{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T11:54:25Z","timestamp":1782474865484,"version":"3.54.5"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"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":["Vis Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00371-025-03810-2","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:17:04Z","timestamp":1740010624000},"page":"7367-7377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Unet-based image segmentation and binarization for water level detection"],"prefix":"10.1007","volume":"41","author":[{"given":"Peng","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuming","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuangao","family":"Ai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houming","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonghan","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"key":"3810_CR1","unstructured":"Jianzhang, L.: Pay attention to the impact of climate change and design the strictest water resources management system\u2014interview with academician of Chinese academy of engineering and Nanjing Water Conservancy Research InstituteZhang Jianyun. China Water Conserv. 06, 17\u201318 (2010)"},{"issue":"11","key":"3810_CR2","first-page":"119","volume":"37","author":"Yu Wang","year":"2023","unstructured":"Wang, Yu., Wei, Yu., Chuanmeng, S., et al.: Research on intelligent detection method of water level in complex and harsh environment. J. Electron. Meas. Instrum. 37(11), 119\u2013131 (2023)","journal-title":"J. Electron. Meas. Instrum."},{"key":"3810_CR3","doi-asserted-by":"crossref","unstructured":"Tang, Y., Guo, C.: Research on water gauge recognition based on deep learning and image processing. In: Proceedings of the 2021 4th International Conference on E-Business, Information Management and Computer Science, pp. 150\u2013156 (2021)","DOI":"10.1145\/3511716.3511741"},{"key":"3810_CR4","doi-asserted-by":"crossref","unstructured":"Wang, L., Liu, X., Liu, Y., et al.: Knowledge graph-based method for intelligent generation of emergency plans for water conservancy projects. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3302399"},{"key":"3810_CR5","volume":"39","author":"F Hussain","year":"2022","unstructured":"Hussain, F., Wu, R.S., Shih, D.S.: Water table response to rainfall and groundwater simulation using physics-based numerical model: WASH123D. J. Hydrol.: Reg. Stud. 39, 100988 (2022)","journal-title":"J. Hydrol.: Reg. Stud."},{"issue":"5","key":"3810_CR6","volume":"1477","author":"AM Yunita","year":"2020","unstructured":"Yunita, A.M., Wardah, N.N., Sugiarto, A., et al.: Water level measurements at the cikupa pandeglang bantendam using fuzzy sugenowith microcontroler-based ultrasonik sensor. J. Phys.: Conf. Ser. 1477(5), 052048 (2020)","journal-title":"J. Phys.: Conf. Ser."},{"key":"3810_CR7","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1007\/s00371-013-0892-3","volume":"30","author":"S Liu","year":"2014","unstructured":"Liu, S., Cui, W., Wu, Y., et al.: A survey on information visualization: recent advances and challenges. Vis. Comput. 30, 1373\u20131393 (2014)","journal-title":"Vis. Comput."},{"issue":"7","key":"3810_CR8","doi-asserted-by":"publisher","first-page":"2309","DOI":"10.1007\/s11269-022-03144-x","volume":"36","author":"J Li","year":"2022","unstructured":"Li, J., Zheng, W., Lu, C.: An accurate leakage localization method for water supply network based on deep learning network. Water Resour. Manage 36(7), 2309\u20132325 (2022)","journal-title":"Water Resour. Manage"},{"issue":"7","key":"3810_CR9","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.3390\/agriculture13071283","volume":"13","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Kong, X., Guo, K., et al.: Intelligent extraction of terracing using the ASPP ArrU-net deep learning model for soil and water conservation on the loessplateau. Agriculture 13(7), 1283 (2023)","journal-title":"Agriculture"},{"issue":"1","key":"3810_CR10","first-page":"63","volume":"30","author":"L Dai","year":"2024","unstructured":"Dai, L., Sheng, B., Chen, T., Wu, Q., Liu, R., Cai, C., et al.: A deep learning system for predicting time to progression of diabetic retinopathy. Nat. Med. 30(1), 63\u201371 (2024)","journal-title":"Nat. Med."},{"key":"3810_CR11","unstructured":"Sheng, B., Guan, Z., Lim, L.L., et al.: Large language models for diabetes care: Potentials and prospects. Sci. Bull. S2095-9273(24)00004 (2024)"},{"key":"3810_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107235","volume":"127","author":"CM Ranieri","year":"2024","unstructured":"Ranieri, C.M., Foletto, A.V.K., Garcia, R.D., et al.: Water level identification with laser sensors, inertial units, and machine learning. Eng. Appl. Artif. Intell. 127, 107235 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"2","key":"3810_CR13","doi-asserted-by":"publisher","first-page":"960","DOI":"10.3390\/s23020960","volume":"23","author":"M Miller","year":"2023","unstructured":"Miller, M., Kisiel, A., Cembrowska-Lech, D., et al.: IoT in water quality monitoring-Are we really here? Sensors 23(2), 960 (2023)","journal-title":"Sensors"},{"issue":"03","key":"3810_CR14","first-page":"598","volume":"45","author":"Z Rongxing","year":"2023","unstructured":"Rongxing, Z., Guo, Z., Zhifeng, X., et al.: Study on water level detection method of water gauge guided by small area. J. Yunnan Univ. (Natl. Sci. Edn.) 45(03), 598\u2013610 (2023)","journal-title":"J. Yunnan Univ. (Natl. Sci. Edn.)"},{"issue":"01","key":"3810_CR15","first-page":"59","volume":"37","author":"L Xinyu","year":"2023","unstructured":"Xinyu, L., Chuanmeng, S., Wei, Yu., et al.: Intelligent detection method of water level in harsh scene based on Transformer and residual channel attention. J. Electron. Meas. Instrum. 37(01), 59\u201369 (2023)","journal-title":"J. Electron. Meas. Instrum."},{"issue":"08","key":"3810_CR16","first-page":"1","volume":"05","author":"L Zhong","year":"2024","unstructured":"Zhong, L., Ruirui, M., Xiaolong, W., et al.: Watermark detection algorithm based on improved PIDNet. Comput. Eng. 05(08), 1\u201313 (2024)","journal-title":"Comput. Eng."},{"key":"3810_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109477","volume":"180","author":"JMGP Isidoro","year":"2021","unstructured":"Isidoro, J.M.G.P., Martins, R., Carvalho, R.F., et al.: A high-frequency low-cost technique for measuring small-scale water level fluctuations using computer vision. Measurement 180, 109477 (2021)","journal-title":"Measurement"},{"issue":"1","key":"3810_CR18","doi-asserted-by":"publisher","first-page":"152","DOI":"10.3390\/w16010152","volume":"16","author":"D Fronzi","year":"2024","unstructured":"Fronzi, D., Narang, G., Galdelli, A., et al.: Towards groundwater-level prediction using prophet forecasting method by exploiting a high-resolution hydrogeological monitoring system. Water 16(1), 152 (2024)","journal-title":"Water"},{"issue":"4","key":"3810_CR19","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1007\/s12583-022-1662-9","volume":"35","author":"L Guo","year":"2024","unstructured":"Guo, L., He, K., Liu, H., et al.: Physical prediction model of compound hydrodynamic unload-load response ratio and its application in reservoir colluvium landslide. J. Earth Sci. 35(4), 1304\u20131315 (2024)","journal-title":"J. Earth Sci."},{"key":"3810_CR20","doi-asserted-by":"crossref","unstructured":"Salam, A.: Internet of things for water sustainability. In: Internet of Things for Sustainable Community Development: Wireless Communications, Sensing, and Systems. Cham: Springer, pp. 113\u2013145 (2024)","DOI":"10.1007\/978-3-031-62162-8_4"},{"issue":"08","key":"3810_CR21","first-page":"1","volume":"05","author":"D Xiaorong","year":"2024","unstructured":"Xiaorong, D., Yanbing, G.: Analysis of water level detection algorithm combined with contextual attention mechanism. Beijing Water 05(08), 1\u20137 (2024)","journal-title":"Beijing Water"},{"key":"3810_CR22","unstructured":"Wang, D.: Research and application of reading algorithm for complex water level gauge. Beijing University of Posts and Telecommunications (2023)"},{"key":"3810_CR23","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., Da Silva, E.A.B.: A survey on performance metrics for object-detection algorithms. In: 2020 International Conference on Systems, Signals and Image Processing). IEEE, pp. 237\u2013242 (2020)","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"issue":"9","key":"3810_CR24","doi-asserted-by":"publisher","first-page":"5935","DOI":"10.1007\/s00371-023-03145-w","volume":"40","author":"J Tang","year":"2024","unstructured":"Tang, J., Wang, X., Yang, X., et al.: TSNet: task-specific network for joint diabetic retinopathy grading and lesion segmentation of ultra-wide optical coherence tomography angiography images. Vis. Comput. 40(9), 5935\u20135946 (2024)","journal-title":"Vis. Comput."},{"issue":"8","key":"3810_CR25","doi-asserted-by":"publisher","first-page":"AIp2400289","DOI":"10.1056\/AIp2400289","volume":"1","author":"W Ma","year":"2024","unstructured":"Ma, W., Sheng, B., Liu, Y., et al.: Evolution of future medical AI models-from task-specific, disease-centric to universal health. NEJM AI 1(8), AIp2400289 (2024)","journal-title":"NEJM AI"},{"issue":"4","key":"3810_CR26","doi-asserted-by":"publisher","DOI":"10.1002\/cav.2287","volume":"35","author":"J Ye","year":"2024","unstructured":"Ye, J., Meng, X., Guo, D., et al.: Neural foveated super-resolution for real-time VR rendering. Comput. Anim. Virtual Worlds 35(4), e2287 (2024)","journal-title":"Comput. Anim. Virtual Worlds"},{"key":"3810_CR27","doi-asserted-by":"crossref","unstructured":"Qian, B., Chen, H., Wang, X., et al.: DRAC 2022: a public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images. Patterns 5(3) (2024)","DOI":"10.1016\/j.patter.2024.100929"},{"issue":"3","key":"3810_CR28","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1038\/s42255-024-00988-y","volume":"6","author":"H Li","year":"2024","unstructured":"Li, H., Zhang, L., Li, J., et al.: Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota. Nat. Metab. 6(3), 578\u2013597 (2024)","journal-title":"Nat. Metab."},{"issue":"6","key":"3810_CR29","doi-asserted-by":"publisher","first-page":"e279","DOI":"10.1016\/j.jhep.2023.11.017","volume":"80","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Liu, H., Sheng, B., et al.: Preliminary fatty liver disease grading using general-purpose online large language models: ChatGPT-4 or Bard? J. Hepatol. 80(6), e279\u2013e281 (2024)","journal-title":"J. Hepatol."},{"key":"3810_CR30","unstructured":"Qian, B., Wang, X., Guan, Z., et al.: HRDC challenge: a public benchmark for hypertension and hypertensive retinopathy classification from fundus images. Vis. Comput. 1\u201317 (2024)"},{"issue":"4","key":"3810_CR31","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.dld.2024.01.191","volume":"56","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Wu, L., Wang, Y., et al.: Unexpectedly low accuracy of GPT-4 in identifying common liver diseases from CT scan images. Dig. Liver Dis. 56(4), 718\u2013720 (2024)","journal-title":"Dig. Liver Dis."},{"issue":"9","key":"3810_CR32","doi-asserted-by":"publisher","first-page":"6627","DOI":"10.1007\/s00371-023-03189-y","volume":"40","author":"G Huang","year":"2024","unstructured":"Huang, G., Wen, Y., Qian, B., et al.: Attention-based multi-scale feature fusion network for myopia grading using optical coherence tomography images. Vis. Comput. 40(9), 6627\u20136638 (2024)","journal-title":"Vis. Comput."},{"key":"3810_CR33","doi-asserted-by":"crossref","unstructured":"Li, J., Guan, Z., Wang, J., et al.: Integrated image-based deep learning and language models for primary diabetes care. Nat. Med. 1\u201311 (2024)","DOI":"10.3389\/fcvm.2024.1384977"},{"key":"3810_CR34","unstructured":"Qian, B., Chen, H., Xu, Y., et al.: Deep contour attention learning for scleral deformation from OCT images. Vis. Comput. 1\u201316 (2024)"},{"key":"3810_CR35","doi-asserted-by":"crossref","unstructured":"Kong, L., Jiao, L.L., Jia, F., et al.: An improved image super-resolution reconstruction method based on LapSRN. In: 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. IEEE, pp. 1\u20135 (2021)","DOI":"10.1109\/CISP-BMEI53629.2021.9624332"},{"issue":"2","key":"3810_CR36","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/s11390-024-3419-7","volume":"39","author":"Y Jung","year":"2024","unstructured":"Jung, Y., Kong, J., Sheng, B., et al.: A transfer function design for medical volume data using a knowledge database based on deep image and primitive intensity profile features retrieval. J. Comput. Sci. Technol. 39(2), 320\u2013335 (2024)","journal-title":"J. Comput. Sci. Technol."},{"key":"3810_CR37","doi-asserted-by":"crossref","unstructured":"Li, X., Meng, M., Huang, Z., et al.: 3dpx: Progressive 2d-to-3d oral image reconstruction with hybrid mlp-cnn networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, pp. 25\u201334 (2024)","DOI":"10.1007\/978-3-031-72104-5_3"},{"issue":"3","key":"3810_CR38","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1038\/s41433-023-02724-4","volume":"38","author":"Y Huang","year":"2024","unstructured":"Huang, Y., Cheung, C.Y., Li, D., et al.: AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye 38(3), 464\u2013472 (2024)","journal-title":"Eye"},{"key":"3810_CR39","doi-asserted-by":"crossref","unstructured":"Qin, Y., Zhao, N., Yang, J., et al.: UrbanEvolver: function-aware urban layout regeneration. Int. J. Comput. Vis. 1\u201320 (2024)","DOI":"10.1007\/s11263-024-02030-w"},{"issue":"8","key":"3810_CR40","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/S2213-8587(24)00154-2","volume":"12","author":"B Sheng","year":"2024","unstructured":"Sheng, B., Pushpanathan, K., Guan, Z., et al.: Artificial intelligence for diabetes care: current and future prospects. Lancet Diabetes Endocrinol. 12(8), 569\u2013595 (2024)","journal-title":"Lancet Diabetes Endocrinol."},{"key":"3810_CR41","doi-asserted-by":"crossref","unstructured":"Chen, Y., Jiang, R., Zheng, Y., et al.: Dual branch multi-level semantic learning for few-shot segmentation. IEEE Trans. Image Process. 33, 1432\u20131447 (2024)","DOI":"10.1109\/TIP.2024.3364056"},{"key":"3810_CR42","doi-asserted-by":"crossref","unstructured":"Ali, S.G., Wang, X., Li, P., et al.: Egdnet: an efficient glomerular detection network for multiple anomalous pathological feature in glomerulonephritis. Vis. Comput. 1\u201318 (2024)","DOI":"10.1007\/s00371-024-03570-5"},{"issue":"4","key":"3810_CR43","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1002\/ima.22337","volume":"29","author":"Z Li","year":"2019","unstructured":"Li, Z., Wang, S.H., Fan, R.R., et al.: Teeth category classification via seven-layer deep convolutional neural network with max pooling and global average pooling. Int. J. Imaging Syst. Technol. 29(4), 577\u2013583 (2019)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"3810_CR44","unstructured":"Li, Y., Yuan, Y.: Convergence analysis of two-layer neural networks with relu activation. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"issue":"7","key":"3810_CR45","first-page":"579","volume":"8","author":"MC Popescu","year":"2009","unstructured":"Popescu, M.C., Balas, V.E., Perescu-Popescu, L., et al.: Multilayer perceptron and neural networks. WSEAS Trans. Circuits Syst. 8(7), 579\u2013588 (2009)","journal-title":"WSEAS Trans. Circuits Syst."},{"key":"3810_CR46","unstructured":"Ali, S.G., Zhang, C., Guan, Z., et al.: AI-enhanced digital technologies for myopia management: advancements, challenges, and future prospects. Vis. Comput. 1\u201317 (2024)"},{"key":"3810_CR47","doi-asserted-by":"crossref","unstructured":"Li, L., Chen, Z., Dai, L., et al.: MA-MFCNet: mixed attention-based multi-scale feature calibration network for image dehazing. IEEE Trans. Emerg. Top. Comput. Intell. (2024)","DOI":"10.1109\/TETCI.2024.3382233"},{"issue":"1","key":"3810_CR48","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s41746-024-01204-7","volume":"7","author":"Z Qi","year":"2024","unstructured":"Qi, Z., Li, T., Chen, J., et al.: A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children. npj Digital Med. 7(1), 206 (2024)","journal-title":"npj Digital Med."},{"key":"3810_CR49","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 568\u2013578 (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"3810_CR50","first-page":"9355","volume":"34","author":"X Chu","year":"2021","unstructured":"Chu, X., Tian, Z., Wang, Y., et al.: Twins: Revisiting the design of spatial attention in vision transformers. Adv. Neural. Inf. Process. Syst. 34, 9355\u20139366 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3810_CR51","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03810-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-03810-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-03810-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T06:12:42Z","timestamp":1757139162000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-03810-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,20]]},"references-count":51,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["3810"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-03810-2","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,20]]},"assertion":[{"value":"9 January 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}