{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:11:18Z","timestamp":1778371878836,"version":"3.51.4"},"reference-count":84,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2019YFE0127600"],"award-info":[{"award-number":["2019YFE0127600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate knowledge of urban forest patterns contributes to well-managed urbanization, but accurate urban tree canopy mapping is still a challenging task because of the complexity of the urban structure. In this paper, a new method that combines double-branch U-NET with multi-temporal satellite images containing phenological information is introduced to accurately map urban tree canopies. Based on the constructed GF-2 image dataset, we developed a double-branch U-NET based on the feature fusion strategy using multi-temporal images to obtain an accuracy improvement with an IOU (intersection over union) of 2.3% and an F1-Score of 1.3% at the pixel level compared to the U-NET using mono-temporal images which performs best in existing studies for urban tree canopy mapping. We also found that the double-branch U-NET based on the feature fusion strategy has better accuracy than the early fusion strategy and decision fusion strategy in processing multi-temporal images for urban tree canopy mapping. We compared the impact of image combinations of different seasons on the urban tree canopy mapping task and found that the combination of summer and autumn images had the highest accuracy in the study area. Our research not only provides a high-precision urban tree canopy mapping method but also provides a direction to improve the accuracy both from the model structure and data potential when using deep learning for urban tree canopy mapping.<\/jats:p>","DOI":"10.3390\/rs15030765","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T10:19:28Z","timestamp":1675073968000},"page":"765","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Urban Tree Canopy Mapping Based on Double-Branch Convolutional Neural Network and Multi-Temporal High Spatial Resolution Satellite Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0074-9583","authenticated-orcid":false,"given":"Shuaiqiang","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environmental and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environmental and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environmental and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Mao","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjun","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environmental and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3510-2002","authenticated-orcid":false,"given":"Wenxuan","family":"Bao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory for Remote Sensing of Environmental and Digital Cities, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ufug.2017.05.005","article-title":"The social and economic value of cultural ecosystem services provided by urban forests in North America: A review and suggestions for future research","volume":"25","author":"Nesbitt","year":"2017","journal-title":"Urban For. Urban Green."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.ecoser.2018.07.006","article-title":"Business attitudes towards funding ecosystem services provided by urban forests","volume":"32","author":"Davies","year":"2018","journal-title":"Ecosyst. Serv."},{"key":"ref_3","unstructured":"United Nations Department for Economic and Social Affairs (2018). World Urbanization Prospects 2018, United Nations Department for Economic and Social Affairs."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bao, W., Gong, A., Zhao, Y., Chen, S., Ba, W., and He, Y. (2022). High-Precision Population Spatialization in Metropolises Based on Ensemble Learning: A Case Study of Beijing, China. Remote Sens., 14.","DOI":"10.3390\/rs14153654"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bao, W., Gong, A., Zhang, T., Zhao, Y., Li, B., and Chen, S. (2023). Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data. Remote Sens., 15.","DOI":"10.3390\/rs15020458"},{"key":"ref_6","unstructured":"(2023, January 05). The World Bank. Available online: https:\/\/data.worldbank.org\/indicator\/SP.URB.TOTL.IN.ZS."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1146\/annurev-environ-100809-125336","article-title":"The New Geography of Contemporary Urbanization and the Environment","volume":"35","author":"Seto","year":"2010","journal-title":"Annu. Rev. Envron. Resour."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1017\/RDC.2019.92","article-title":"Human Activity recorded in carbon isotopic composition of atmospheric CO2 in Gliwice urban area and surroundings (southern Poland) in the years 2011\u20132013","volume":"62","author":"Piotrowska","year":"2020","journal-title":"Radiocarbon"},{"key":"ref_9","unstructured":"Chaparro, L., and Terradas, J. (2009). Ecological Services of Urban Forest in Barcelona, Institut Municipal de Parcs i Jardins Ajuntament de Barcelona, \u00c0rea de Medi Ambient."},{"key":"ref_10","unstructured":"Sensu\u0142a, B., Wilczy\u0144ski, S., and Piotrowska, N. (2017, January 23\u201328). Bio-monitoring of the most industrialized area in Poland: Trees\u2019 response to climate and anthropogenic environmental changes. Proceedings of the 19th EGU General Assembly, Vienna, Austria."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.envpol.2013.03.019","article-title":"Carbon storage and sequestration by trees in urban and community areas of the United States","volume":"178","author":"Nowak","year":"2013","journal-title":"Environ. Pollut."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ufug.2018.07.023","article-title":"Influence of evaporative cooling by urban forests on cooling demand in cities","volume":"37","author":"Moss","year":"2019","journal-title":"Urban For. Urban Green."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1007\/s13280-014-0507-x","article-title":"Contribution of Ecosystem Services to Air Quality and Climate Change Mitigation Policies: The Case of Urban Forests in Barcelona, Spain","volume":"43","author":"Chaparro","year":"2014","journal-title":"AMBIO"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tyrv\u00e4inen, L., Pauleit, S., Seeland, K., and Vries, S.D. (2005). Benefits and Uses of Urban Forests and Trees, Springer.","DOI":"10.1007\/3-540-27684-X_5"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2458-6-149","article-title":"Vitamin G: Effects of green space on health, well-being, and social safety","volume":"6","author":"Groenewegen","year":"2006","journal-title":"BMC Public Health"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.landurbplan.2017.09.025","article-title":"Locating provisioning ecosystem services in urban forests: Forageable woody species in New York City, USA","volume":"170","author":"Hurley","year":"2018","journal-title":"Landsc. Urban Plan"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1007\/s11676-019-00916-x","article-title":"Towards an integrative approach to evaluate the environmental ecosystem services provided by urban forest","volume":"30","author":"Roeland","year":"2019","journal-title":"J. For. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, W.Y., Sanesi, G., and Lafortezza, R. (2019). Remote Sensing in Urban Forestry: Recent Applications and Future Directions. Remote Sens., 11.","DOI":"10.3390\/rs11101144"},{"key":"ref_19","unstructured":"Grove, J.M., Neil-Dunne, J.O., Pelletier, K., Nowak, D., and Walton, J. (2006). A Report on New York City\u2019s Present and Possible Urban Tree Canopy, United States Department of Agriculture, Forest Service."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2005.12.001","article-title":"A temporal analysis of urban forest carbon storage using remote sensing","volume":"101","author":"Myeong","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.ufug.2016.04.003","article-title":"Mapping urban forest structure and function using hyperspectral imagery and lidar data","volume":"17","author":"Alonzo","year":"2016","journal-title":"Urban For. Urban Green."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.ecolind.2017.05.014","article-title":"Combining high-resolution images and LiDAR data to model ecosystem services perception in compact urban systems","volume":"96","author":"Lafortezza","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_23","first-page":"400","article-title":"Review on the Use of Remote Sensing for Urban Forest Monitoring","volume":"42","author":"Shojanoori","year":"2016","journal-title":"Arboric. Urban For."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.apgeog.2008.10.001","article-title":"Modeling urban leaf area index with AISA+ hyperspectral data","volume":"29","author":"Jensen","year":"2009","journal-title":"Appl. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11252-016-0574-9","article-title":"Dynamic heterogeneity: A framework to promote ecological integration and hypothesis generation in urban systems","volume":"20","author":"Pickett","year":"2017","journal-title":"Urban Ecosyst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1890\/1540-9295(2007)5[80:SHIUER]2.0.CO;2","article-title":"Spatial heterogeneity in urban ecosystems: Reconceptualizing land cover and a framework for classification","volume":"5","author":"Cadenasso","year":"2007","journal-title":"Front. Ecol. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3054","DOI":"10.3390\/rs13163054","article-title":"Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning","volume":"13","author":"Martins","year":"2021","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.rse.2012.06.011","article-title":"A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species","volume":"124","author":"Pu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"Hamerly, G., and Elkan, C. (2003). Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1142\/S0218195907002252","article-title":"A fast implementation of the ISODATA clustering algorithm","volume":"17","author":"Memarsadeghi","year":"2007","journal-title":"Int. J. Comput. Geom. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2797","DOI":"10.1007\/s40747-021-00457-z","article-title":"Remote sensing techniques: Mapping and monitoring of mangrove ecosystem\u2014A review","volume":"7","author":"Maurya","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3845","DOI":"10.3390\/rs12223845","article-title":"A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images","volume":"12","author":"Xu","year":"2020","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1002\/cem.873","article-title":"An introduction to decision tree modeling","volume":"18","author":"Myles","year":"2004","journal-title":"J. Chemom."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","article-title":"A random forest guided tour","volume":"25","author":"Biau","year":"2016","journal-title":"Test"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1109\/36.905255","article-title":"Unsupervised retraining of a maximum likelihood classifier for the analysis of multitemporal remote sensing images","volume":"39","author":"Bruzzone","year":"2001","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Shinde, P.P., and Shah, S. (2018). A Review of Machine Learning and Deep Learning Applications, IEEE.","DOI":"10.1109\/ICCUBEA.2018.8697857"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e1264","DOI":"10.1002\/widm.1264","article-title":"Deep learning for remote sensing image classification: A survey","volume":"8","author":"Li","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Han, L., and Zhu, L. (2020). How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?. Remote Sens., 12.","DOI":"10.3390\/rs12030417"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pluto-Kossakowska, J. (2021). Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition. Agriculture, 11.","DOI":"10.3390\/agriculture11100999"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhou, Y., Wang, F., Wang, S., and Xu, Z. (2021). SDGH-Net: Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression. Remote Sens., 13.","DOI":"10.3390\/rs13030499"},{"key":"ref_47","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An introduction to convolutional neural networks. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recogn."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Networks Learn."},{"key":"ref_50","first-page":"84","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101279","DOI":"10.1016\/j.ecoinf.2021.101279","article-title":"Amazon forest cover change mapping based on semantic segmentation by U-Nets","volume":"62","author":"Bragagnolo","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_55","unstructured":"Chen, L., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the 15th European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wagner, F.H., and Hirye, M.C.M. (2019). Tree Cover for the Year 2010 of the Metropolitan Region of S\u00e3o Paulo, Brazil. Data, 4.","DOI":"10.3390\/data4040145"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Wang, Z., Fan, C., and Xian, M. (2021). Application and Evaluation of a Deep Learning Architecture to Urban Tree Canopy Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13091749"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1111\/gcb.14619","article-title":"Plant phenology and global climate change: Current progresses and challenges","volume":"25","author":"Piao","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring vegetation phenology using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.rse.2014.10.018","article-title":"Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery","volume":"156","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.ufug.2018.01.021","article-title":"Mapping vegetation functional types in urban areas with WorldView-2 imagery: Integrating object-based classification with phenology","volume":"31","author":"Yan","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ulsig, L., Nichol, C.J., Huemmrich, K.F., Landis, D.R., Middleton, E.M., Lyapustin, A.I., Mammarella, I., Levula, J., and Porcar-Castell, A. (2017). Detecting inter-annual variations in the phenology of evergreen conifers using long-term MODIS vegetation index time series. Remote Sens., 9.","DOI":"10.3390\/rs9010049"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s10661-010-1327-5","article-title":"Mapping urban forest tree species using IKONOS imagery: Preliminary results","volume":"172","author":"Pu","year":"2011","journal-title":"Environ. Monit. Assess"},{"key":"ref_65","first-page":"144","article-title":"Assessing the potential of multi-seasonal high resolution Pl\u00e9iades satellite imagery for mapping urban tree species","volume":"71","author":"Pu","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","article-title":"Multisource Remote Sensing Data Classification Based on Convolutional Neural Network","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Gaetano, R., Ienco, D., Ose, K., and Cresson, R. (2018). A Two-Branch CNN Architecture for Land Cover Classification of PAN and MS Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10111746"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Guo, Y., Li, Z., Chen, E., Zhang, X., Zhao, L., Xu, E., Hou, Y., and Liu, L. (2021). A Deep Fusion uNet for Mapping Forests at Tree Species Levels with Multi-Temporal High Spatial Resolution Satellite Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13183613"},{"key":"ref_69","first-page":"4381","article-title":"EMFNet: Enhanced Multisource Fusion Network for Land Cover Classification","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Stars"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"112794","DOI":"10.1016\/j.rse.2021.112794","article-title":"The urban morphology on our planet\u2014Global perspectives from space","volume":"269","author":"Zhu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, J., Feng, Q., and Yin, D. (2020). Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches. Remote Sens., 12.","DOI":"10.3390\/rs12111744"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Guo, Y., Li, Z., Chen, E., Zhang, X., Zhao, L., Xu, E., Hou, Y., and Sun, R. (2020). An End-to-End Deep Fusion Model for Mapping Forests at Tree Species Levels with High Spatial Resolution Satellite Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12203324"},{"key":"ref_73","first-page":"3988","article-title":"Automatic Extraction of Built-Up Areas From Panchromatic and Multispectral Remote Sensing Images Using Double-Stream Deep Convolutional Neural Networks","volume":"11","author":"Tan","year":"2018","journal-title":"IEEE J. Stars"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Ali, A.V., Comai, S., and Matteucci, M. (2020). Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12152495"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.fcr.2019.02.022","article-title":"Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images","volume":"235","author":"Yang","year":"2019","journal-title":"Field Crop. Res."},{"key":"ref_76","unstructured":"Beijing Municipal Forestry and Parks Bureau (2022, August 13). The Report on Work Completion in 2021 of Beijing Municipal Forestry and Parks Bureau, Available online: http:\/\/yllhj.beijing.gov.cn\/zwgk\/sx\/202201\/t20220106_2584218.shtml."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"111719","DOI":"10.1117\/1.OE.51.11.111719","article-title":"Quick atmospheric correction code: Algorithm description and recent upgrades","volume":"51","author":"Bernstein","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_78","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"Kroese","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S. (2016). V-net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation, IEEE.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M. (2017). Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations, Springer.","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., and Pal, C. (2016). The Importance of Skip Connections in Biomedical Image Segmentation, Springer.","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Chen, M., Wu, J., Liu, L., Zhao, W., Tian, F., Shen, Q., Zhao, B., and Du, R. (2021). DR-Net: An Improved Network for Building Extraction from High Resolution Remote Sensing Image. 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