{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:18:57Z","timestamp":1774397937715,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T00:00:00Z","timestamp":1533081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1414108"],"award-info":[{"award-number":["1414108"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.<\/jats:p>","DOI":"10.3390\/s18082484","type":"journal-article","created":{"date-parts":[[2018,8,1]],"date-time":"2018-08-01T11:22:34Z","timestamp":1533122554000},"page":"2484","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images"],"prefix":"10.3390","volume":"18","author":[{"given":"Weixing","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Geography, University of Connecticut, Storrs, CT 06269, USA"},{"name":"Center for Environmental Science and Engineering, University of Connecticut, Storrs, CT 06269, USA"},{"name":"Connecticut State Data Center, University of Connecticut, Storrs, CT 06269, USA"},{"name":"Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA"},{"name":"Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chandi","family":"Witharana","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA"},{"name":"Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4558-3292","authenticated-orcid":false,"given":"Weidong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9165-5584","authenticated-orcid":false,"given":"Chuanrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Connecticut, Storrs, CT 06269, USA"},{"name":"Center for Environmental Science and Engineering, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojiang","family":"Li","sequence":"additional","affiliation":[{"name":"MIT Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Parent","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA"},{"name":"Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,1]]},"reference":[{"key":"ref_1","unstructured":"Nagura, S., Masumoto, T., Endo, K., Wakasa, F., Watanabe, S., and Ikeda, K. (1989). Development of mapping system for distribution facility management. Electricity Distribution, Proceedings of 10th International Conference on Electricity Distribution, CIRED 1989, Brighton, UK, 8\u201312 May 1989, IET."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ijepes.2017.12.016","article-title":"Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning","volume":"99","author":"Nguyen","year":"2018","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_3","unstructured":"(2018, February 01). CITYLAB. Available online: https:\/\/www.citylab.com\/environment\/2017\/10\/how-open-source-mapping-helps-hurricane-recovery\/542565\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cetin, B., Bikdash, M., and McInerney, M. (2009, January 5\u20138). Automated electric utility pole detection from aerial images. Proceedings of the IEEE Southeastcon 2009, Atlanta, GA, USA.","DOI":"10.1109\/SECON.2009.5174047"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1016\/S0167-8655(99)00126-9","article-title":"combined analysis to extract objects in remote sensing images","volume":"20","author":"Bernstein","year":"1999","journal-title":"Pattern Recognit. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/S0262-8856(03)00097-0","article-title":"Corner detection and matching for visual tracking during power line inspection","volume":"21","author":"Golightly","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/j.conengprac.2004.09.008","article-title":"A laboratory test-bed for an automated power line inspection system","volume":"13","author":"Jones","year":"2005","journal-title":"Control Eng. Pract."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s11220-017-0172-9","article-title":"Monitoring of Overhead Transmission Lines: A Review from the Perspective of Contactless Technologies","volume":"18","author":"Khawaja","year":"2017","journal-title":"Sens. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, W.H., Tajbakhsh, A., Rathbone, C., and Vashishtha, Y. (2010, January 5\u20137). Image processing to automate condition assessment of overhead line components. Proceedings of the 2010 1st International Conference on Applied Robotics for the Power Industry, Montreal, QC, Canada.","DOI":"10.1109\/CARPI.2010.5624447"},{"key":"ref_10","unstructured":"Tong, W.G., Li, B.S., Yuan, J.S., and Zhao, S.T. (2009, January 12\u201315). Transmission line extraction and recognition from natural complex background. Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, Baoding, China."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1049\/pe:20010103","article-title":"Aerial video inspection of overhead power lines","volume":"15","author":"Whitworth","year":"2001","journal-title":"Power Eng. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/LGRS.2007.895714","article-title":"Automatic extraction of power lines from aerial images","volume":"4","author":"Yan","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1109\/7.272288","article-title":"Power lines: Radar measurements and detection algorithm for polarimetric SAR images","volume":"30","author":"Sarabandi","year":"1994","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1802","DOI":"10.1109\/8.901268","article-title":"Extraction of power line maps from millimeter-wave polarimetric SAR images","volume":"48","author":"Sarabandi","year":"2000","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.14358\/PERS.78.11.1227","article-title":"A piecewise catenary curve model growing for 3D power line reconstruction","volume":"78","author":"Jwa","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s00138-010-0279-7","article-title":"Urban scene understanding from aerial and ground LIDAR data","volume":"22","author":"Kim","year":"2011","journal-title":"Mach. Vis. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"821","DOI":"10.14358\/PERS.79.9.821","article-title":"Point-based classification of power line corridor scene using random forests","volume":"79","author":"Kim","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/LGRS.2005.863390","article-title":"Extracting transmission lines from airborne LIDAR data","volume":"3","author":"McLaughlin","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, Q., Liu, L., Zheng, D., Li, C., and Li, K. (2017). Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas. Remote Sens., 9.","DOI":"10.3390\/rs9080771"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.isprsjprs.2006.03.004","article-title":"Measuring the distance of vegetation from powerlines using stereo vision","volume":"60","author":"Sun","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2016.04.011","article-title":"Remote sensing methods for power line corridor surveys","volume":"119","author":"Matikainen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","unstructured":"Moore, A.J., Schubert, M., and Rymer, N. (2017, February 12). Autonomous Inspection of Electrical Transmission Structures with Airborne UV Sensors-NASA Report on Dominion Virginia Power Flights of November 2016, Available online: https:\/\/ntrs.nasa.gov\/archive\/nasa\/casi.ntrs.nasa.gov\/20170004692.pdf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Oh, J., and Lee, C. (2017). 3D power line extraction from multiple aerial images. Sensors, 17.","DOI":"10.3390\/s17102244"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yuan, X., Li, W., and Chen, S. (2017). Automatic Power Line Inspection Using UAV Images. Remote Sens., 9.","DOI":"10.3390\/rs9080824"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11267","DOI":"10.3390\/rs61111267","article-title":"Fully-automated power line extraction from airborne laser scanning point clouds in forest areas","volume":"6","author":"Zhu","year":"2014","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.isprsjprs.2013.10.008","article-title":"An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds","volume":"87","author":"Cabo","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"641","DOI":"10.3390\/rs2030641","article-title":"Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data","volume":"2","author":"Jaakkola","year":"2010","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3302","DOI":"10.3390\/rs6043302","article-title":"Extraction of urban power lines from vehicle-borne LiDAR data","volume":"6","author":"Cheng","year":"2014","journal-title":"Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/01431161.2015.1125549","article-title":"Extraction of power-transmission lines from vehicle-borne lidar data","volume":"37","author":"Guan","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sharma, H., Adithya, V., Dutta, T., and Balamuralidhar, P. (2015, January 23\u201325). Image Analysis-Based Automatic Utility Pole Detection for Remote Surveillance. Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Adelaide, Australia.","DOI":"10.1109\/DICTA.2015.7371267"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MC.2010.170","article-title":"Google street view: Capturing the world at street level","volume":"43","author":"Anguelov","year":"2010","journal-title":"Computer"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.ufug.2015.06.006","article-title":"Assessing street-level urban greenery using Google Street View and a modified green view index","volume":"14","author":"Li","year":"2015","journal-title":"Urban For. Urban Green."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1016\/j.ufug.2015.07.006","article-title":"Who lives in greener neighborhoods? The distribution of street greenery and its association with residents\u2019 socioeconomic conditions in Hartford, Connecticut, USA","volume":"14","author":"Li","year":"2015","journal-title":"Urban For. Urban Green."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1080\/15481603.2017.1338389","article-title":"Building block level urban land-use information retrieval based on Google Street View images","volume":"54","author":"Li","year":"2017","journal-title":"GIsci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.compenvurbsys.2017.03.001","article-title":"Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View","volume":"64","author":"Zhang","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.landurbplan.2017.08.011","article-title":"Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View","volume":"169","author":"Li","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cheng, W., and Song, Z. (2008, January 27\u201330). Power pole detection based on graph cut. Proceedings of the 2008 Congress on Image and Signal Processing, Sanya, China.","DOI":"10.1109\/CISP.2008.440"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1049\/iet-ipr.2009.0293","article-title":"Digital image processing approach using combined wavelet hidden Markov model for well-being analysis of insulators","volume":"5","author":"Murthy","year":"2011","journal-title":"IET Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Huang, F., and Sugimoto, A. (2013, January 28\u201329). An Approach for Utility Pole Recognition in Real Conditions. Proceedings of the Image and Video Technology\u2014PSIVT 2013 Workshops, Guanajuato, Mexico.","DOI":"10.1007\/978-3-642-53926-8"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neucom.2013.09.023","article-title":"Power line detection from optical images","volume":"129","author":"Song","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3379","DOI":"10.1109\/TGRS.2010.2046905","article-title":"Evaluation of aerial remote sensing techniques for vegetation management in power-line corridors","volume":"48","author":"Mills","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","unstructured":"Buduma, N., and Locascio, N. (2017). Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, O\u2019Reilly Media, Inc.. [1st ed.]."},{"key":"ref_43","unstructured":"G\u00e9ron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media, Inc.. [1st ed.]."},{"key":"ref_44","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_47","unstructured":"Patterson, J., and Gibson, A. (2017). Deep Learning: A Practitioner\u2019s Approach, O\u2019Reilly Media, Inc.. [1st ed.]."},{"key":"ref_48","unstructured":"Puneet, S., and Filippo Maria, B. (2017, January 12\u201314). DEBC Detection with Deep Learning. Proceedings of the 20th Scandinavian Conference on Image Analysis, Troms\u00f8, Norway."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (arXiv, 2017). Focal loss for dense object detection, arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_50","unstructured":"(2018, February 15). Google. Available online: https:\/\/developers.google.com\/maps\/documentation\/streetview\/intro."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_52","unstructured":"Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., and Garnett, R. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 28, Neural Information Processing Systems Foundation, Inc."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_54","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Timofte, R., and Van Gool, L. (2011, January 6\u201313). Multi-view manhole detection, recognition, and 3D localisation. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130242"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2012.11.009","article-title":"Detection and 3D reconstruction of traffic signs from multiple view color images","volume":"77","author":"Soheilian","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hebbalaguppe, R., Garg, G., Hassan, E., Ghosh, H., and Verma, A. (2017, January 24\u201331). Telecom Inventory management via object recognition and localisation on Google Street View Images. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (2017), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.86"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Krylov, V.A., Kenny, E., and Dahyot, R. (2018). Automatic Discovery and Geotagging of Objects from Street View Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10050661"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/7.256302","article-title":"Performance analysis of bearing-only target location algorithms","volume":"28","author":"Gavish","year":"1992","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.sigpro.2009.05.026","article-title":"Localization of Multiple Emitters Based on the Sequential PHD Filter","volume":"90","author":"Zhang","year":"2010","journal-title":"Signal Process."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Reed, J.D., da Silva, C.R., and Buehrer, R.M. (2008, January 16\u201319). Multiple-source localization using line-of-bearing measurements: Approaches to the data association problem. Proceedings of the MILCOM 2008\u20142008 IEEE Military Communications Conference, San Diego, CA, USA.","DOI":"10.1109\/MILCOM.2008.4753444"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Grabbe, M.T., Hamschin, B.M., and Douglas, A.P. (2013, January 2\u20139). A measurement correlation algorithm for line-of-bearing geo-location. Proceedings of the 2013 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2013.6496828"},{"key":"ref_63","first-page":"79","article-title":"Research into the algorithm of false points elimination in three-station cross location","volume":"32","author":"Tan","year":"2009","journal-title":"Shipboard Electron. Countermeas."},{"key":"ref_64","unstructured":"Reed, J. (2009). Approaches to Multiple-Source Localization and Signal Classification. [Ph.D. Thesis, Virginia Polytechnic Institute and State University]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2484\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:15:41Z","timestamp":1760195741000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/8\/2484"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,1]]},"references-count":64,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["s18082484"],"URL":"https:\/\/doi.org\/10.3390\/s18082484","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,1]]}}}