{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:25:31Z","timestamp":1779294331704,"version":"3.51.4"},"reference-count":109,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"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":[[2025,11]]},"DOI":"10.1007\/s00521-025-11644-1","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T18:55:30Z","timestamp":1759172130000},"page":"26765-26822","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning framework for land cover and land use classification: five case studies with hyperspectral and RGB imagery"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2198-2870","authenticated-orcid":false,"given":"Bilal","family":"Arain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed M.","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibrahim","family":"Alrashdi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karam M.","family":"Sallam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed","family":"Abdel-Basset","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"issue":"5","key":"11644_CR1","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1007\/s11554-021-01166-z","volume":"18","author":"I Ahmed","year":"2021","unstructured":"Ahmed I, Ahmad M, Jeon G (2021) A real-time efficient object segmentation system based on U-Net using aerial drone images. J Real-Time Image Process 18(5):1745\u20131758","journal-title":"J Real-Time Image Process"},{"key":"11644_CR2","first-page":"1","volume":"66","author":"A Ahmed","year":"2024","unstructured":"Ahmed A, Harishnaika N (2024) The geospatial modelling of vegetation carbon storage analysis in Google Earth engine using machine learning techniques. Earth Sci Inf 66:1\u201314","journal-title":"Earth Sci Inf"},{"issue":"1","key":"11644_CR3","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TPAMI.2018.2873729","volume":"42","author":"N Akhtar","year":"2018","unstructured":"Akhtar N, Mian A (2018) Hyperspectral recovery from RGB images using Gaussian processes. IEEE Trans Pattern Anal Mach Intell 42(1):100\u2013113","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"11644_CR4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/2150704X.2023.2167057","volume":"14","author":"M\u00c7 Aksoy","year":"2023","unstructured":"Aksoy M\u00c7, Sirmacek B, \u00dcnsalan C (2023) Land classification in satellite images by injecting traditional features to CNN models. Remote Sens Lett 14(2):157\u2013167","journal-title":"Remote Sens Lett"},{"issue":"1","key":"11644_CR5","doi-asserted-by":"crossref","first-page":"2014192","DOI":"10.1080\/08839514.2021.2014192","volume":"36","author":"A Alem","year":"2022","unstructured":"Alem A, Kumar S (2022) Transfer learning models for land cover and land use classification in remote sensing image. Appl Artif Intell 36(1):2014192","journal-title":"Appl Artif Intell"},{"key":"11644_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3349285","author":"M Aljebreen","year":"2024","unstructured":"Aljebreen M, Mengash HA, Alamgeer M, Alotaibi SS, Salama AS, Hamza MA (2024) Land use and land cover classification using river formation dynamics algorithm with deep learning on remote sensing images. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2023.3349285","journal-title":"IEEE Access"},{"issue":"2","key":"11644_CR7","doi-asserted-by":"crossref","DOI":"10.3390\/rs15020316","volume":"15","author":"MQ Alkhatib","year":"2023","unstructured":"Alkhatib MQ et al (2023) Tri-cnn: a three branch model for hyperspectral image classification. Remote Sens 15(2):316","journal-title":"Remote Sens"},{"key":"11644_CR8","doi-asserted-by":"crossref","unstructured":"Amanatulla M, Swathi G, Pallavi M, Bindu KP (2025) MRI Scans for Deep Learning-Based Chronic Nephropathy Detection: A Comparison of CNN, MobileNet, VGG16, and ResNet-50 Models. In: 2024 5th International Conference for Emerging Technology (INCET). IEEE, pp 1\u20136","DOI":"10.1109\/INCET61516.2024.10593144"},{"key":"11644_CR9","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112074","volume":"165","author":"V Anitha","year":"2024","unstructured":"Anitha V, Manimegalai D, Kalaiselvi S (2024) Downstream lingering attention transformer network (DsLATNet) for land use land cover classification: a bicolor deep learning framework. Appl Soft Comput 165:112074","journal-title":"Appl Soft Comput"},{"key":"11644_CR10","doi-asserted-by":"crossref","unstructured":"Arad B, Ben-Shahar O (2016) Sparse recovery of hyperspectral signal from natural RGB images. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part VII 14, Springer, pp 19\u201334","DOI":"10.1007\/978-3-319-46478-7_2"},{"key":"11644_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.asr.2024.08.062","author":"A Arfa","year":"2024","unstructured":"Arfa A, Minaei M (2024) Utilizing multitemporal indices and spectral bands of Sentinel-2 to enhance land use and land cover classification with random forest and support vector machine. Adv Space Res. https:\/\/doi.org\/10.1016\/j.asr.2024.08.062","journal-title":"Adv Space Res"},{"key":"11644_CR12","doi-asserted-by":"publisher","DOI":"10.1002\/tqem.22254","author":"B Asmare","year":"2024","unstructured":"Asmare B, Neculina A, Wubie A, Egbe A, Charleine D, Ambo F (2024) The impact of land use and land cover change on the stream water quality in Limbe I municipality, Cameroon. Environ Qual Manage. https:\/\/doi.org\/10.1002\/tqem.22254","journal-title":"Environ Qual Manage"},{"key":"11644_CR13","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2023.102333","volume":"78","author":"A Azedou","year":"2023","unstructured":"Azedou A, Amine A, Kisekka I, Lahssini S, Bouziani Y, Moukrim S (2023) Enhancing land cover\/land use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN). Ecol Inform 78:102333","journal-title":"Ecol Inform"},{"key":"11644_CR14","first-page":"1","volume":"63","author":"A Azeem","year":"2025","unstructured":"Azeem A, Li Z, Siddique A, Zhang Y, Cao D (2025) Memory-augmented detection transformer for few-shot object detection in remote sensing imagery. IEEE Trans Geosci Remote Sens 63:1\u201321","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"18","key":"11644_CR15","doi-asserted-by":"crossref","first-page":"54001","DOI":"10.1007\/s11042-023-17612-y","volume":"83","author":"A Bhatt","year":"2024","unstructured":"Bhatt A, Bhatt VT (2024) Dcrff-Lhrf: an improvised methodology for efficient land-cover classification on Eurosat dataset. Multimed Tools Appl 83(18):54001\u201354025","journal-title":"Multimed Tools Appl"},{"key":"11644_CR16","doi-asserted-by":"crossref","unstructured":"Bidari I, Chickerur S, Kadam S (2023) Semantic segmentation using U-Net architecture for change detection on hyperspectral imagery. In: 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, pp 932\u2013937","DOI":"10.1109\/ICSCNA58489.2023.10370358"},{"key":"11644_CR17","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda M, Maki A, Mazurowski M (2018) A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw 106:249\u2013259","journal-title":"Neural Netw"},{"issue":"3","key":"11644_CR18","doi-asserted-by":"crossref","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":"11644_CR19","unstructured":"Catalano L (2024) A Transformer-based approach to air quality prediction in Milan through satellite imagery combined with meteorological and morphological data. PhD diss., Politecnico di Torino"},{"key":"11644_CR20","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2024.04.021","volume":"212","author":"X Che","year":"2024","unstructured":"Che X et al (2024) Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks. ISPRS J Photogramm Remote Sens 212:73\u201395","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"10","key":"11644_CR21","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","volume":"54","author":"Y Chen","year":"2016","unstructured":"Chen Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232\u20136251","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11644_CR22","unstructured":"Chen L, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587"},{"key":"11644_CR23","doi-asserted-by":"crossref","unstructured":"Cho K, Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2020) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 2014. arXiv preprint arXiv:1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"11644_CR24","doi-asserted-by":"crossref","DOI":"10.1016\/j.envc.2023.100800","volume":"14","author":"MS Chowdhury","year":"2024","unstructured":"Chowdhury MS (2024) Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use\/cover classification of urban setting. Environmental Challenges 14:100800","journal-title":"Environmental Challenges"},{"key":"11644_CR25","unstructured":"Clevert D, Unterthiner T, Hochreiter S (2020) Fast and accurate deep network learning by exponential linear units (ELUs). arXiv 2015. arXiv preprint arXiv:1511.07289"},{"key":"11644_CR26","doi-asserted-by":"crossref","DOI":"10.1016\/j.pce.2024.103559","volume":"134","author":"DN Cloete","year":"2024","unstructured":"Cloete DN, Shoko C, Dube T, Clarke S (2024) Remote sensing-based land use land cover classification for the Heuningnes Catchment, Cape Agulhas, South Africa. Phys Chem Earth, Parts A\/B\/C 134:103559","journal-title":"Phys Chem Earth, Parts A\/B\/C"},{"key":"11644_CR27","doi-asserted-by":"crossref","unstructured":"Cui Y, Jia M, Lin T, Song Y, Belongie S (2019) Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9268\u20139277","DOI":"10.1109\/CVPR.2019.00949"},{"issue":"10","key":"11644_CR28","doi-asserted-by":"crossref","first-page":"7854","DOI":"10.3390\/su15107854","volume":"15","author":"H Dastour","year":"2023","unstructured":"Dastour H, Hassan Q (2023) A comparison of deep transfer learning methods for land use and land cover classification. Sustainability 15(10):7854","journal-title":"Sustainability"},{"issue":"4","key":"11644_CR29","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1109\/LGRS.2004.837009","volume":"1","author":"F Dell\u2019Acqua","year":"2004","unstructured":"Dell\u2019Acqua F, Gamba P, Ferrari A, Palmason J, Benediktsson J, \u00c1rnason K (2004) Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geosci Remote Sens Lett 1(4):322\u2013326","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"11644_CR30","volume":"79","author":"T Do","year":"2024","unstructured":"Do T, Tran H, Do A (2024) Classifying forest cover and mapping forest fire susceptibility in Dak Nong province, Vietnam utilizing remote sensing and machine learning. Ecol Inf 79:102392","journal-title":"Ecol Inf"},{"issue":"1","key":"11644_CR31","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s11760-023-02701-0","volume":"18","author":"PA Ebenezer","year":"2024","unstructured":"Ebenezer PA, Manohar S (2024) Land use\/land cover change classification and prediction using deep learning approaches. Signal Image Video Process 18(1):223\u2013232","journal-title":"Signal Image Video Process"},{"issue":"17","key":"11644_CR32","doi-asserted-by":"crossref","first-page":"26451","DOI":"10.1007\/s11042-021-10783-6","volume":"80","author":"M Elpeltagy","year":"2021","unstructured":"Elpeltagy M, Sallam H (2021) Automatic prediction of COVID\u2212 19 from chest images using modified ResNet50. Multimed Tools Appl 80(17):26451\u201326463","journal-title":"Multimed Tools Appl"},{"issue":"9","key":"11644_CR33","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.3390\/f14091881","volume":"14","author":"X Fan","year":"2023","unstructured":"Fan X, Chen L, Xu X, Yan C, Fan J, Li X (2023) Land cover classification of remote sensing images based on hierarchical convolutional recurrent neural network. Forests 14(9):1881","journal-title":"Forests"},{"key":"11644_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112611","author":"M Fayaz","year":"2024","unstructured":"Fayaz M, Dang LM, Moon H (2024) Enhancing land cover classification via deep ensemble network. Knowl-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2024.112611","journal-title":"Knowl-Based Syst"},{"key":"11644_CR35","first-page":"66","volume":"29","author":"Y Gal","year":"2016","unstructured":"Gal Y, Ghahramani Z (2016) A theoretically grounded application of dropout in recurrent neural networks. Adv Neural Inf Process Syst 29:66","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"11644_CR36","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","volume":"65","author":"R Green","year":"1998","unstructured":"Green R, Eastwood M, Sarture C, Chrien T, Aronsson M, Chippendale B, Faust J (1998) Imaging spectroscopy and the airborne visible\/infrared imaging spectrometer (AVIRIS). Remote Sens Environ 65(3):227\u2013248","journal-title":"Remote Sens Environ"},{"issue":"2","key":"11644_CR37","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1109\/TGRS.2015.2465899","volume":"54","author":"R Hang","year":"2015","unstructured":"Hang R, Liu Q, Song H, Sun Y (2015) Matrix-based discriminant subspace ensemble for hyperspectral image spatial\u2013spectral feature fusion. IEEE Trans Geosci Remote Sens 54(2):783\u2013794","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11644_CR38","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"11644_CR39","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":"11644_CR40","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14. Springer International Publishing, pp 630\u2013645","DOI":"10.1007\/978-3-319-46493-0_38"},{"issue":"7","key":"11644_CR41","doi-asserted-by":"crossref","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 Observ Remote Sens 12(7):2217\u20132226","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"7","key":"11644_CR42","doi-asserted-by":"crossref","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":"11644_CR43","unstructured":"Hendrycks D, Gimpel K (2016) A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136"},{"key":"11644_CR44","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.inffus.2022.06.003","volume":"86","author":"Y Himeur","year":"2022","unstructured":"Himeur Y, Rimal B, Tiwary A, Amira A (2022) Using artificial intelligence and data fusion for environmental monitoring: a review and future perspectives. Inf Fusion 86:44\u201375","journal-title":"Inf Fusion"},{"key":"11644_CR45","doi-asserted-by":"publisher","unstructured":"Hochreiter S, Schmidhuber J (1997) Long Short-term Memory. Neural Computation. MIT-Press https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"issue":"8","key":"11644_CR46","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"G Houdt","year":"2020","unstructured":"Houdt G, Mosquera C, N\u00e1poles G (2020) A review on the long short-term memory model. Artif Intell Rev 53(8):5929\u20135955","journal-title":"Artif Intell Rev"},{"key":"11644_CR47","unstructured":"Howard A, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications (2017). arXiv preprint arXiv:1704.04861 126"},{"key":"11644_CR48","doi-asserted-by":"crossref","first-page":"179028","DOI":"10.1109\/ACCESS.2020.3027979","volume":"8","author":"I Ihianle","year":"2020","unstructured":"Ihianle I, Nwajana A, Ebenuwa S, Otuka R, Owa K, Orisatoki M (2020) A deep learning approach for human activities recognition from multimodal sensing devices. IEEE Access 8:179028\u2013179038","journal-title":"IEEE Access"},{"key":"11644_CR49","doi-asserted-by":"crossref","unstructured":"Irfan A, Sun G, Li Y, Zhang H (2024) Cascaded Deep Learning Model for Accurate Land Use and Land Cover Classification. In: IGARSS 2024\u20132024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp 3077\u20133080","DOI":"10.1109\/IGARSS53475.2024.10641891"},{"key":"11644_CR50","doi-asserted-by":"crossref","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 Inform 65:101412","journal-title":"Ecol Inform"},{"key":"11644_CR51","doi-asserted-by":"crossref","unstructured":"Karakus O, Ma W, Rosin P (2024) Land Cover Classification Using Attention-Based Multi-Modal Image Fusion: An Explainable Analysis. In: Signal and Image Processing for Remote Sensing. CRC Press, pp 309\u2013337","DOI":"10.1201\/9781003382010-20"},{"issue":"11","key":"11644_CR52","doi-asserted-by":"crossref","first-page":"4663","DOI":"10.1016\/j.asr.2023.08.057","volume":"72","author":"A Kumar","year":"2023","unstructured":"Kumar A, Gorai AK (2023) A comparative evaluation of deep convolutional neural network and deep neural network-based land use\/land cover classifications of mining regions using fused multi-sensor satellite data. Adv Space Res 72(11):4663\u20134676","journal-title":"Adv Space Res"},{"key":"11644_CR53","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1016\/j.procs.2023.01.124","volume":"218","author":"N Kumari","year":"2023","unstructured":"Kumari N, Minz S (2023) Deep residual SVM: a hybrid learning approach to obtain high discriminative feature for land use and land cover classification. Procedia Comput Sci 218:1454\u20131462","journal-title":"Procedia Comput Sci"},{"issue":"1","key":"11644_CR54","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","volume":"19","author":"D Landgrebe","year":"2002","unstructured":"Landgrebe D (2002) Hyperspectral image data analysis. IEEE Signal Process Mag 19(1):17\u201328","journal-title":"IEEE Signal Process Mag"},{"issue":"12","key":"11644_CR55","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.3390\/rs9121330","volume":"9","author":"Q Liu","year":"2017","unstructured":"Liu Q, Zhou F, Hang R, Yuan X (2017) Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification. Remote Sens 9(12):1330","journal-title":"Remote Sens"},{"key":"11644_CR56","unstructured":"Luo C, He X, Zhan J, Wang L, Gao W, Dai J (2020) Comparison and benchmarking of AI models and frameworks on mobile devices. arXiv preprint arXiv:2005.05085"},{"key":"11644_CR57","unstructured":"Maas A, Hannun A, Ng A (2013) Rectifier nonlinearities improve neural network acoustic models. In: Proc. ICML, vol 30, no. 1, p 3"},{"issue":"3","key":"11644_CR58","doi-asserted-by":"crossref","first-page":"330","DOI":"10.3390\/rs1030330","volume":"1","author":"R Manandhar","year":"2009","unstructured":"Manandhar R, Odeh IOA, Ancev T (2009) Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement. Remote Sens 1(3):330\u2013344","journal-title":"Remote Sens"},{"key":"11644_CR59","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2023.105931","volume":"172","author":"L Martinez-Sanchez","year":"2024","unstructured":"Martinez-Sanchez L et al (2024) Automatic classification of land cover from LUCAS in-situ landscape photos using semantic segmentation and a random forest model. Environ Model Softw 172:105931","journal-title":"Environ Model Softw"},{"key":"11644_CR60","first-page":"1","volume":"65","author":"S Mei","year":"2022","unstructured":"Mei S, Geng Y, Hou J, Du Q (2022) Learning hyperspectral images from RGB images via a coarse-to-fine CNN. Sci China Inf Sci 65:1\u201314","journal-title":"Sci China Inf Sci"},{"key":"11644_CR61","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neucom.2023.03.025","volume":"536","author":"M Moharram","year":"2023","unstructured":"Moharram M, Sundaram D (2023) Land use and land cover classification with hyperspectral data: a comprehensive review of methods, challenges and future directions. Neurocomputing 536:90\u2013113","journal-title":"Neurocomputing"},{"issue":"7","key":"11644_CR62","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","volume":"55","author":"L Mou","year":"2017","unstructured":"Mou L, Ghamisi P, Zhu XX (2017) Deep recurrent neural networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(7):3639\u20133655","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11644_CR63","doi-asserted-by":"crossref","first-page":"1431645","DOI":"10.3389\/fenvs.2024.1431645","volume":"12","author":"B Mutale","year":"2024","unstructured":"Mutale B, Withanage N, Mishra P, Shen J, Abdelrahman K, Fnais M (2024) A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from Lusaka and Colombo. Front Environ Sci 12:1431645","journal-title":"Front Environ Sci"},{"issue":"24","key":"11644_CR64","volume":"14","author":"HAH Naji","year":"2022","unstructured":"Naji HAH, Li T, Xue Q, Duan X (2022) A hypered deep-learning-based model of hyperspectral images generation and classification for imbalanced data. Remote Sens 14(24):6406","journal-title":"Remote Sens"},{"issue":"2","key":"11644_CR65","first-page":"131","volume":"6","author":"B Odoh","year":"2024","unstructured":"Odoh B, Nwokeabia C (2024) Impact of land use and land cover changes on groundwater dynamics in selected local government areas of Anambra State, Nigeria. Int J Earth Sci Knowl Appl 6(2):131\u2013142","journal-title":"Int J Earth Sci Knowl Appl"},{"issue":"1","key":"11644_CR66","doi-asserted-by":"crossref","DOI":"10.1080\/19475705.2023.2290350","volume":"15","author":"CB Pande","year":"2024","unstructured":"Pande CB et al (2024) Impact of land use\/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling. Geomat Nat Hazards Risk 15(1):2290350","journal-title":"Geomat Nat Hazards Risk"},{"issue":"1","key":"11644_CR67","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1186\/s12302-024-00901-0","volume":"36","author":"CB Pande","year":"2024","unstructured":"Pande CB et al (2024) Characterizing land use\/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation. Environ Sci Eur 36(1):84","journal-title":"Environ Sci Eur"},{"issue":"1","key":"11644_CR68","doi-asserted-by":"crossref","first-page":"2290350","DOI":"10.1080\/19475705.2023.2290350","volume":"15","author":"C Pande","year":"2024","unstructured":"Pande C, Diwate P, Orimoloye I, Sidek L, Mishra A, Moharir K, Pal S, Alshehri F, Tolche A (2024) Impact of land use\/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling. Geomat Nat Hazards Risk 15(1):2290350","journal-title":"Geomat Nat Hazards Risk"},{"issue":"1","key":"11644_CR69","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s10661-023-12131-7","volume":"196","author":"D Parashar","year":"2024","unstructured":"Parashar D, Kumar A, Palni S, Pandey A, Singh A, Singh AP (2024) Use of machine learning-based classification algorithms in the monitoring of land use and land cover practices in a hilly terrain. Environ Monit Assess 196(1):8","journal-title":"Environ Monit Assess"},{"issue":"4","key":"11644_CR70","doi-asserted-by":"crossref","first-page":"654","DOI":"10.3390\/agronomy11040654","volume":"11","author":"E Portal\u00e9s-Juli\u00e0","year":"2021","unstructured":"Portal\u00e9s-Juli\u00e0 E, Campos-Taberner M, Garc\u00eda-Haro F, Gilabert M (2021) Assessing the sentinel-2 capabilities to identify abandoned crops using deep learning. Agronomy 11(4):654","journal-title":"Agronomy"},{"issue":"14","key":"11644_CR71","doi-asserted-by":"crossref","first-page":"6089","DOI":"10.3390\/su16146089","volume":"16","author":"K Psistaki","year":"2024","unstructured":"Psistaki K, Tsantopoulos G, Paschalidou A (2024) An overview of the role of forests in climate change mitigation. Sustainability 16(14):6089","journal-title":"Sustainability"},{"key":"11644_CR72","first-page":"1","volume":"66","author":"A Putty","year":"2025","unstructured":"Putty A, Annappa B, Perumal S (2025) Semantic segmentation of remotely sensed images for land-use and land-cover classification: a comprehensive review. IETE Tech Rev 66:1\u201316","journal-title":"IETE Tech Rev"},{"issue":"4","key":"11644_CR73","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.11591\/ijai.v11.i4.pp1344-1352","volume":"11","author":"N Rachburee","year":"2022","unstructured":"Rachburee N, Punlumjeak W (2022) Lotus species classification using transfer learning based on VGG16, ResNet152V2, and MobileNetV2. IAES Int J Artif Intell (IJ-AI) 11(4):1344","journal-title":"IAES Int J Artif Intell (IJ-AI)"},{"key":"11644_CR74","unstructured":"Rasti B, Hong D, Hang R, Ghamisi P, Kang X, Chanussot J, Benediktsson J (2020) Feature Extraction for Hyperspectral Imagery. IEEE Geoscience and Remote Sensing Magazine, December 2020"},{"key":"11644_CR75","doi-asserted-by":"crossref","unstructured":"Rubab S et al (2024) A novel network level fusion architecture of proposed self-attention and vision transformer models for land use and land cover classification from remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens","DOI":"10.1109\/JSTARS.2024.3426950"},{"key":"11644_CR76","unstructured":"Rustowicz R, Cheong R, Wang L, Ermon S, Burke M, Lobell D (2019) Semantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods. In: Proceedings of the IEEE\/cvf conference on computer vision and pattern recognition workshops, pp 75\u201382"},{"key":"11644_CR77","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm M, Korner M (2017) Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 11\u201319","DOI":"10.1109\/CVPRW.2017.193"},{"issue":"04","key":"11644_CR78","doi-asserted-by":"crossref","DOI":"10.4236\/ijg.2017.84033","volume":"8","author":"SS Rwanga","year":"2017","unstructured":"Rwanga SS, Ndambuki JM (2017) Accuracy assessment of land use\/land cover classification using remote sensing and GIS. Int J Geosci 8(04):611","journal-title":"Int J Geosci"},{"key":"11644_CR79","doi-asserted-by":"crossref","unstructured":"Saini R, Singh S (2024) Land Use Land Cover Classification Using Machine Learning and Remote Sensing Data: A Case Study of Karnaprayag, Uttarakhand, India. In: 2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP). IEEE, pp 1\u20136","DOI":"10.1109\/ICECSP61809.2024.10698531"},{"issue":"11","key":"11644_CR80","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal K (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673\u20132681","journal-title":"IEEE Trans Signal Process"},{"key":"11644_CR81","first-page":"66","volume":"28","author":"X Shi","year":"2015","unstructured":"Shi X, Chen Z, Wang H, Yeung D, Wong W, Woo W (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Adv Neural Inf Process Syst 28:66","journal-title":"Adv Neural Inf Process Syst"},{"key":"11644_CR82","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"issue":"3","key":"11644_CR83","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1007\/s12040-024-02345-9","volume":"133","author":"LS Sunil","year":"2024","unstructured":"Sunil LS, Abraham MT, Satyam N (2024) Mapping built-up area expansion in landslide susceptible zones using automatic land use\/land cover classification. J Earth Syst Sci 133(3):132","journal-title":"J Earth Syst Sci"},{"key":"11644_CR84","doi-asserted-by":"crossref","unstructured":"Suryawanshi V, Adivarekar S, Bajaj K, Badami R (2023) Comparative Study of Regularization Techniques for VGG16, VGG19 and ResNet-50 for Plant Disease Detection. In: International Conference on Communication and Computational Technologies. Springer, pp 771\u2013781","DOI":"10.1007\/978-981-99-3485-0_61"},{"issue":"7","key":"11644_CR85","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.3390\/rs12071135","volume":"12","author":"S Talukdar","year":"2020","unstructured":"Talukdar S, Singha P, Mahato S, Pal S, Liou Y-A, Rahman A (2020) Land-use land-cover classification by machine learning classifiers for satellite observations\u2014a review. Remote Sens 12(7):1135","journal-title":"Remote Sens"},{"issue":"1","key":"11644_CR86","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s12145-023-01109-1","volume":"17","author":"G Tejasree","year":"2024","unstructured":"Tejasree G (2024) A novel multi-class land use\/land cover classification using deep kernel attention transformer for hyperspectral images. Earth Sci Inform 17(1):593\u2013616","journal-title":"Earth Sci Inform"},{"issue":"1","key":"11644_CR87","first-page":"52","volume":"27","author":"G Tejasree","year":"2024","unstructured":"Tejasree G, Agilandeeswari L (2024) Land use\/land cover (LULC) classification using deep-LSTM for hyperspectral images. Egypt J Remote Sens Space Sci 27(1):52\u201368","journal-title":"Egypt J Remote Sens Space Sci"},{"key":"11644_CR88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3251652","volume":"20","author":"A Temenos","year":"2023","unstructured":"Temenos A, Temenos N, Kaselimi M, Doulamis A, Doulamis N (2023) Interpretable deep learning framework for land use and land cover classification in remote sensing using SHAP. IEEE Geosci Remote Sens Lett 20:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"11644_CR89","first-page":"1","volume":"62","author":"R Tobar-D\u00edaz","year":"2023","unstructured":"Tobar-D\u00edaz R, Gao Y, Mas J, Cambr\u00f3n-Sandoval V (2023) Classification of land use and land cover through machine learning algorithms: a literature review. Rev Teledeteccion 62:1\u201319","journal-title":"Rev Teledeteccion"},{"key":"11644_CR90","doi-asserted-by":"crossref","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4489\u20134497","DOI":"10.1109\/ICCV.2015.510"},{"issue":"15","key":"11644_CR91","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.3390\/rs12152495","volume":"12","author":"A Vali","year":"2020","unstructured":"Vali A, Comai S, 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(15):2495","journal-title":"Remote Sens"},{"issue":"15","key":"11644_CR92","doi-asserted-by":"crossref","DOI":"10.3390\/rs12152495","volume":"12","author":"A Vali","year":"2020","unstructured":"Vali A, Comai S, 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(15):2495","journal-title":"Remote Sens"},{"issue":"6","key":"11644_CR93","doi-asserted-by":"crossref","first-page":"4802","DOI":"10.1109\/TGRS.2020.3012276","volume":"59","author":"Q Wu","year":"2020","unstructured":"Wu Q, Hou B, Wen Z, Ren Z, Jiao L (2020) Cost-sensitive latent space learning for imbalanced PolSAR image classification. IEEE Trans Geosci Remote Sens 59(6):4802\u20134817","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11644_CR94","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.isprsjprs.2023.12.005","volume":"207","author":"Y Xu","year":"2024","unstructured":"Xu Y, Ma Y, Zhang Z (2024) Self-supervised pre-training for large-scale crop mapping using Sentinel-2 time series. ISPRS J Photogramm Remote Sens 207:312\u2013325","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"11644_CR95","volume":"157","author":"Z Xue","year":"2025","unstructured":"Xue Z et al (2025) Multimodal self-supervised learning for remote sensing data land cover classification. Pattern Recogn 157:110959","journal-title":"Pattern Recogn"},{"key":"11644_CR96","doi-asserted-by":"crossref","first-page":"179516","DOI":"10.1109\/ACCESS.2020.3028030","volume":"8","author":"SA Yamashkin","year":"2020","unstructured":"Yamashkin SA, Yamashkin AA, Zanozin VV, Radovanovic MM, Barmin AN (2020) Improving the efficiency of deep learning methods in remote sensing data analysis: geosystem approach. IEEE Access 8:179516\u2013179529","journal-title":"IEEE Access"},{"issue":"9","key":"11644_CR97","doi-asserted-by":"crossref","first-page":"5408","DOI":"10.1109\/TGRS.2018.2815613","volume":"56","author":"X Yang","year":"2018","unstructured":"Yang X, Ye Y, Li X, Lau R, Zhang X, Huang X (2018) Hyperspectral image classification with deep learning models. IEEE Trans Geosci Remote Sens 56(9):5408\u20135423","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11644_CR98","doi-asserted-by":"crossref","unstructured":"Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 270\u2013279","DOI":"10.1145\/1869790.1869829"},{"key":"11644_CR99","doi-asserted-by":"crossref","first-page":"6916","DOI":"10.1109\/JSTARS.2021.3090085","volume":"14","author":"M Ye","year":"2021","unstructured":"Ye M, Ruiwen N, Chang Z, He G, Tianli H, Shijun L, Yu S, Tong Z, Ying G (2021) A lightweight model of VGG-16 for remote sensing image classification. IEEE J Sel Top Appl Earth Observ Remote Sens 14:6916\u20136922","journal-title":"IEEE J Sel Top Appl Earth Observ Remote Sens"},{"issue":"2","key":"11644_CR100","first-page":"216","volume":"27","author":"Z Zafar","year":"2024","unstructured":"Zafar Z, Zubair M, Zha Y, Fahd S, Nadeem AA (2024) Performance assessment of machine learning algorithms for mapping of land use\/land cover using remote sensing data. Egypt J Remote Sens Space Sci 27(2):216\u2013226","journal-title":"Egypt J Remote Sens Space Sci"},{"key":"11644_CR101","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","volume":"221","author":"C Zhang","year":"2019","unstructured":"Zhang C et al (2019) Joint deep learning for land cover and land use classification. Remote Sens Environ 221:173\u2013187","journal-title":"Remote Sens Environ"},{"issue":"5","key":"11644_CR102","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.3390\/ijerph19052567","volume":"19","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Liu M, Kong L, Peng T, Xie D, Zhang L, Tian L, Zou X (2022) Temporal characteristics of stress signals using GRU algorithm for heavy metal detection in rice based on Sentinel-2 images. Int J Environ Res Public Health 19(5):2567","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"11644_CR103","doi-asserted-by":"crossref","first-page":"11905","DOI":"10.1038\/s41598-022-16223-1","volume":"12","author":"J Zhang","year":"2022","unstructured":"Zhang J, Su R, Fu Q, Ren W, Heide F, Nie Y (2022) A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging. Sci Rep 12(1):11905","journal-title":"Sci Rep"},{"issue":"24","key":"11644_CR104","doi-asserted-by":"crossref","first-page":"6298","DOI":"10.3390\/rs14246298","volume":"14","author":"J Zhang","year":"2022","unstructured":"Zhang J, Xu S, Sun J, Ou D, Wu X, Wang M (2022) Unsupervised adversarial domain adaptation for agricultural land extraction of remote sensing images. Remote Sens 14(24):6298","journal-title":"Remote Sens"},{"key":"11644_CR105","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rama.2023.10.007","volume":"92","author":"Z Zhao","year":"2024","unstructured":"Zhao Z et al (2024) Comparison of three machine learning algorithms using Google Earth Engine for land use land cover classification. Rangel Ecol Manage 92:129\u2013137","journal-title":"Rangel Ecol Manage"},{"key":"11644_CR106","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rama.2023.10.007","volume":"92","author":"Z Zhao","year":"2024","unstructured":"Zhao Z, Islam F, Waseem L, Tariq A, Nawaz M, Islam I, Bibi T (2024) Comparison of three machine learning algorithms using Google Earth engine for land use land cover classification. Rangel Ecol Manag 92:129\u2013137","journal-title":"Rangel Ecol Manag"},{"issue":"21","key":"11644_CR107","doi-asserted-by":"crossref","first-page":"8966","DOI":"10.3390\/s23218966","volume":"23","author":"S Zhao","year":"2023","unstructured":"Zhao S, Tu K, Ye S, Tang H, Hu Y, Xie C (2023) Land use and land cover classification meets deep learning: a review. Sensors 23(21):8966","journal-title":"Sensors"},{"issue":"2","key":"11644_CR108","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TGRS.2014.2333539","volume":"53","author":"Y Zhou","year":"2014","unstructured":"Zhou Y, Peng J, Chen CLP (2014) Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification. IEEE Trans Geosci Remote Sens 53(2):1082\u20131095","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"21","key":"11644_CR109","volume":"14","author":"J Zhou","year":"2022","unstructured":"Zhou J, Zeng S, Xiao Z, Zhou J, Li H, Kang Z (2022) An enhanced spectral fusion 3D CNN model for hyperspectral image classification. Remote Sens 14(21):5334","journal-title":"Remote Sens"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11644-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11644-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11644-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T20:16:42Z","timestamp":1761077802000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11644-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":109,"journal-issue":{"issue":"32","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["11644"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11644-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,29]]},"assertion":[{"value":"21 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 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":"None.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}