{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T09:56:57Z","timestamp":1792490217124,"version":"4.5.0"},"reference-count":69,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,19]],"date-time":"2020-05-19T00:00:00Z","timestamp":1589846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rice is an important agricultural crop in the Southwest Hilly Area, China, but there has been a lack of efficient and accurate monitoring methods in the region. Recently, convolutional neural networks (CNNs) have obtained considerable achievements in the remote sensing community. However, it has not been widely used in mapping a rice paddy, and most studies lack the comparison of classification effectiveness and efficiency between CNNs and other classic machine learning models and their transferability. This study aims to develop various machine learning classification models with remote sensing data for comparing the local accuracy of classifiers and evaluating the transferability of pretrained classifiers. Therefore, two types of experiments were designed: local classification experiments and model transferability experiments. These experiments were conducted using cloud-free Sentinel-2 multi-temporal data in Banan District and Zhongxian County, typical hilly areas of Southwestern China. A pure pixel extraction algorithm was designed based on land-use vector data and a Google Earth Online image. Four convolutional neural network (CNN) algorithms (one-dimensional (Conv-1D), two-dimensional (Conv-2D) and three-dimensional (Conv-3D_1 and Conv-3D_2) convolutional neural networks) were developed and compared with four widely used classifiers (random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP)). Recall, precision, overall accuracy (OA) and F1 score were applied to evaluate classification accuracy. The results showed that Conv-2D performed best in local classification experiments with OA of 93.14% and F1 score of 0.8552 in Banan District, OA of 92.53% and F1 score of 0.8399 in Zhongxian County. CNN-based models except Conv-1D provided more desirable performance than non-CNN classifiers. Besides, among the non-CNN classifiers, XGBoost received the best result with OA of 89.73% and F1 score of 0.7742 in Banan District, SVM received the best result with OA of 88.57% and F1 score of 0.7538 in Zhongxian County. In model transferability experiments, almost all CNN classifiers had low transferability. RF and XGBoost models have achieved acceptable F1 scores for transfer (RF = 0.6673 and 0.6469, XGBoost = 0.7171 and 0.6709, respectively).<\/jats:p>","DOI":"10.3390\/rs12101620","type":"journal-article","created":{"date-parts":[[2020,5,20]],"date-time":"2020-05-20T02:48:24Z","timestamp":1589942904000},"page":"1620","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Mapping Rice Paddy Based on Machine Learning with Sentinel-2 Multi-Temporal Data: Model Comparison and Transferability"],"prefix":"10.3390","volume":"12","author":[{"given":"Weichun","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Resources and Environment, Southwest University, Chongqing 400716, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Southwest University, Chongqing 400716, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400716, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linqing","family":"Zhan","sequence":"additional","affiliation":[{"name":"Chongqing Agricultural Technology Extension Station, Chongqing Municipal Committee of Agriculture and Rural Affairs, Chongqing 400121, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8803-7056","authenticated-orcid":false,"given":"Jing","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100000, China"},{"name":"Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17111","DOI":"10.1038\/nplants.2017.111","article-title":"Food security Ghana rice revolution falters","volume":"3","author":"Scarrow","year":"2017","journal-title":"Nat. Plants"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1126\/science.1185383","article-title":"Food security: The challenge of feeding 9 billion people","volume":"327","author":"Godfray","year":"2010","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"117","DOI":"10.12911\/22998993\/108651","article-title":"Cellular automata markov method, an approach for rice self-sufficiency projection","volume":"20","author":"Sutrisno","year":"2019","journal-title":"J. Ecol. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, Y.S. (2019). The challenges and strategies of food security under rapid urbanization in China. Sustainability, 11.","DOI":"10.3390\/su11020542"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.foodres.2014.03.015","article-title":"Food demand and the food security challenge with rapid economic growth in the emerging economies of India and China","volume":"63","author":"Gandhi","year":"2014","journal-title":"Food Res. Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/S0065-2113(04)92004-4","article-title":"Rice and water","volume":"92","author":"Bouman","year":"2007","journal-title":"Adv. Agron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1111\/gcb.12034","article-title":"Methane emissions from rice paddies natural wetlands, lakes in China: Synthesis new estimate","volume":"19","author":"Chen","year":"2013","journal-title":"Glob. Chang. Biol."},{"key":"ref_8","first-page":"124","article-title":"Remote-sensing and image interpretation\u2014Lillesand, Tm, Kiefer, Rw","volume":"96","author":"Kirby","year":"1980","journal-title":"Scot. Geogr. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1080\/22797254.2017.1279819","article-title":"A highly efficient temporal-spatial probability synthesized model from multi-temporal remote sensing for paddy rice identification","volume":"50","author":"Sun","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4343","DOI":"10.1080\/01431160802549369","article-title":"Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset","volume":"30","author":"Motohka","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.isprsjprs.2015.05.011","article-title":"Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data","volume":"106","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","first-page":"13","article-title":"Detection and estimation of mixed paddy rice cropping patterns with MODIS data","volume":"13","author":"Peng","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2005.09.007","article-title":"Spatio-temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers","volume":"100","author":"Sakamoto","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1080\/01431160110107734","article-title":"Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data","volume":"23","author":"Xiao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, M., Lin, H., Wang, G.X., Sun, H., and Fu, J. (2018). Mapping paddy rice using a Convolutional Neural Network (CNN) with landsat 8 datasets in the Dongting Lake Area, China. Remote Sens., 10.","DOI":"10.3390\/rs10111840"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2001","DOI":"10.1016\/S2095-3119(12)60457-2","article-title":"Common spectral bands and optimum vegetation indices for monitoring leaf nitrogen accumulation in rice and wheat","volume":"11","author":"Wang","year":"2012","journal-title":"J. Integr. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.isprsjprs.2015.04.008","article-title":"Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery","volume":"105","author":"Qin","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11707-015-0518-3","article-title":"Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China","volume":"10","author":"Jin","year":"2016","journal-title":"Front. Earth Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"63526","DOI":"10.1117\/1.JRS.6.063526","article-title":"Mapping paddy rice agriculture in a highly fragmented area using a geographic information system object-based post classification process","volume":"6","author":"Shiu","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1080\/17538947.2017.1356388","article-title":"Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data","volume":"11","author":"Liu","year":"2018","journal-title":"Int. J. Digit Earth"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y.H., Ling, F., Wang, Q.M., Li, W.B., and Li, X.D. (2016). Water bodies\u2019 mapping from sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.biosystemseng.2018.04.020","article-title":"Forecasting maize yield at field scale based on high-resolution satellite imagery","volume":"171","author":"Schwalbert","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"046003","DOI":"10.1117\/1.JRS.11.046003","article-title":"Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information","volume":"11","author":"Mishra","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1080\/01431161.2017.1395969","article-title":"Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data","volume":"39","author":"Onojeghuo","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.compag.2008.07.009","article-title":"Quantifying sub-pixel signature of paddy rice field using an artificial neural network","volume":"65","author":"Karkee","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/258619","article-title":"Deep convolutional neural networks for hyperspectral image classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2015.1047045","article-title":"Spectral-spatial classification of hyperspectral images using deep convolutional neural networks","volume":"6","author":"Yue","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, R.S., Yang, X.C., Wang, J.X., and Latif, A. (2018). Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning. Water, 10.","DOI":"10.3390\/w10050585"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep learning based multi-temporal crop classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.atmosenv.2016.09.020","article-title":"A comprehensive approach for the evaluation and comparison of emission inventories in Madrid","volume":"145","author":"Vedrenne","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Vakalopoulou, M., Karantzalos, K., Komodakis, N., and Paragios, N. (2015, January 26\u201331). Building detection in very high resolution multispectral data with deep learning features. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2017.08.011","article-title":"Contextually guided very-high-resolution imagery classification with semantic segments","volume":"132","author":"Zhao","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Tang, H. (2019). A comprehensive evaluation of approaches for built-up area extraction from landsat oli images using massive samples. Remote Sens., 11.","DOI":"10.20944\/preprints201812.0067.v1"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sen. Mag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"042609","DOI":"10.1117\/1.JRS.11.042609","article-title":"Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community","volume":"11","author":"Ball","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep learning classification of land cover and crop types using remote sensing data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104162","DOI":"10.1016\/j.landusepol.2019.104162","article-title":"Socio-cultural roots of rural settlement dispersion in Sichuan Basin: The perspective of Chinese lineage","volume":"88","author":"Liu","year":"2019","journal-title":"Land Use Policy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.apgeog.2013.07.010","article-title":"Ecosystem health assessment based on analysis of a land use database","volume":"44","author":"Yu","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Guidici, D., and Clark, M.L. (2017). One-Dimensional convolutional neural network land-cover classification of multi-seasonal hyperspectral imagery in the San Francisco Bay Area, California. Remote Sens., 9.","DOI":"10.3390\/rs9060629"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., and Cribb, M. (2019). Estimating 1-km-resolution PM2. 5 concentrations across China using the space-time random forest approach. Remote Sens. Environ., 231.","DOI":"10.1016\/j.rse.2019.111221"},{"key":"ref_51","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_52","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 22\u201327). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TGRS.1990.572944","article-title":"Neural network approaches versus statistical methods in classification of multisource remote sensing data","volume":"28","author":"Benediktsson","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"016020","DOI":"10.1117\/1.JRS.12.016020","article-title":"UFCN: A fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle","volume":"12","author":"Kestur","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","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":"GISci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., and Clune, J. (2015, January 7\u201312). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"ref_62","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 Medical Image Computing and Computer (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1080\/01431161.2018.1528024","article-title":"A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery","volume":"40","author":"Ji","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5631","DOI":"10.1029\/2018WR024136","article-title":"Measuring river wetted width from remotely sensed imagery at the subpixel scale with a deep convolutional neural network","volume":"55","author":"Ling","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2019.06.010","article-title":"Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information","volume":"154","author":"Fang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201321). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"13265","DOI":"10.1021\/acs.est.9b03258","article-title":"Satellite-derived 1-km-resolution PM1 concentrations from 2014 to 2018 across China","volume":"53","author":"Wei","year":"2019","journal-title":"Environ. Sci. Technol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.5194\/acp-20-3273-2020","article-title":"Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees","volume":"20","author":"Wei","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_69","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 3\u20136). How transferable are features in deep neural networks?. Proceedings of the International Conference on Neural Information Processing Systems (ICONIP), Kuching, Malaysia."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/10\/1620\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:30:13Z","timestamp":1760175013000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/10\/1620"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,19]]},"references-count":69,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12101620"],"URL":"https:\/\/doi.org\/10.3390\/rs12101620","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,19]]}}}