom.2018.01.092","article-title":"Computer Vision and Deep Learning Techniques for Pedestrian Detection and Tracking: A Survey","volume":"300","author":"Brunetti","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.patrec.2015.09.010","article-title":"A Survey on Representation-Based Classification and Detection in Hyperspectral Remote Sensing Imagery","volume":"83","author":"Li","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A Survey on Object Detection in Optical Remote Sensing Images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"25363","DOI":"10.1109\/ACCESS.2018.2823501","article-title":"Intelligent Map Reader: A Framework for Topographic Map Understanding with Deep Learning and Gazetteer","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-017-0254-7","article-title":"Panicle-SEG: A Robust Image Segmentation Method for Rice Panicles in the Field Based on Deep Learning and Superpixel Optimization","volume":"13","author":"Xiong","year":"2017","journal-title":"Plant Methods"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"59613","DOI":"10.1109\/ACCESS.2019.2915368","article-title":"Vehicle Detection in Aerial Images Using Rotation-Invariant Cascaded Forest","volume":"7","author":"Ma","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, S., Cai, T., Tang, X., Zhang, Y., and Wang, C. (2022). Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet. Entropy, 24.","DOI":"10.3390\/e24010112"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103589","DOI":"10.1016\/j.bspc.2022.103589","article-title":"Detection of Cervical Lesions in Colposcopic Images Based on the RetinaNet Method","volume":"75","author":"Chen","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_40","first-page":"171","article-title":"Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection","volume":"116","author":"Jaeger","year":"2020","journal-title":"Proc. Mach. Learn. Health Workshop PMLR"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.cageo.2016.04.013","article-title":"Assessing the Impact of Graphical Quality on Automatic Text Recognition in Digital Maps","volume":"93","author":"Chiang","year":"2016","journal-title":"Comput. Geosci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6265","DOI":"10.1109\/TGRS.2016.2567481","article-title":"Guided Superpixel Method for Topographic Map Processing","volume":"54","author":"Miao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2018.02.102","article-title":"A Review of Recent Advances in Scanned Topographic Map Processing","volume":"328","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5047","DOI":"10.1109\/TGRS.2011.2157697","article-title":"Automatic Feature Extraction and Text Recognition from Scanned Topographic Maps","volume":"49","author":"Pezeshk","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","unstructured":"Zhou, X. (2019). GeoAI-Enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data. [Ph.D. Thesis, Arizona State University]."},{"key":"ref_46","unstructured":"Zhou, X., Li, W., Arundel, S.T., and Liu, J. (2018). Deep Convolutional Neural Networks for Map-Type Classification. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6978","DOI":"10.1109\/ACCESS.2019.2963213","article-title":"Undefined Automated Extraction of Human Settlement Patterns from Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks","volume":"8","author":"Uhl","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Courtial, A., El Ayedi, A., Touya, G., and Zhang, X. (2020). Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9050338"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 16\u201320). Libra R-Cnn: Towards Balanced Learning for Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"An, T.-K., and Kim, M.-H. (2010, January 23\u201324). A New Diverse AdaBoost Classifier. Proceedings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence, Sanya, China.","DOI":"10.1109\/AICI.2010.82"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pan, S., Wang, Y., Liu, C., and Ding, X. (2015, January 18\u201322). A Discriminative Cascade CNN Model for Offline Handwritten Digit Recognition. Proceedings of the 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, Japan.","DOI":"10.1109\/MVA.2015.7153240"},{"key":"ref_52","first-page":"233","article-title":"Small Object Detection Oriented Improved-RetinaNet Model and Its Application","volume":"48","author":"Luo","year":"2021","journal-title":"Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7594\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:47:37Z","timestamp":1760143657000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7594"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,7]]},"references-count":52,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197594"],"URL":"https:\/\/doi.org\/10.3390\/s22197594","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,10,7]]}}}