{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:12:12Z","timestamp":1783570332897,"version":"3.55.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s00521-025-11314-2","type":"journal-article","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T23:52:27Z","timestamp":1748821947000},"page":"16381-16408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A CNN-based framework for land use land cover classification of heterogeneous terrain using satellite images"],"prefix":"10.1007","volume":"37","author":[{"given":"Anurina","family":"Tarafdar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asif Iqbal","family":"Middya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sounak","family":"Banerjee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunirmal","family":"Khatua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7598-8266","authenticated-orcid":false,"given":"Sarbani","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"issue":"1","key":"11314_CR1","first-page":"77","volume":"18","author":"J Rawat","year":"2015","unstructured":"Rawat J, Kumar M (2015) Monitoring land use\/cover change using remote sensing and gis techniques: a case study of hawalbagh block, district almora, Uttarakhand, India. Egypt J Remote Sens Space Sci 18(1):77\u201384","journal-title":"Egypt J Remote Sens Space Sci"},{"key":"11314_CR2","first-page":"165","volume":"11","author":"MA Kadhim","year":"2020","unstructured":"Kadhim MA, Abed MH (2020) Convolutional neural network for satellite image classification. Intell Inf Database Syst : Recent Dev 11:165\u2013178","journal-title":"Intell Inf Database Syst : Recent Dev"},{"issue":"10","key":"11314_CR3","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1109\/LGRS.2017.2731997","volume":"14","author":"G Cheng","year":"2017","unstructured":"Cheng G, Li Z, Yao X, Guo L, Wei Z (2017) Remote sensing image scene classification using bag of convolutional features. IEEE Geosci Remote Sens Lett 14(10):1735\u20131739","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"11314_CR4","unstructured":"O\u2019Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458"},{"key":"11314_CR5","doi-asserted-by":"crossref","unstructured":"Gharbia R, Khalifa NEM, Hassanien AE (2020) Land cover classification using deep convolutional neural networks. In: international conference on intelligent systems design and applications, pp. 911\u2013920. Springer","DOI":"10.1007\/978-3-030-71187-0_84"},{"key":"11314_CR6","doi-asserted-by":"publisher","first-page":"369","DOI":"10.5194\/isprs-archives-XLIII-B3-2021-369-2021","volume":"43","author":"H Yassine","year":"2021","unstructured":"Yassine H, Tout K, Jaber M (2021) Improving lulc classification from satellite imagery using deep learning-eurosat dataset. Int Arch Photogramm, Remote Sens Spat Inf Sci 43:369\u2013376","journal-title":"Int Arch Photogramm, Remote Sens Spat Inf Sci"},{"issue":"23","key":"11314_CR7","doi-asserted-by":"publisher","first-page":"8083","DOI":"10.3390\/s21238083","volume":"21","author":"R Naushad","year":"2021","unstructured":"Naushad R, Kaur T, Ghaderpour E (2021) Deep transfer learning for land use and land cover classification: a comparative study. Sensors 21(23):8083","journal-title":"Sensors"},{"issue":"3","key":"11314_CR8","doi-asserted-by":"publisher","first-page":"274","DOI":"10.3390\/rs11030274","volume":"11","author":"M Carranza-Garc\u00eda","year":"2019","unstructured":"Carranza-Garc\u00eda M, Garc\u00eda-Guti\u00e9rrez J, Riquelme JC (2019) A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sens 11(3):274","journal-title":"Remote Sens"},{"key":"11314_CR9","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","volume":"221","author":"C Zhang","year":"2019","unstructured":"Zhang C, Sargent I, Pan X, Li H, Gardiner A, Hare J, Atkinson PM (2019) Joint deep learning for land cover and land use classification. Remote Sens Environ 221:173\u2013187","journal-title":"Remote Sens Environ"},{"key":"11314_CR10","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.rse.2017.11.026","volume":"205","author":"R Goldblatt","year":"2018","unstructured":"Goldblatt R, Stuhlmacher MF, Tellman B, Clinton N, Hanson G, Georgescu M, Wang C, Serrano-Candela F, Khandelwal AK, Cheng W-H et al (2018) Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens Environ 205:253\u2013275","journal-title":"Remote Sens Environ"},{"issue":"7","key":"11314_CR11","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","volume":"12","author":"P Helber","year":"2019","unstructured":"Helber P, Bischke B, Dengel A, Borth D (2019) Eurosat: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE J Sel Top Appl Earth Obs Remote Sens 12(7):2217\u20132226","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"11314_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2021.101412","volume":"65","author":"J Jagannathan","year":"2021","unstructured":"Jagannathan J, Divya C (2021) Deep learning for the prediction and classification of land use and land cover changes using deep convolutional neural network. Ecol Inf 65:101412","journal-title":"Ecol Inf"},{"key":"11314_CR13","doi-asserted-by":"crossref","unstructured":"Dewangkoro H, Arymurthy AM (2021) Land use and land cover classification using cnn, svm, and channel squeeze & spatial excitation block. In: IOP conference series: earth and environmental science, 704, p. 012048. IOP Publishing","DOI":"10.1088\/1755-1315\/704\/1\/012048"},{"key":"11314_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2024.102808","volume":"83","author":"C Liu","year":"2024","unstructured":"Liu C, Yuan X, Ni G, Liu Y, Qi Y, Miao S (2024) Utilizing deep transfer learning to discover changes in landscape patterns in urban wetland parks based on multispectral remote sensing. Ecol Inf 83:102808","journal-title":"Ecol Inf"},{"issue":"3","key":"11314_CR15","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.52783\/jes.4008","volume":"20","author":"AV Nimavat","year":"2024","unstructured":"Nimavat AV, Makwana KR, Kandoriya KP, Vyas CA, Rathod KR (2024) A novel transfer learning based deep model for land classification. J Electr Syst 20(3):2089\u20132096","journal-title":"J Electr Syst"},{"issue":"2","key":"11314_CR16","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","volume":"1","author":"S Niu","year":"2020","unstructured":"Niu S, Liu Y, Wang J, Song H (2020) A decade survey of transfer learning (2010\u20132020). IEEE Trans Artif Intell 1(2):151\u2013166","journal-title":"IEEE Trans Artif Intell"},{"key":"11314_CR17","doi-asserted-by":"crossref","unstructured":"Yin X, Chen W, Wu X, Yue H (2017) Fine-tuning and visualization of convolutional neural networks. In: 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1310\u20131315. IEEE","DOI":"10.1109\/ICIEA.2017.8283041"},{"key":"11314_CR18","doi-asserted-by":"publisher","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","volume":"9","author":"H Alhichri","year":"2021","unstructured":"Alhichri H, Alswayed AS, Bazi Y, Ammour N, Alajlan NA (2021) Classification of remote sensing images using efficientnet-b3 cnn model with attention. IEEE Access 9:14078\u201314094","journal-title":"IEEE Access"},{"issue":"5","key":"11314_CR19","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1080\/15481603.2017.1323377","volume":"54","author":"X Yu","year":"2017","unstructured":"Yu X, Wu X, Luo C, Ren P (2017) Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GISci Remote Sens 54(5):741\u2013758","journal-title":"GISci Remote Sens"},{"key":"11314_CR20","doi-asserted-by":"publisher","first-page":"147","DOI":"10.5194\/isprs-archives-XLII-5-147-2018","volume":"42","author":"RS Bhowmick","year":"2018","unstructured":"Bhowmick RS, Kumar A, Singh GD, Kumar S (2018) Model for land cover estimation using unsupervised machine learning on google maps color images. Int Arch Photogramm, Remote Sens Spat Inf Sci 42:147\u2013154","journal-title":"Int Arch Photogramm, Remote Sens Spat Inf Sci"},{"key":"11314_CR21","unstructured":"Platform GM (2023) Maps Static API. https:\/\/developers.google.com\/maps\/documentation\/maps-static\/start. Accessed: 2023-11-30"},{"key":"11314_CR22","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary PhD Workshop (IIPhDW), pp. 117\u2013122. IEEE","DOI":"10.1109\/IIPHDW.2018.8388338"},{"issue":"1","key":"11314_CR23","first-page":"6973103","volume":"2018","author":"S Albawi","year":"2017","unstructured":"Albawi S, Bayat O, Al-Azawi S (2017) Ucan ON (2017) Social touch gesture recognition using convolutional neural network. Computational Intell Neurosci 2018(1):6973103","journal-title":"Computational Intell Neurosci"},{"key":"11314_CR24","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11314_CR25","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580"},{"key":"11314_CR26","unstructured":"Riva M (2023) Batch Normalization in Convolutional Neural Networks. Accessed: 2023-01-06. https:\/\/www.baeldung.com\/cs\/batch-normalization-cnn"},{"key":"11314_CR27","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"11314_CR28","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11314_CR29","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"11314_CR30","unstructured":"Phelber: EuroSAT: Land Use and Land Cover Classification with Sentinel-2. Accessed: 2024-03-06 (2023). https:\/\/github.com\/phelber\/eurosat"},{"key":"11314_CR31","unstructured":"Fazackerley C (2019) Distance Calculations using latitudes and longitudes. Accessed: 2023-02-10"},{"key":"11314_CR32","unstructured":"Quora: Calculating new longitude latitude. Accessed: 2024-02-10 (2017). https:\/\/stackoverflow.com\/questions\/7477003\/calculating-new-longitude-latitude-from-old-n-meters"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11314-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11314-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11314-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T16:34:14Z","timestamp":1757176454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11314-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,2]]},"references-count":32,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["11314"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11314-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,2]]},"assertion":[{"value":"26 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}