{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T09:14:46Z","timestamp":1770714886077,"version":"3.49.0"},"reference-count":59,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T00:00:00Z","timestamp":1709337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0802400"],"award-info":[{"award-number":["2022YFF0802400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["81961128002"],"award-info":[{"award-number":["81961128002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2022YFF0802400"],"award-info":[{"award-number":["2022YFF0802400"]}]},{"name":"National Natural Science Foundation of China","award":["81961128002"],"award-info":[{"award-number":["81961128002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The escalating evolution of aquaculture has wielded a profound and far-reaching impact on regional sustainable development, ecological equilibrium, and food security. Currently, most aquaculture mapping efforts mainly focus on coastal aquaculture ponds rather than diverse inland aquaculture areas. Recognizing all types of aquaculture areas and accurately classifying different types of aquaculture areas remains a challenge. Here, on the basis of the Google Earth Engine (GEE) and the time-series Sentinel-1 and -2 data, we developed a novel hierarchical framework extraction method for mapping fine inland aquaculture areas (aquaculture ponds + rice-crawfish fields) by employing distinct phenological disparities within two temporal windows (T1 and T2) in Qianjiang, so-called \u201cHome of Chinese Crawfish\u201d. Simultaneously, we evaluated the classification performance of four distinct machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Gradient Boosting (GTB), as well as 11 feature combinations. Following an exhaustive comparative analysis, we selected the optimal machine learning classifier (i.e., the RF classifier) and the optimal feature combination (i.e., feature combination after an automated feature selection method) to classify the aquaculture areas with high accuracy. The results underscore the robustness of the proposed methodology, achieving an outstanding overall accuracy of 93.8%, with an F1 score of 0.94 for aquaculture. The result indicates that an area of 214.6 \u00b1 10.5 km2 of rice-crawfish fields, constituting approximately 83% of the entire aquaculture area in Qianjiang, followed by aquaculture ponds (44.3 \u00b1 10.7 km2, 17%). The proposed hierarchical framework, based on significant phenological characteristics of varied aquaculture types, provides a new approach to monitoring inland freshwater aquaculture in China and other regions of the world.<\/jats:p>","DOI":"10.3390\/rs16050893","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T10:11:57Z","timestamp":1709547117000},"page":"893","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Freshwater Aquaculture Mapping in \u201cHome of Chinese Crawfish\u201d by Using a Hierarchical Classification Framework and Sentinel-1\/2 Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5718-4605","authenticated-orcid":false,"given":"Chen","family":"Wang","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"},{"name":"School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China"}]},{"given":"Genhou","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0386-5646","authenticated-orcid":false,"given":"Geli","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8970-0123","authenticated-orcid":false,"given":"Yifeng","family":"Cui","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yingli","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7416-9234","authenticated-orcid":false,"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Kaifeng 475004, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103547","DOI":"10.1016\/j.marpol.2019.103547","article-title":"Misunderstandings, Myths and Mantras in Aquaculture: Its Contribution to World Food Supplies Has Been Systematically over Reported","volume":"106","author":"Edwards","year":"2019","journal-title":"Mar. Policy"},{"key":"ref_2","unstructured":"(2022). The State of World Fisheries and Aquaculture 2022, FAO."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"E2","DOI":"10.1038\/s41586-021-04331-3","article-title":"Aquaculture Will Continue to Depend More on Land than Sea","volume":"603","author":"Zhang","year":"2022","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"064040","DOI":"10.1088\/1748-9326\/acd8d2","article-title":"Exploring the Emergence and Changing Dynamics of a New Integrated Rice-Crawfish Farming System in China","volume":"18","author":"Wei","year":"2023","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"113347","DOI":"10.1016\/j.rse.2022.113347","article-title":"Interannual Changes of Coastal Aquaculture Ponds in China at 10-m Spatial Resolution during 2016\u20132021","volume":"284","author":"Wang","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"103100","article-title":"Global Mapping of the Landside Clustering of Aquaculture Ponds from Dense Time-Series 10 m Sentinel-2 Images on Google Earth Engine","volume":"115","author":"Wang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ottinger, M., Bachofer, F., Huth, J., and Kuenzer, C. (2022). Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series. Remote Sens., 14.","DOI":"10.3390\/rs14010153"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2023.03.019","article-title":"Learning Spectral-Spatial Representations from VHR Images for Fine-Scale Crop Type Mapping: A Case Study of Rice-Crayfish Field Extraction in South China","volume":"199","author":"Cai","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sun, Z., Luo, J., Yang, J., Yu, Q., Zhang, L., Xue, K., and Lu, L. (2020). Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12183086"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.ocecoaman.2015.10.015","article-title":"Aquaculture: Relevance, Distribution, Impacts and Spatial Assessments\u2014A Review","volume":"119","author":"Ottinger","year":"2016","journal-title":"Ocean Coast. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109341","DOI":"10.1016\/j.agrformet.2023.109341","article-title":"Ecological Restoration Exacerbates the Agriculture-Induced Water Crisis in North China Region","volume":"331","author":"Zhou","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1002\/lno.12362","article-title":"Spatial and Temporal Variability in Summertime Dissolved Carbon Dioxide and Methane in Temperate Ponds and Shallow Lakes","volume":"68","author":"Ray","year":"2023","journal-title":"Limnol. Oceanogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1038\/s41561-021-00715-2","article-title":"Half of Global Methane Emissions Come from Highly Variable Aquatic Ecosystem Sources","volume":"14","author":"Rosentreter","year":"2021","journal-title":"Nat. Geosci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, C., Hu, N., Song, W., Chen, Q., and Zhu, L. (2019). Aquaculture Feeds Can Be Outlaws for Eutrophication When Hidden in Rice Fields? A Case Study in Qianjiang, China. Int. J. Environ. Res. Public. Health, 16.","DOI":"10.3390\/ijerph16224471"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, L., Song, Z., Zhou, Y., Zhong, S., Yu, Y., Liu, T., Gao, X., Li, L., Kong, C., and Wang, X. (2023). The Accumulation of Toxic Elements (Pb, Hg, Cd, As, and Cu) in Red Swamp Crayfish (Procambarus Clarkii) in Qianjiang and the Associated Risks to Human Health. Toxics, 11.","DOI":"10.3390\/toxics11070635"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"159749","DOI":"10.1016\/j.scitotenv.2022.159749","article-title":"Trace Elements in Red Swamp Crayfish (Procambarus Clarkii) in China: Spatiotemporal Variation and Human Health Implications","volume":"857","author":"Li","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103167","DOI":"10.1016\/j.agsy.2021.103167","article-title":"Understanding the Dynamics of Integrated Rice\u2013Crawfish Farming in Qianjiang County, China Using Landsat Time Series Images","volume":"191","author":"Wei","year":"2021","journal-title":"Agric. Syst."},{"key":"ref_18","first-page":"102702","article-title":"Exploring the Potential of Chinese GF-6 Images for Crop Mapping in Regions with Complex Agricultural Landscapes","volume":"107","author":"Xia","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8124","DOI":"10.1080\/01431161.2021.1973687","article-title":"Phenology-Based Decision Tree Classification of Rice-Crayfish Fields from Sentinel-2 Imagery in Qianjiang, China","volume":"42","author":"Xia","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105348","DOI":"10.1016\/j.ocecoaman.2020.105348","article-title":"Automatic Extraction of Aquaculture Ponds Based on Google Earth Engine","volume":"198","author":"Xia","year":"2020","journal-title":"Ocean Coast. Manag."},{"key":"ref_21","first-page":"13","article-title":"Extracting Aquaculture Ponds from Natural Water Surfaces around Inland Lakes on Medium Resolution Multispectral Images","volume":"80","author":"Zeng","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","unstructured":"(2023, November 07). People\u2019s Government of Qianjiang City Summary of the City\u2019s Crawfish Industry in 2021 and Priorities for 2022, Available online: http:\/\/www.hbqj.gov.cn\/xwzx\/ztbd\/qjlxsjgx\/ghzj\/202211\/t20221107_4392034.html."},{"key":"ref_23","unstructured":"(2023, November 07). China Fisheries Society 2022 China Crayfish Industry Development Report. Available online: http:\/\/www.china-cfa.org\/xwzx\/xydt\/2022\/0531\/732.html."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2019.1650447","article-title":"Land Cover and Land Use Classification Performance of Machine Learning Algorithms in a Boreal Landscape Using Sentinel-2 Data","volume":"57","author":"Abdi","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/LGRS.2010.2095409","article-title":"Implementation on Landsat Data of a Simple Cloud-Mask Algorithm Developed for MODIS Land Bands","volume":"8","author":"Oreopoulos","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/s41597-021-00827-9","article-title":"The 10-m Crop Type Maps in Northeast China during 2017\u20132019","volume":"8","author":"You","year":"2021","journal-title":"Sci. Data"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127637","DOI":"10.1016\/j.jhydrol.2022.127637","article-title":"Rapid Surface Water Expansion Due to Increasing Artificial Reservoirs and Aquaculture Ponds in North China Plain","volume":"608","author":"Zhou","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"150139","DOI":"10.1016\/j.scitotenv.2021.150139","article-title":"Leveraging Google Earth Engine Platform to Characterize and Map Small Seasonal Wetlands in the Semi-Arid Environments of South Africa","volume":"803","author":"Gxokwe","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2173659","DOI":"10.1080\/22797254.2023.2173659","article-title":"Urban Land-Use Classification Using Machine Learning Classifiers: Comparative Evaluation and Post-Classification Multi-Feature Fusion Approach","volume":"56","author":"Ouma","year":"2023","journal-title":"Eur. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pizarro, S.E., Pricope, N.G., Vargas-Machuca, D., Huanca, O., and \u00d1aupari, J. (2022). Mapping Land Cover Types for Highland Andean Ecosystems in Peru Using Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14071562"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (2000). The Nature of Statistical Learning Theory, Springer New York.","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6197","DOI":"10.1038\/s41598-022-09766-w","article-title":"Weighted P-Norm Distance t Kernel SVM Classification Algorithm Based on Improved Polarization","volume":"12","author":"Liu","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support Vector Machines in Remote Sensing: A Review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109126","DOI":"10.1016\/j.ress.2023.109126","article-title":"Support Vector Machine in Structural Reliability Analysis: A Review","volume":"233","author":"Roy","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s11273-020-09731-2","article-title":"A Comparison of Data Mining Techniques and Multi-Sensor Analysis for Inland Marshes Delineation","volume":"28","author":"Simioni","year":"2020","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"113656","DOI":"10.1016\/j.rse.2023.113656","article-title":"Multi-Sensor Detection of Spring Breakup Phenology of Canada\u2019s Lakes","volume":"295","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"112105","DOI":"10.1016\/j.rse.2020.112105","article-title":"Improving Land Cover Classification in an Urbanized Coastal Area by Random Forests: The Role of Variable Selection","volume":"251","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic Gradient Boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111521","DOI":"10.1016\/j.rse.2019.111521","article-title":"Leveraging Google Earth Engine (GEE) and Machine Learning Algorithms to Incorporate in Situ Measurement from Different Times for Rangelands Monitoring","volume":"236","author":"Zhou","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Xu, Y., Hu, Z., Zhang, Y., Wang, J., Yin, Y., and Wu, G. (2021). Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China. Remote Sens., 13.","DOI":"10.3390\/rs13214320"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2012.10.031","article-title":"Making Better Use of Accuracy Data in Land Change Studies: Estimating Accuracy and Area and Quantifying Uncertainty Using Stratified Estimation","volume":"129","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (1993). Remote Sensing Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-642-88087-2"},{"key":"ref_49","unstructured":"Davis, S.M., Landgrebe, D.A., Phillips, T.L., Swain, P.H., Hoffer, R.M., Lindenlaub, J.C., and Silva, L.F. (1978). Remote Sensing: The Quantitative Approach, McGraw-Hill International Book Co."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.rse.2017.07.025","article-title":"Spectral Analysis of Airborne Passive Microwave Measurements of Alpine Snowpack: Colorado, USA","volume":"205","author":"Kim","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yang, Y., Yang, D., Wang, X., Zhang, Z., and Nawaz, Z. (2021). Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform. Remote Sens., 13.","DOI":"10.3390\/rs13245064"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5415","DOI":"10.1080\/10106049.2021.1917005","article-title":"Detailed and Automated Classification of Land Use\/Land Cover Using Machine Learning Algorithms in Google Earth Engine","volume":"37","author":"Pan","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.1109\/JSTARS.2023.3269430","article-title":"Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China","volume":"16","author":"Han","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","first-page":"101902","article-title":"Rapid Expansion of Coastal Aquaculture Ponds in China from Landsat Observations during 1984\u20132016","volume":"82","author":"Ren","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global Land Cover Mapping at 30m Resolution: A POK-Based Operational Approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"113076","DOI":"10.1016\/j.rse.2022.113076","article-title":"Fusing Earth Observation and Socioeconomic Data to Increase the Transferability of Large-Scale Urban Land Use Classification","volume":"278","author":"Rosier","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111838","DOI":"10.1016\/j.rse.2020.111838","article-title":"Open-Source Data-Driven Urban Land-Use Mapping Integrating Point-Line-Polygon Semantic Objects: A Case Study of Chinese Cities","volume":"247","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"113758","DOI":"10.1016\/j.rse.2023.113758","article-title":"Global Urban High-Resolution Land-Use Mapping: From Benchmarks to Multi-Megacity Applications","volume":"298","author":"Zhong","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"113767","DOI":"10.1016\/j.rse.2023.113767","article-title":"Building Use and Mixed-Use Classification with a Transformer-Based Network Fusing Satellite Images and Geospatial Textual Information","volume":"297","author":"Zhou","year":"2023","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/893\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:08:27Z","timestamp":1760105307000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/893"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,2]]},"references-count":59,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050893"],"URL":"https:\/\/doi.org\/10.3390\/rs16050893","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,2]]}}}