{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:06:56Z","timestamp":1767182816553,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,27]],"date-time":"2020-09-27T00:00:00Z","timestamp":1601164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1F1A1063327"],"award-info":[{"award-number":["2019R1F1A1063327"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Prompt updates of land cover maps are important, as spatial information of land cover is widely used in many areas. However, current manual digitizing methods are time consuming and labor intensive, hindering rapid updates of land cover maps. The objective of this study was to develop an artificial intelligence (AI) based land cover classification model that allows for rapid land cover classification from high-resolution remote sensing (HRRS) images. The model comprises of three modules: pre-processing, land cover classification, and post-processing modules. The pre-processing module separates the HRRS image into multiple aspects by overlapping 75% using the sliding window algorithm. The land cover classification module was developed using the convolutional neural network (CNN) concept, based the FusionNet network and used to assign a land cover type to the separated HRRS images. Post-processing module determines ultimate land cover types by summing up the separated land cover result from the land cover classification module. Model training and validation were conducted to evaluate the performance of the developed model. The land cover maps and orthographic images of 547.29 km2 in area from the Jeonnam province in Korea were used to train the model. For model validation, two spatial and temporal different sites, one from Subuk-myeon of Jeonnam province in 2018 and the other from Daseo-myeon of Chungbuk province in 2016, were randomly chosen. The model performed reasonably well, demonstrating overall accuracies of 0.81 and 0.71, and kappa coefficients of 0.75 and 0.64, for the respective validation sites. The model performance was better when only considering the agricultural area by showing overall accuracy of 0.83 and kappa coefficients of 0.73. It was concluded that the developed model may assist rapid land cover update especially for agricultural areas and incorporation field boundary lineation is suggested as future study to further improve the model accuracy.<\/jats:p>","DOI":"10.3390\/rs12193171","type":"journal-article","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T08:02:58Z","timestamp":1601280178000},"page":"3171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Development of Land Cover Classification Model Using AI Based FusionNet Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0096-0058","authenticated-orcid":false,"given":"Jinseok","family":"Park","sequence":"first","affiliation":[{"name":"Global Smart Farm Convergence Major, Department of Rural Systems Engineering, Seoul National University, Seoul 08826, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seongju","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Rural Systems Engineering, Seoul National University, Seoul 08826, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2589-1199","authenticated-orcid":false,"given":"Rokgi","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Rural Systems Engineering, Seoul National University, Seoul 08826, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0248-0146","authenticated-orcid":false,"given":"Kyo","family":"Suh","sequence":"additional","affiliation":[{"name":"Graduate School of International Agricultural Technology, Institute of Green Bio Science Technology, Seoul National University, Gangwon 25354, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6847-2472","authenticated-orcid":false,"given":"Inhong","family":"Song","sequence":"additional","affiliation":[{"name":"Global Smart Farm Convergence Major, Research Institute of Agriculture and Life sciences, Department of Rural Systems Engineering, Seoul National University, Seoul 08826, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1080\/11263500802410892","article-title":"The concept of land ecological network and its design using a land unit approach","volume":"142","author":"Blasi","year":"2008","journal-title":"Plant Biosyst."},{"key":"ref_2","first-page":"1","article-title":"A multitarget land use change simulation model based on cellular automata and its application","volume":"2014","author":"Yang","year":"2014","journal-title":"Abstr. Appl. Anal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11586","DOI":"10.3390\/rs70911586","article-title":"Satellite-observed energy budget change of deforestation in northeastern China and its climate implications","volume":"7","author":"He","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.rse.2011.08.025","article-title":"Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources","volume":"122","author":"Anderson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1029\/2007WR006644","article-title":"Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions","volume":"44","author":"Schilling","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1111\/j.1467-9493.2010.00394.x","article-title":"Six decades of agricultural land use change in Bangladesh: Effects on crop diversity, productivity, food availability and the environment, 1948\u20132006","volume":"31","author":"Rahman","year":"2010","journal-title":"Singap. J. Trop. Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16062","DOI":"10.3390\/rs71215815","article-title":"Building a data set over 12 globally distributed sites to support the development of agriculture monitoring applications with Sentinel-2","volume":"7","author":"Bontemps","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-02142-7","article-title":"An assessment of the global impact of 21st century land use change on soil erosion","volume":"8","author":"Borrelli","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.7745\/KJSSF.2012.45.6.1164","article-title":"Farmland use mapping using high resolution images and land use change analysis","volume":"45","author":"Kyungdo","year":"2012","journal-title":"Korean J. Soil Sci. Fertil."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3301","DOI":"10.1080\/01431161.2011.568531","article-title":"Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image","volume":"33","author":"Song","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","first-page":"105","article-title":"Land cover object-oriented base classification using digital aerial photo image","volume":"19","author":"Lee","year":"2011","journal-title":"J. Korean Soc. Geospat. Inf. Syst."},{"key":"ref_12","first-page":"107","article-title":"An empirical study on the land cover classification method using IKONOS image","volume":"6","author":"Sakong","year":"2003","journal-title":"J. Korean Assoc. Geogr. Inf. Stud."},{"key":"ref_13","first-page":"65","article-title":"Integrating supervised and unsupervised classification methods to develop a more accurate land cover classification","volume":"59","author":"Enderle","year":"2005","journal-title":"J. Ark. Acad. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/TGRS.2006.864391","article-title":"Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory","volume":"44","author":"Laha","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1007\/s12524-019-00945-3","article-title":"Object-oriented method combined with deep convolutional neural networks for land-use-type classification of remote sensing images","volume":"47","author":"Jin","year":"2019","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/TPAMI.2015.2474388","article-title":"Pedestrian detection with spatially pooled features and structured ensemble learning","volume":"38","author":"Paisitkriangkrai","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1109\/LGRS.2015.2391297","article-title":"Land-cover classification of remotely sensed images using compressive sensing having severe scarcity of labeled patterns","volume":"12","author":"Roy","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","first-page":"32","article-title":"Classification of multispectral satellite images using clustering with SVM classifier","volume":"35","author":"Prasad","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2012.2202912","article-title":"An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery","volume":"51","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"75","article-title":"Land cover classification of image data using artificial neural networks","volume":"12","author":"Kang","year":"2006","journal-title":"J. Korean Soc. Rural Plan."},{"key":"ref_21","first-page":"65","article-title":"An analysis of land cover classification methods using IKONOS satellite image","volume":"20","author":"Kang","year":"2012","journal-title":"J. Korean Soc. Geospat. Inf. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/978-1-4020-4385-7_10","article-title":"Segmentation and object-based image analysis","volume":"10","author":"Lang","year":"2010","journal-title":"Remote Sens. Urban Suburb. Areas"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"L\u00e4ngkvist, M., Kiselev, A., Alirezaie, M., and Loutfi, A. (2016). Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sens., 8.","DOI":"10.3390\/rs8040329"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1017\/S0021859618000436","article-title":"A review of the use of convolutional neural networks in agriculture","volume":"156","author":"Kamilaris","year":"2018","journal-title":"J. Agric. Sci."},{"key":"ref_25","unstructured":"Gavade, A.B., and Rajpurohit, V.S. (2019). Systematic analysis of satellite image-based land cover classification techniques: Literature review and challenges. Int. J. Comput. Appl., 1\u201310."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1109\/LGRS.2015.2483680","article-title":"Multiview deep learning for land-use classification","volume":"12","author":"Luus","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.procs.2015.07.525","article-title":"Cattle race classification using gray level co-occurrence matrix convolutional neural networks","volume":"59","author":"Santoni","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Chan, C.S., Wilkin, P., and Remagnino, P. (2015, January 27\u201330). Deep-plant: Plant identification with convolutional neural networks. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350839"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.compag.2016.07.003","article-title":"Deep learning for plant identification using vein morphological patterns","volume":"127","author":"Grinblat","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Fu, G., Liu, C., Zhou, R., Sun, T., and Zhang, Q. (2017). Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sens., 9.","DOI":"10.3390\/rs9050498"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s11629-016-3950-2","article-title":"Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning","volume":"14","author":"Lu","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Carranza-Garc\u00eda, M., Garc\u00eda-Guti\u00e9rrez, J., and Riquelme, J. (2019). A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11030274"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.isprsjprs.2020.05.022","article-title":"Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters","volume":"166","author":"Pan","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","unstructured":"Quan, T.M., Hildebrand, D.G., and Jeong, W.K. (2016). Fusionnet: A deep fully residual convolutional neural network for image segmentation in connectomics. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016, January 17\u201321). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_39","unstructured":"Shang, W., Sohn, K., Almeida, D., and Lee, H. (2016, January 19\u201324). Understanding and improving convolutional neural networks via concatenated rectified linear units. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_40","unstructured":"Zeiler, M.D., and Fergus, R. (2013). Stochastic pooling for regularization of deep convolutional neural networks. arXiv."},{"key":"ref_41","unstructured":"(2020, September 27). Cultivated Area by City and County in 2017 from Kostat Total Survey of Agriculture, Forestry and Fisheries. Available online: http:\/\/kosis.kr\/statHtml\/statHtml.do?orgId=101&tblId=DT_1EB002&conn_path=I2."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"26","DOI":"10.11108\/kagis.2012.15.4.026","article-title":"A study on object-based image analysis methods for land cover classification in agricultural areas","volume":"15","author":"Kim","year":"2012","journal-title":"J. Korean Assoc. Geogr. Inf. Stud."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3171\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:14:18Z","timestamp":1760177658000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,27]]},"references-count":43,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193171"],"URL":"https:\/\/doi.org\/10.3390\/rs12193171","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,9,27]]}}}