{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T20:07:05Z","timestamp":1774987625839,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030141318","type":"print"},{"value":"9783030141325","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,3,6]],"date-time":"2019-03-06T00:00:00Z","timestamp":1551830400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-14132-5_15","type":"book-chapter","created":{"date-parts":[[2019,3,5]],"date-time":"2019-03-05T03:59:54Z","timestamp":1551758394000},"page":"191-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Land Cover Classification Based on Sentinel-2 Satellite Imagery Using Convolutional Neural Network Model: A Case Study in Semarang Area, Indonesia"],"prefix":"10.1007","author":[{"given":"Yaya","family":"Heryadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eka","family":"Miranda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,3,6]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Weng, Q., Mao, Z., Lin, J., Liao, X.: Land-use scene classification based on a CNN using a constrained extreme learning machine. Int. J. Remote Sensing pp. 1\u201318 (2018)","DOI":"10.1080\/01431161.2018.1458346"},{"key":"15_CR2","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep learning (adaptive computation and mechine learning series). The MIT Press (2016)"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Dahl, J.V., Koch, K.C., Kleinhans, E., Ostwald, E., Schulz, G., Buell, U., Hanrath, P.: Convolutional networks and applications in vision. In: Proceedings of IEEE International Symposium on Circuits and Systems, pp. 253\u2013256, Paris, France (2010)","DOI":"10.1109\/ISCAS.2010.5537907"},{"issue":"1","key":"15_CR4","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1109\/TGRS.2011.2159726","volume":"50","author":"S Moustakidis","year":"2012","unstructured":"Moustakidis, S., Mallinis, G., Koutsias, N., Theocharis, J.B.: SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 50(1), 149\u2013169 (2012)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"15_CR5","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/BF00130487","volume":"7","author":"MJ Swain","year":"1991","unstructured":"Swain, M.J., Ballard, D.H.: Color Indexing. Int. J. Comput. Vision 7(1), 11\u201332 (1991)","journal-title":"Int. J. Comput. Vision"},{"issue":"7","key":"15_CR6","doi-asserted-by":"publisher","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","volume":"55","author":"GS Xia","year":"2017","unstructured":"Xia, G.S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L.: AID: a benchmark dataset for performance evaluation of aerial scene classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3965\u20133981 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Zhao, W., Du, S., Emery, W.J.: Object-based convolutional neural network for high-resolution imagery classification. IEEE J. Sel Topics Appl. Earth Observations And Remote Sensing, pp. 3386\u20133396 (2017)","DOI":"10.1109\/JSTARS.2017.2680324"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Kussul, N., Lavreniuk, M., Skakun, S. Shelestov, A.: Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sensing Lett., pp. 778\u2013782 (2017)","DOI":"10.1109\/LGRS.2017.2681128"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Zhu, Yi., Newsam, S.: Land use classification using convolutional neural networks applied to ground-level images, In: International Conference on Advances in Geographic Information Systems Proceedings on Proceedings, pp 1\u20134, Seattle, USA (2015)","DOI":"10.1145\/2820783.2820851"},{"issue":"3","key":"15_CR10","first-page":"251","volume":"4","author":"C Yang","year":"2018","unstructured":"Yang, C., Rottensteiner, F., Heipke, C.: Classification of land cover and land use based on convolutional neural networks. ISPRS Annals Photogr. Remote Sensing Spatial Inf. Sci. 4(3), 251\u2013258 (2018)","journal-title":"ISPRS Annals Photogr. Remote Sensing Spatial Inf. Sci."},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Kampffmeyer, M., Salberg, A., Jenssen, R.: Urban land cover classification with missing data using deep convolutional neural networks, In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Texas, USA (2017)","DOI":"10.1109\/IGARSS.2017.8128164"},{"issue":"2","key":"15_CR12","first-page":"1091","volume":"42","author":"K Suzuki","year":"2018","unstructured":"Suzuki, K., Rin, U., Maeda, Y., Takeda, H.: Forest cover classification using geospatial multimodal data. Remote Sensing Spatial Inf. Sci. 42(2), 1091\u20131096 (2018)","journal-title":"Remote Sensing Spatial Inf. Sci."},{"issue":"1746","key":"15_CR13","first-page":"1","volume":"10","author":"R Gaetano","year":"2018","unstructured":"Gaetano, R., Ienco, D., Ose, K., Cresson, R.: A two-branch CNN architecture for land cover classification of PAN and MS imagery. Remote Sensing. 10(1746), 1\u201320 (2018)","journal-title":"Remote Sensing."},{"key":"15_CR14","unstructured":"National Standardization Agency. Indonesian national standard RSNI-1. Land cover class in medium resolution optical imagery interpretation. Indonesia"},{"key":"15_CR15","unstructured":"Feature extraction based on object oriented analysis. http:\/\/www.ecognition.com, last accessed 2018\/12\/03"},{"key":"15_CR16","unstructured":"Sentinel 2 EO. (n.d.). Sentinel Hub. Retrieved 10 Apr 2018, from Sentinel 2 EO products: https:\/\/sentinel-hub.com\/develop\/documentation\/eo_products\/Sentinel2EOproducts, last accessed 2018\/11\/28"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Belgiau, M., Dragut, L., Strobl, J.: quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS J. Photogr. Remote Sensing, pp. 205\u2013215 (2014)","DOI":"10.1016\/j.isprsjprs.2013.11.007"},{"key":"15_CR18","unstructured":"Indonesia Ministery of Forest and Environment (2017). WebGIS Indonesia Ministery of Forest and Environment. Retrieved 20 Apr 2018, from Peta Interaktif KLHK: http:\/\/webgis.dephut.go.id:8080\/kemenhut\/index.php\/id\/peta\/peta-interaktif (2018, last accessed 2018\/11\/28"},{"key":"15_CR19","unstructured":"Brownlee, J.: Machine learning mastery from feature importance and feature selection with XGBoost in Python. https:\/\/machinelearningmastery.com\/feature-importance-and-feature-selection-with-xgboost-in-python\/. Last accesses 2018\/10\/15"}],"container-title":["Studies in Computational Intelligence","Intelligent Information and Database Systems: Recent Developments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-14132-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:33:33Z","timestamp":1774985613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-14132-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,6]]},"ISBN":["9783030141318","9783030141325"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-14132-5_15","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"value":"1860-949X","type":"print"},{"value":"1860-9503","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,6]]},"assertion":[{"value":"6 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yogyakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}