{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T07:50:14Z","timestamp":1774684214104,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T00:00:00Z","timestamp":1684972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CONCYTEC\/PROCIENCIA","award":["PE501078853-2022"],"award-info":[{"award-number":["PE501078853-2022"]}]},{"name":"the \u00c1ngel San Bartolom\u00e9 scholarship","award":["PE501078853-2022"],"award-info":[{"award-number":["PE501078853-2022"]}]},{"name":"the Vicerrectorado de Investigaci\u00f3n (VRI) at Pontificia Universidad Cat\u00f3lica del Per\u00fa (PUCP)","award":["PE501078853-2022"],"award-info":[{"award-number":["PE501078853-2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Damage identification soon after a large-magnitude earthquake is a major problem for early disaster response activities. The faster the damaged areas are identified, the higher the survival chances of inhabitants. Current methods for damage identification are based on the application of artificial intelligence techniques using remote sensing data. Such methods require a large amount of high-quality labeled data for calibration and\/or fine-tuning processes, which are expensive in the aftermath of large-scale disasters. In this paper, we propose a novel semi-supervised classification approach for identifying urban changes induced by an earthquake between images recorded at different times. We integrate information from a small set of labeled data with information from ground motion and fragility functions computed on large unlabeled data. A relevant consideration is that ground motion and fragility functions can be computed in real time. The urban changes induced by the 2023 Turkey earthquake sequence are reported as an evaluation of the proposed method. The method was applied to the interferometric coherence computed from C-band synthetic aperture radar images from Sentinel-1. We use only 39 samples labeled as changed and 9000 unlabeled samples. The results show that our method is able to identify changes between images associated with the effects of an earthquake with an accuracy of about 81%. We conclude that the proposed method can rapidly identify affected areas in the aftermath of a large-magnitude earthquake.<\/jats:p>","DOI":"10.3390\/rs15112754","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T02:00:19Z","timestamp":1685066419000},"page":"2754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9747-6546","authenticated-orcid":false,"given":"Aymar","family":"Portillo","sequence":"first","affiliation":[{"name":"GERDIS Research Group, Department of Engineering, Pontificia Universidad Cat\u00f3lica del Per\u00fa, Av. Universitaria 1801, Lima 15088, Peru"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1764-3160","authenticated-orcid":false,"given":"Luis","family":"Moya","sequence":"additional","affiliation":[{"name":"GERDIS Research Group, Department of Engineering, Pontificia Universidad Cat\u00f3lica del Per\u00fa, Av. Universitaria 1801, Lima 15088, Peru"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,25]]},"reference":[{"key":"ref_1","unstructured":"Ohta, Y., Murakami, H., Watoh, Y., and Koyama, M. (2004, January 1\u20136). A model for evaluating life span characteristics of entrapped occupants by an earthquake. Proceedings of the 13th World Conference on Earthquake Engineering, Vancouver, BC, Canada."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111968","DOI":"10.1016\/j.rse.2020.111968","article-title":"Landsat 9: Empowering open science and applications through continuity","volume":"248","author":"Masek","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jones, C.E., P, M., and Rao, S. (2021, January 11\u201316). The NISAR Mission\u2019s Capabilities for Natural Hazards Monitoring. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553295"},{"key":"ref_6","unstructured":"MAXAR (2023, February 17). Open Data Program. Available online: https:\/\/www.maxar.com\/open-data\/."},{"key":"ref_7","unstructured":"Planet (2023, March 01). Disaster DATA. Available online: https:\/\/www.planet.com\/disasterdata\/."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Koshimura, S., Moya, L., Mas, E., and Bai, Y. (2020). Tsunami damage detection with remote sensing: A review. Geosciences, 10.","DOI":"10.3390\/geosciences10050177"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1193\/1.2101807","article-title":"Visual damage interpretation of buildings in Bam city using QuickBird images following the 2003 Bam, Iran, earthquake","volume":"21","author":"Yamazaki","year":"2005","journal-title":"Earthq. Spectra"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1193\/1.2101027","article-title":"Building damage mapping of the 2003 Bam, Iran, earthquake using Envisat\/ASAR intensity imagery","volume":"21","author":"Matsuoka","year":"2005","journal-title":"Earthq. Spectra"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1193\/1.4000120","article-title":"Extraction of Tsunami-Flooded Areas and Damaged Buildings in the 2011 Tohoku-Oki Earthquake from TerraSAR-X Intensity Images","volume":"29","author":"Liu","year":"2013","journal-title":"Earthq. Spectra"},{"key":"ref_12","unstructured":"Yamazaki, F., and Liu, W. (2016, January 22\u201324). Remote sensing technologies for post-earthquake damage assessment: A case study on the 2016 Kumamoto earthquake. Proceedings of the 6th Asia Conference on Earthquake Engg, Cebu City, Philippines."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"65","DOI":"10.5194\/nhess-18-65-2018","article-title":"Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan","volume":"18","author":"Moya","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2019.01.008","article-title":"3D gray level co-occurrence matrix and its application to identifying collapsed buildings","volume":"149","author":"Moya","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111743","DOI":"10.1016\/j.rse.2020.111743","article-title":"Detecting urban changes using phase correlation and L1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake-tsunami","volume":"242","author":"Moya","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"636197","DOI":"10.3389\/fbuil.2021.636197","article-title":"StEER: A community-centered approach to assessing the performance of the built environment after natural hazard events","volume":"7","author":"Roueche","year":"2021","journal-title":"Front. Built Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Okada, G., Moya, L., Mas, E., and Koshimura, S. (2021). The potential role of news media to construct a machine learning based damage mapping framework. Remote Sens., 13.","DOI":"10.3390\/rs13071401"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wieland, M., Liu, W., and Yamazaki, F. (2016). Learning change from synthetic aperture radar images: Performance evaluation of a support vector machine to detect earthquake and tsunami-induced changes. Remote Sens., 8.","DOI":"10.3390\/rs8100792"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1193\/121516eqs232m","article-title":"Building damage assessment in the 2015 Gorkha, Nepal, earthquake using only post-event dual polarization synthetic aperture radar imagery","volume":"33","author":"Bai","year":"2017","journal-title":"Earthq. Spectra"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Adriano, B., Xia, J., Baier, G., Yokoya, N., and Koshimura, S. (2019). Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 sulawesi earthquake and tsunami in Palu, Indonesia. Remote Sens., 11.","DOI":"10.3390\/rs11070886"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2017.2772349","article-title":"A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks","volume":"15","author":"Bai","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1109\/TGRS.2020.3000296","article-title":"Building change detection in VHR SAR images via unsupervised deep transcoding","volume":"59","author":"Saha","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2021.02.016","article-title":"Learning from multimodal and multitemporal earth observation data for building damage mapping","volume":"175","author":"Adriano","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","first-page":"4020405","article-title":"Improving landslide detection on SAR data through deep learning","volume":"19","author":"Nava","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5753","DOI":"10.1109\/JSTARS.2022.3189875","article-title":"A Deep Learning Model for Road Damage Detection After an Earthquake Based on Synthetic Aperture Radar (SAR) and Field Datasets","volume":"15","author":"Karimzadeh","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","unstructured":"Gupta, R., Goodman, B., Patel, N., Hosfelt, R., Sajeev, S., Heim, E., Doshi, J., Lucas, K., Choset, H., and Gaston, M. (2019, January 16\u201317). Creating xBD: A dataset for assessing building damage from satellite imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102726","DOI":"10.1016\/j.ipm.2021.102726","article-title":"Semisupervised SAR image change detection based on a siamese variational autoencoder","volume":"59","author":"Zhao","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_28","first-page":"5203016","article-title":"SAR-TSCC: A Novel Approach for Long Time Series SAR Image Change Detection and Pattern Analysis","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1002\/eqe.4290190802","article-title":"A strong motion database for the Chiba seismometer array and its engineering analysis","volume":"19","author":"Katayama","year":"1990","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_30","first-page":"65","article-title":"Strong-motion seismograph network operated by NIED: K-NET and KiK-net","volume":"4","author":"Aoi","year":"2004","journal-title":"J. Jpn. Assoc. Earthq. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1038\/nature10227","article-title":"Coseismic and postseismic slip of the 2011 magnitude-9 Tohoku-Oki earthquake","volume":"475","author":"Ozawa","year":"2011","journal-title":"Nature"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1640002","DOI":"10.1142\/S1793431116400029","article-title":"Comparison of coseismic displacement obtained from GEONET and seismic networks","volume":"10","author":"Moya","year":"2016","journal-title":"J. Earthq. Tsunami"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1002\/eqe.4290210201","article-title":"Soil amplification based on seismometer array and microtremor observations in Chiba, Japan","volume":"21","author":"Lu","year":"1992","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"904","DOI":"10.20965\/jdr.2013.p0904","article-title":"Nationwide 7.5-arc-second Japan engineering geomorphologic classification map and Vs30 zoning","volume":"8","author":"Wakamatsu","year":"2013","journal-title":"J. Disaster Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., Feizizadeh, B., and Matsuoka, M. (2019). DEM-based Vs30 map and terrain surface classification in nationwide scale\u2014A case study in Iran. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120537"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1142\/9781848160194_0007","article-title":"Vulnerability functions for japanese","volume":"2","author":"Yamazaki","year":"2000","journal-title":"Implic. Recent Earthquakes Seism. Risk"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1193\/1.2720892","article-title":"Creating fragility functions for performance-based earthquake engineering","volume":"23","author":"Porter","year":"2007","journal-title":"Earthq. Spectra"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1142\/S0578563409002004","article-title":"Developing fragility functions for tsunami damage estimation using numerical model and post-tsunami data from Banda Aceh, Indonesia","volume":"51","author":"Koshimura","year":"2009","journal-title":"Coast. Eng. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1007\/s00024-020-02501-4","article-title":"Characteristics of tsunami fragility functions developed using different sources of damage data from the 2018 Sulawesi earthquake and tsunami","volume":"177","author":"Mas","year":"2020","journal-title":"Pure Appl. Geophys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"464","DOI":"10.20965\/jdr.2022.p0464","article-title":"Development of Fragility Curves for Japanese Buildings Based on Integrated Damage Data from the 2016 Kumamoto Earthquake","volume":"17","author":"Torisawa","year":"2022","journal-title":"J. Disaster Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1016\/j.ijdrr.2018.03.034","article-title":"An integrated method to extract collapsed buildings from satellite imagery, hazard distribution and fragility curves","volume":"31","author":"Moya","year":"2018","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Moya, L., Marval Perez, L.R., Mas, E., Adriano, B., Koshimura, S., and Yamazaki, F. (2018). Novel unsupervised classification of collapsed buildings using satellite imagery, hazard scenarios and fragility functions. Remote Sens., 10.","DOI":"10.3390\/rs10020296"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8288","DOI":"10.1109\/TGRS.2020.3046004","article-title":"Disaster intensity-based selection of training samples for remote sensing building damage classification","volume":"59","author":"Moya","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1785\/BSSA0850051343","article-title":"Attenuation of earthquake ground motion in Japan including deep focus events","volume":"85","author":"Molas","year":"1995","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1002\/eqe.323","article-title":"Near-fault spatial variation in strong ground motion due to rupture directivity and hanging wall effects from the Chi-Chi, Taiwan earthquake","volume":"32","author":"Shabestari","year":"2003","journal-title":"Earthq. Eng. Struct. Dyn."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.tecto.2004.03.031","article-title":"Estimation of the spatial distribution of ground motion parameters for two recent earthquakes in Japan","volume":"390","author":"Shabestari","year":"2004","journal-title":"Tectonophysics"},{"key":"ref_47","unstructured":"\u00d6nder, \u00c7etin., Bray, J., Frost, D., Hortacsu, A., Miranda, E., Moss, R., and Stewart, J. (2023). February 6, 2023 T\u00fcrkiye Earthquakes: Report on Geoscience and Engineering Impacts, Technical Report GEER Association Report 082; The Earthquake Engineering Research Institute and Geotechnical Extreme Event Reconnaissance Association and The Earthquake Engineering Association and Earthquake Engineering Foundation of T\u00fcrkiye."},{"key":"ref_48","unstructured":"USGS (2023, February 06). M7.8 and M7.5 Kahramanmara\u015f Earthquake Sequence Struck near Nurda\u011f\u0131, Turkey (T\u00fcrkiye), Available online: https:\/\/www.usgs.gov\/news\/featured-story\/m78-and-m75-kahramanmaras-earthquake-sequence-near-nurdagi-turkey-turkiye."},{"key":"ref_49","unstructured":"Disaster and Emergency Management Authority (2023). Press Bulletin-32 about the Earthquake in Kahramanmaras, Technical Report; Ministry of Interior."},{"key":"ref_50","unstructured":"Earthquake Engineering Research Center (2023). Preliminary Reconnaissance Report on February 6, 2023, Pazarcik Mw=7.7 and Elbistan Mw=7.6, Kahramanmaras-Turkiye Earthquakes, Technical Report METU\/EERC 2023-01; Middle East Technical University."},{"key":"ref_51","unstructured":"Thompson, A. (2023, February 20). Why the Earthquake in Turkey Was So Damaging and Deadly. Scientific American. Available online: https:\/\/www.scientificamerican.com\/article\/why-the-earthquake-in-turkey-was-so-damaging-and-deadly\/."},{"key":"ref_52","unstructured":"BBC (2023, February 20). Turkey Earthquake: Why Did So Many Buildings Collapse?. Available online: https:\/\/www.bbc.com\/news\/64568826."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., Matsuoka, M., Miyajima, M., Adriano, B., Fallahi, A., and Karashi, J. (2018). Sequential SAR Coherence Method for the Monitoring of Buildings in Sarpole-Zahab, Iran. Remote Sens., 10.","DOI":"10.3390\/rs10081255"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Moya, L., Endo, Y., Okada, G., Koshimura, S., and Mas, E. (2019). Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods. Remote Sens., 11.","DOI":"10.3390\/rs11192320"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Moya, L., Mas, E., and Koshimura, S. (2020). Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon. Remote Sens., 12.","DOI":"10.3390\/rs12142244"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Karimzadeh, S., and Matsuoka, M. (2021). A Preliminary Damage Assessment Using Dual Path Synthetic Aperture Radar Analysis for the M 6.4 Petrinja Earthquake (2020), Croatia. Remote Sens., 13.","DOI":"10.3390\/rs13122267"},{"key":"ref_57","unstructured":"Earth Observation Center (2023, February 17). World Settlement Footprint 2019. Available online: https:\/\/download.geoservice.dlr.de\/WSF2019\/."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1553\/giscience2021_01_s33","article-title":"Understanding current trends in global urbanisation-the world settlement footprint suite","volume":"9","author":"Marconcini","year":"2021","journal-title":"GI_Forum"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1016\/j.engstruct.2007.07.016","article-title":"Fragility-based assessment of typical mid-rise and low-rise RC buildings in Turkey","volume":"30","author":"Erberik","year":"2008","journal-title":"Eng. Struct."},{"key":"ref_60","unstructured":"USGS (2023, February 20). M 7.8\u201426 Km ENE of Nurda\u011f\u0131, Turkey, Available online: https:\/\/earthquake.usgs.gov\/earthquakes\/eventpage\/us6000jllz\/executive."},{"key":"ref_61","unstructured":"The Independent (2023, February 07). Watch Again: View from Hatay after Third Quake Hits Turkey. Available online: https:\/\/youtu.be\/QkSjKtSj7Ls."},{"key":"ref_62","unstructured":"StEER (2023, February 22). StEER Network Activation. Available online: https:\/\/www.steer.network\/kaharamanmaras."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0925-2312(93)90006-O","article-title":"Backpropagation and stochastic gradient descent method","volume":"5","author":"Amari","year":"1993","journal-title":"Neurocomputing"},{"key":"ref_64","unstructured":"Robinson, C., Fobi Nsutezo, S., Pound, E., Ortiz, A., Rosa, M., White, K., Dodhia, R., Zolli, A., Birge, C., and Ferres, L. (2023). Turkey Earthquake Report, Technical Report MSR-TR-2023-7; Microsoft."},{"key":"ref_65","unstructured":"Planet (2023, March 01). Planet\u2019s Response to Earthquakes in Turkey and Syria. Available online: https:\/\/www.planet.com\/pulse\/planets-response-to-earthquakes-in-turkey-and-syria\/."},{"key":"ref_66","unstructured":"MAXAR (2023, February 08). Turkey and Syria Earthquake 2023. Available online: https:\/\/www.maxar.com\/open-data\/turkey-earthquake-2023."},{"key":"ref_67","unstructured":"IRIDeS (2023, May 11). 2023 T\u00fcrkiye-Syria Earthquake. Available online: https:\/\/irides.maps.arcgis.com\/apps\/dashboards\/ffb8ae5f27964ad8843c5e99556e0ff5."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2754\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:42:06Z","timestamp":1760125326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,25]]},"references-count":67,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15112754"],"URL":"https:\/\/doi.org\/10.3390\/rs15112754","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,25]]}}}