{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:38:38Z","timestamp":1780616318593,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":48,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819658084","type":"print"},{"value":"9789819658091","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-5809-1_8","type":"book-chapter","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:33:53Z","timestamp":1745606033000},"page":"131-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Comprehensive Framework for\u00a0Fine-Grained Object Recognition in\u00a0Remote Sensing"],"prefix":"10.1007","author":[{"given":"Xin","family":"Chi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghua","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiting","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s11235-024-01148-z","volume":"87","author":"R Ahmad","year":"2024","unstructured":"Ahmad, R.: Smart remote sensing network for disaster management: an overview. Telecommun. Syst. 87, 213\u2013237 (2024)","journal-title":"Telecommun. Syst."},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-031-65703-0_11","volume":"2","author":"S Panda","year":"2024","unstructured":"Panda, S., Yadav, V.-S., Tripathi, V.-K.: Application of remote sensing in natural resource management. Sustain. Dev. Geospat. Technol. 2, 173\u2013180 (2024)","journal-title":"Sustain. Dev. Geospat. Technol."},{"issue":"1","key":"8_CR3","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/s43017-022-00373-x","volume":"4","author":"N Casagli","year":"2023","unstructured":"Casagli, N., Intrieri, E., Tofani, V., Gigli, G., Raspini, F.: Landslide detection, monitoring and prediction with remote-sensing techniques. Nat. Rev. Earth Environ. 4(1), 51\u201364 (2023)","journal-title":"Nat. Rev. Earth Environ."},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"112577","DOI":"10.1016\/j.rse.2021.112577","volume":"264","author":"M Kucharczyk","year":"2021","unstructured":"Kucharczyk, M., Hugenholtz, C.-H.: Remote sensing of natural hazard-related disasters with small drones: global trends, biases, and research opportunities. Remote Sens. Environ. 264, 112577 (2021)","journal-title":"Remote Sens. Environ."},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.ijdrr.2018.09.015","volume":"33","author":"K Kaku","year":"2019","unstructured":"Kaku, K.: Satellite remote sensing for disaster management support: a holistic and staged approach based on case studies in Sentinel Asia. Int. J. Disaster Risk Reduction 33, 417\u2013432 (2019)","journal-title":"Int. J. Disaster Risk Reduction"},{"key":"8_CR6","doi-asserted-by":"publisher","first-page":"105418","DOI":"10.1016\/j.autcon.2024.105418","volume":"163","author":"N Ejaz","year":"2024","unstructured":"Ejaz, N., Choudhury, S.: Computer vision in drone imagery for infrastructure management. Autom. Constr. 163, 105418 (2024)","journal-title":"Autom. Constr."},{"issue":"10","key":"8_CR7","first-page":"649","volume":"13","author":"J Fan","year":"2019","unstructured":"Fan, J., Saadeghvaziri, M.-A.: Applications of drones in infrastructures: challenges and opportunities. Int. J. Mech. Mechatron. Eng. 13(10), 649\u2013655 (2019)","journal-title":"Int. J. Mech. Mechatron. Eng."},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Zhu, S., Yang, K.: Deep remote sensing object detection for smart city applications. In: 2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII), pp. 167\u2013171 (2024). https:\/\/doi.org\/10.1109\/ICMIII62623.2024.00039","DOI":"10.1109\/ICMIII62623.2024.00039"},{"issue":"1","key":"8_CR9","first-page":"012052","volume":"2608","author":"Y Liu","year":"2023","unstructured":"Liu, Y.: Application of remote sensing technology in smart city construction and planning. J. Phys: Conf. Ser. 2608(1), 012052 (2023)","journal-title":"J. Phys: Conf. Ser."},{"issue":"1","key":"8_CR10","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1038\/s41597-023-02576-3","volume":"10","author":"Y Xi","year":"2023","unstructured":"Xi, Y., et al.: A satellite imagery dataset for long-term sustainable development in united states cities. Sci. Data 10(1), 866 (2023)","journal-title":"Sci. Data"},{"key":"8_CR11","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.isprsjprs.2021.12.004","volume":"184","author":"X Sun","year":"2022","unstructured":"Sun, X., et al.: FAIR1M: a benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery. ISPRS J. Photogramm. Remote. Sens. 184, 116\u2013130 (2022)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"issue":"4","key":"8_CR12","doi-asserted-by":"publisher","first-page":"747","DOI":"10.3390\/rs13040747","volume":"13","author":"Y Di","year":"2021","unstructured":"Di, Y., Jiang, Z., Zhang, H.: A public dataset for fine-grained ship classification in optical remote sensing images. Remote Sens. 13(4), 747 (2021)","journal-title":"Remote Sens."},{"key":"8_CR13","first-page":"1","volume":"62","author":"Z Guo","year":"2024","unstructured":"Guo, Z., et al.: MSRIP-Net: addressing interpretability and accuracy challenges in aircraft fine-grained recognition of remote sensing images. IEEE Trans. Geosci. Remote Sens. 62, 1\u201317 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"3","key":"8_CR14","doi-asserted-by":"publisher","first-page":"5243","DOI":"10.32604\/cmc.2024.050879","volume":"79","author":"S Song","year":"2024","unstructured":"Song, S., Zhang, R., Hu, M., Huang, F.: Fine-grained ship recognition based on visible and near-infrared multimodal remote sensing images: dataset, methodology and evaluation. Comput. Mater. Continua 79(3), 5243\u20135271 (2024)","journal-title":"Comput. Mater. Continua"},{"key":"8_CR15","first-page":"1","volume":"61","author":"G Cheng","year":"2023","unstructured":"Cheng, G., Li, Q., Wang, G., Xie, X., Min, L., Han, J.: SFRNet: fine-grained oriented object recognition via separate feature refinement. IEEE Trans. Geosci. Remote Sens. 61, 1\u201310 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"14","key":"8_CR16","doi-asserted-by":"publisher","first-page":"3146","DOI":"10.3390\/electronics12143146","volume":"12","author":"Q Guan","year":"2023","unstructured":"Guan, Q., Liu, Y., Chen, L., Zhao, S., Li, G.: Aircraft detection and fine-grained recognition based on high-resolution remote sensing images. Electronics 12(14), 3146 (2023)","journal-title":"Electronics"},{"key":"8_CR17","first-page":"1","volume":"60","author":"Y Han","year":"2021","unstructured":"Han, Y., Yang, X., Pu, T., Peng, Z.: Fine-grained recognition for oriented ship against complex scenes in optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201318 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"24","key":"8_CR18","doi-asserted-by":"publisher","first-page":"4187","DOI":"10.3390\/rs12244187","volume":"12","author":"W Liang","year":"2020","unstructured":"Liang, W., Li, J., Diao, W., Sun, X., Fu, K., Wu, Y.: FGATR-Net: automatic network architecture design for fine-grained aircraft type recognition in remote sensing images. Remote Sens. 12(24), 4187 (2020)","journal-title":"Remote Sens."},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Osswald-Cankaya, M., Mayer, H.: Fine-grained airplane recognition in satellite images based on task separation and orientation normalization. In: 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 6545\u20136548 (2023). https:\/\/doi.org\/10.1109\/IGARSS52108.2023.10283156","DOI":"10.1109\/IGARSS52108.2023.10283156"},{"issue":"22","key":"8_CR20","doi-asserted-by":"publisher","first-page":"3755","DOI":"10.3390\/w14223755","volume":"14","author":"G Zhao","year":"2022","unstructured":"Zhao, G., Yao, P., Fu, L., Zhang, Z., Lu, S., Long, T.: A deep learning method based on two-stage CNN framework for recognition of Chinese reservoirs with Sentinel-2 images. Water 14(22), 3755 (2022)","journal-title":"Water"},{"key":"8_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2020.3011973","volume":"19","author":"Z Huang","year":"2021","unstructured":"Huang, Z., Wang, F., You, H., Hu, Y.: Shadow information-based slender targets detection method in optical satellite images. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2021)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"6","key":"8_CR22","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.3390\/rs14061385","volume":"14","author":"Z Huang","year":"2022","unstructured":"Huang, Z., Wang, F., You, H., Hu, Y.: Imaging parameters-considered slender target detection in optical satellite images. Remote Sens. 14(6), 1385 (2022)","journal-title":"Remote Sens."},{"key":"8_CR23","doi-asserted-by":"publisher","unstructured":"Liu, Z., Yuan, L., Weng, L., Yang, Y.: A high resolution optical satellite image dataset for ship recognition and some new baselines. In: 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM), pp. 324\u2013331 (2017). https:\/\/doi.org\/10.5220\/0006120603240331","DOI":"10.5220\/0006120603240331"},{"key":"8_CR24","doi-asserted-by":"publisher","unstructured":"Shermeyer, J., Hossler, T., Van-Etten, A., Hogan, D., Lewis, R., Kim, D.: RarePlanes: synthetic data takes flight. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 207\u2013217 (2021). https:\/\/doi.org\/10.1109\/WACV48630.2021.00025","DOI":"10.1109\/WACV48630.2021.00025"},{"key":"8_CR25","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1109\/JSTARS.2020.2981686","volume":"13","author":"X Zhang","year":"2020","unstructured":"Zhang, X., Lv, Y., Yao, L., Xiong, W., Fu, C.: A new benchmark and an attribute-guided multilevel feature representation network for fine-grained ship classification in optical remote sensing images. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13, 1271\u20131285 (2020)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"8_CR26","doi-asserted-by":"publisher","unstructured":"Cai, X., Lai, Q., Wang, Y., Wang, W., Sun, Z., Yao, Y.: Poly kernel inception network for remote sensing detection. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 27706\u201327716 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02617","DOI":"10.1109\/CVPR52733.2024.02617"},{"key":"8_CR27","doi-asserted-by":"publisher","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M. M., Yang, J., Li, X.: Large selective kernel network for remote sensing object detection. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 16794\u201316805 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.01540","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"8_CR28","first-page":"9969","volume":"35","author":"Y Tang","year":"2022","unstructured":"Tang, Y., Han, K., Guo, J., Xu, C., Xu, C., Wang, Y.: GhostNetv2: enhance cheap operation with long-range attention. Adv. Neural. Inf. Process. Syst. 35, 9969\u20139982 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"8_CR29","doi-asserted-by":"publisher","unstructured":"Tan, M., Pang, R., Le, Q. V.: EfficientDet: scalable and efficient object detection. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10781\u201310790 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"issue":"11","key":"8_CR30","doi-asserted-by":"publisher","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","volume":"20","author":"J Ma","year":"2018","unstructured":"Ma, J., et al.: Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans. Multimedia 20(11), 3111\u20133122 (2018)","journal-title":"IEEE Trans. Multimedia"},{"key":"8_CR31","doi-asserted-by":"publisher","unstructured":"Ma, J., et al.: YOLO-FaceV2: a scale and occlusion aware face detector. arXiv arXiv:2208.02019 (2022). https:\/\/doi.org\/10.48550\/arXiv.2208.02019","DOI":"10.48550\/arXiv.2208.02019"},{"key":"8_CR32","doi-asserted-by":"publisher","unstructured":"Zhou, Y., et al.: MMRotate: a rotated object detection benchmark using PyTorch. In: 30th ACM International Conference on Multimedia, pp. 7331\u20137334 (2022). https:\/\/doi.org\/10.1145\/3503161.3548541","DOI":"10.1145\/3503161.3548541"},{"key":"8_CR33","doi-asserted-by":"publisher","unstructured":"Loshchilov, I.: Decoupled weight decay regularization. arXiv arXiv:1711.05101 (2017). https:\/\/doi.org\/10.48550\/arXiv.1711.05101","DOI":"10.48550\/arXiv.1711.05101"},{"issue":"4","key":"8_CR34","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1007\/s00521-021-06027-1","volume":"34","author":"Y Zeng","year":"2022","unstructured":"Zeng, Y., Guo, Y., Li, J.: Recognition and extraction of high-resolution satellite remote sensing image buildings based on deep learning. Neural Comput. Appl. 34(4), 2691\u20132706 (2022)","journal-title":"Neural Comput. Appl."},{"key":"8_CR35","doi-asserted-by":"publisher","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., Han, J.: Oriented R-CNN for object detection. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3520\u20133529 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00350","DOI":"10.1109\/ICCV48922.2021.00350"},{"issue":"5","key":"8_CR36","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.1109\/TCSVT.2022.3222906","volume":"33","author":"L Dai","year":"2022","unstructured":"Dai, L., Liu, H., Tang, H., Wu, Z., Song, P.: AO2-DETR: arbitrary-oriented object detection transformer. IEEE Trans. Circ. Syst. Video Technol. 33(5), 2342\u20132356 (2022)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"8_CR37","first-page":"1","volume":"62","author":"Y Zeng","year":"2024","unstructured":"Zeng, Y., Chen, Y., Yang, X., Li, Q., Yan, J.: ARS-DETR: aspect ratio-sensitive detection transformer for aerial oriented object detection. IEEE Trans. Geosci. Remote Sens. 62, 1\u201315 (2024)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"8_CR38","doi-asserted-by":"publisher","unstructured":"Yang, X., Yan, J., Feng, Z., He, T.: R3Det: refined single-stage detector with feature refinement for rotating object. In: 35th AAAI Conference on Artificial Intelligence, pp. 3163\u20133171 (2021). https:\/\/doi.org\/10.1609\/aaai.v35i4.16426","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"8_CR39","first-page":"1","volume":"60","author":"J Han","year":"2021","unstructured":"Han, J., Ding, J., Li, J., Xia, G.-S.: Align deep features for oriented object detection. IEEE Trans. Geosci. Remote Sens. 60, 1\u201311 (2021)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"8_CR40","unstructured":"YOLOv8. https:\/\/github.com\/ultralytics\/ultralytics\/tree\/main. Accessed 12 Nov 2023"},{"issue":"4","key":"8_CR41","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","volume":"43","author":"Y Xu","year":"2020","unstructured":"Xu, Y., et al.: Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1452\u20131459 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR42","doi-asserted-by":"publisher","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.-S., Lu, Q.: Learning RoI transformer for oriented object detection in aerial images. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2849\u20132858 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00296","DOI":"10.1109\/CVPR.2019.00296"},{"key":"8_CR43","first-page":"1","volume":"73","author":"P Sun","year":"2024","unstructured":"Sun, P., Zheng, Y., Wu, W., Xu, W., Bai, S., Lu, X.: Learning critical features for arbitrary-oriented object detection in remote sensing optical images. IEEE Trans. Instrum. Meas. 73, 1\u201312 (2024)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"8_CR44","first-page":"1","volume":"36","author":"Y Yu","year":"2024","unstructured":"Yu, Y., Yang, X., Li, Q., Zhou, Y., Da, F., Yan, J.: H2RBox-v2: incorporating symmetry for boosting horizontal box supervised oriented object detection. Adv. Neural. Inf. Process. Syst. 36, 1\u201314 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"8_CR45","doi-asserted-by":"publisher","unstructured":"Sun, P., Zheng, Y., Wu, W., Xu, W., Bai, S.: Metric-aligned sample selection and critical feature sampling for oriented object detection. arXiv arXiv:2306.16718 (2023). https:\/\/doi.org\/10.48550\/arXiv.2306.16718","DOI":"10.48550\/arXiv.2306.16718"},{"issue":"29","key":"8_CR46","doi-asserted-by":"publisher","first-page":"45585","DOI":"10.1007\/s11042-023-15584-7","volume":"82","author":"W Zha","year":"2023","unstructured":"Zha, W., Hu, L., Sun, Y., Li, Y.: ENGD-BiFPN: a remote sensing object detection model based on grouped deformable convolution for power transmission towers. Multimedia Tools Appl. 82(29), 45585\u201345604 (2023)","journal-title":"Multimedia Tools Appl."},{"key":"8_CR47","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.egyr.2023.04.057","volume":"9","author":"W Zha","year":"2023","unstructured":"Zha, W., Hu, L., Duan, C., Li, Y.: Semi-supervised learning-based satellite remote sensing object detection method for power transmission towers. Energy Rep. 9, 15\u201327 (2023)","journal-title":"Energy Rep."},{"issue":"20","key":"8_CR48","doi-asserted-by":"publisher","first-page":"4183","DOI":"10.3390\/rs13204183","volume":"13","author":"Z Huang","year":"2021","unstructured":"Huang, Z., Wang, F., You, H., Hu, Y.: STC-Det: a slender target detector combining shadow and target information in optical satellite images. Remote Sens. 13(20), 4183 (2021)","journal-title":"Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Computational Visual Media"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-5809-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T18:34:00Z","timestamp":1745606040000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-5809-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819658084","9789819658091"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-5809-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Visual Media","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong SAR","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccvm.org\/2025\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}