{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T11:37:29Z","timestamp":1781350649250,"version":"3.54.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"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":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s12145-024-01686-9","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:59:50Z","timestamp":1737939590000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Fuzzy Shuffled Frog Leaping Optimization-based enhanced ConvLSTM for Land Use\/ Land Cover Prediction"],"prefix":"10.1007","volume":"18","author":[{"given":"Sam Navin","family":"MohanRajan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agilandeeswari","family":"Loganthan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prabukumar","family":"Manoharan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"1686_CR1","doi-asserted-by":"publisher","unstructured":"Alshari EA, Bharti W, Gawali (2021) Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings 2.1 : 8\u201317. https:\/\/doi.org\/10.1016\/j.gltp.2021.01.002","DOI":"10.1016\/j.gltp.2021.01.002"},{"key":"1686_CR2","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1016\/j.procs.2020.03.331","volume":"167","author":"J Arora","year":"2020","unstructured":"Arora J, Tushir M (2020) An enhanced spatial intuitionistic fuzzy c-means clustering for image segmentation. Procedia Comput Sci 167:646\u2013655. https:\/\/doi.org\/10.1016\/j.procs.2020.03.331","journal-title":"Procedia Comput Sci"},{"key":"1686_CR3","doi-asserted-by":"publisher","unstructured":"Balha A et al (2021) A comparative analysis of different pixel and object-based classification algorithms using multi-source high spatial resolution satellite data for LULC mapping. Earth Sci Inf 1\u201317. https:\/\/doi.org\/10.1007\/s12145-021-00685-4","DOI":"10.1007\/s12145-021-00685-4"},{"key":"1686_CR4","doi-asserted-by":"publisher","unstructured":"Benbriqa H et al (2021) Deep and Ensemble Learning Based Land Use and Land Cover Classification. International Conference on Computational Science and Its Applications. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-86970-0_41","DOI":"10.1007\/978-3-030-86970-0_41"},{"issue":"3","key":"1686_CR5","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.1007\/s12145-024-01273-y","volume":"17","author":"SM Bhakthan","year":"2024","unstructured":"Bhakthan SM, Loganathan A (2024) A hyperspectral unmixing model using convolutional vision transformer. Earth Sci Inf 17(3):2255\u20132273. https:\/\/doi.org\/10.1007\/s12145-024-01273-y","journal-title":"Earth Sci Inf"},{"key":"1686_CR6","doi-asserted-by":"publisher","first-page":"102477","DOI":"10.1016\/j.jag.2021.102477","volume":"103","author":"P Dou","year":"2021","unstructured":"Dou P et al (2021) Time series remote sensing image classification framework using combination of deep learning and multiple classifiers system. Int J Appl Earth Obs Geoinf 103:102477. https:\/\/doi.org\/10.1016\/j.jag.2021.102477","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"1686_CR7","doi-asserted-by":"publisher","unstructured":"Gamboa-Villafruela C, Javier et al (2021) Convolutional LSTM Architecture for Precipitation Nowcasting Using Satellite Data. Environmental Sciences Proceedings 8.1 : 33. https:\/\/doi.org\/10.3390\/ecas2021-10340","DOI":"10.3390\/ecas2021-10340"},{"key":"1686_CR8","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-030-26458-1_7","volume-title":"Introduction to shuffled Frog leaping Algorithm and its sensitivity to the parameters of the Algorithm. Nature-inspired methods for Metaheuristics Optimization","author":"BG Gandhi","year":"2020","unstructured":"Gandhi BG, Rajeev, Bhattacharjya RK (2020) Introduction to shuffled Frog leaping Algorithm and its sensitivity to the parameters of the Algorithm. Nature-inspired methods for Metaheuristics Optimization. Springer, Cham, pp 105\u2013117. https:\/\/doi.org\/10.1007\/978-3-030-26458-1_7"},{"issue":"2","key":"1686_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00207233.2021.1997220","volume":"80","author":"N Gavrilovskaya","year":"2021","unstructured":"Gavrilovskaya N et al (2021) Advances in space-scale farming: UAV and satellite monitoring of wheat production in Krasnodar. Russian Federation Int J Environ Stud 80(2):1\u201314. https:\/\/doi.org\/10.1080\/00207233.2021.1997220","journal-title":"Russian Federation Int J Environ Stud"},{"issue":"1","key":"1686_CR10","doi-asserted-by":"publisher","first-page":"178","DOI":"10.37934\/arfmts.114.1.178187","volume":"114","author":"K Ghalehteimouri","year":"2024","unstructured":"Ghalehteimouri K, Jafarpour et al (2024) Spatial and temporal water pattern change detection through the normalized difference water index (NDWI) for initial flood assessment: a case study of Kuala Lumpur 1990 and 2021. J Adv Res Fluid Mech Therm Sci 114(1):178\u2013187. https:\/\/doi.org\/10.37934\/arfmts.114.1.178187","journal-title":"J Adv Res Fluid Mech Therm Sci"},{"issue":"2","key":"1686_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11334-021-00414-6","volume":"20","author":"T Halder","year":"2024","unstructured":"Halder T et al (2024) A hybrid approach for water body identification from satellite images using NDWI mapping and histogram of gradients. Innov Syst Softw Eng 20(2):1\u201310. https:\/\/doi.org\/10.1007\/s11334-021-00414-6","journal-title":"Innov Syst Softw Eng"},{"issue":"3","key":"1686_CR12","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.32604\/csse.2022.023221","volume":"42","author":"MA Haq","year":"2022","unstructured":"Haq MA (2022a) Planetscope nanosatellites image classification using machine learning. Comput Syst Sci Eng 42(3):1031\u20131046. https:\/\/doi.org\/10.32604\/csse.2022.023221","journal-title":"Comput Syst Sci Eng"},{"issue":"2","key":"1686_CR13","doi-asserted-by":"publisher","first-page":"2363","DOI":"10.32604\/cmc.2022.023059","volume":"71","author":"MA Haq","year":"2022","unstructured":"Haq MA (2022b) CDLSTM: a novel model for climate change forecasting. Computers. Mater Continua 71(2):2363-2381. https:\/\/doi.org\/10.32604\/cmc.2022.023059","journal-title":"Mater Continua"},{"key":"1686_CR14","doi-asserted-by":"publisher","unstructured":"Haq MA (2022c) SMOTEDNN: A novel model for air pollution forecasting and AQI classification. Computers, Materials & Continua 71.1 https:\/\/doi.org\/10.32604\/cmc.2022.021968","DOI":"10.32604\/cmc.2022.021968"},{"issue":"3","key":"1686_CR15","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/s12524-020-01231-3","volume":"49","author":"M Haq","year":"2021","unstructured":"Haq M, Anul et al (2021a) Deep learning based supervised image classification using UAV images for forest areas classification. J Indian Soc Remote Sens 49(3):601\u2013606. https:\/\/doi.org\/10.1007\/s12524-020-01231-3","journal-title":"J Indian Soc Remote Sens"},{"key":"1686_CR16","doi-asserted-by":"publisher","first-page":"4599","DOI":"10.32604\/cmc.2022.020495","volume":"70","author":"M Haq","year":"2021","unstructured":"Haq M, Anul AK, Jilani, Prabu P (2021b) Deep learning based modeling of groundwater storage change. CMC-Computers. Mater Continua 70:4599\u20134617. https:\/\/doi.org\/10.32604\/cmc.2022.020495","journal-title":"Mater Continua"},{"key":"1686_CR17","doi-asserted-by":"publisher","first-page":"100527","DOI":"10.1016\/j.envdev.2020.100527","volume":"34","author":"S Hasan","year":"2020","unstructured":"Hasan S, Shamim et al (2020) Impact of land use change on ecosystem services: a review. Environ Dev 34:100527. https:\/\/doi.org\/10.1016\/j.envdev.2020.100527","journal-title":"Environ Dev"},{"issue":"12","key":"1686_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/15481603.2021.1932126","volume":"58","author":"X Hu","year":"2021","unstructured":"Hu X et al (2021) Improving wetland cover classification using artificial neural networks with ensemble techniques. GIScience Remote Sens 58(12):1\u201321. https:\/\/doi.org\/10.1080\/15481603.2021.1932126","journal-title":"GIScience Remote Sens"},{"key":"1686_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.3028247","author":"O Kodheli","year":"2020","unstructured":"Kodheli O et al (2020) Satellite communications in the new space era: a survey and future challenges. IEEE Commun Surv Tutorials. https:\/\/doi.org\/10.1109\/COMST.2020.3028247","journal-title":"IEEE Commun Surv Tutorials"},{"key":"1686_CR20","doi-asserted-by":"publisher","unstructured":"Krishnaveni KS, Anilkumar PP (2021), July A Fully Automated Approach to Extract Landcover Features from Landsat Imageries. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 6669\u20136672). IEEE. https:\/\/doi.org\/10.1109\/IGARSS47720.2021.9554848","DOI":"10.1109\/IGARSS47720.2021.9554848"},{"key":"1686_CR21","doi-asserted-by":"publisher","unstructured":"Kuchkorov T et al (2020) Satellite image formation and preprocessing methods. 2020 International Conference on Information Science and Communications Technologies (ICISCT). IEEE. https:\/\/doi.org\/10.1109\/ICISCT50599.2020.9351456","DOI":"10.1109\/ICISCT50599.2020.9351456"},{"key":"1686_CR22","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/978-981-13-7166-0_23","volume-title":"Land Use Land Cover Change detection through GIS and unsupervised learning technique. Information and Communication Technology for Sustainable Development","author":"G Kulkarni","year":"2020","unstructured":"Kulkarni G et al (2020) Land Use Land Cover Change detection through GIS and unsupervised learning technique. Information and Communication Technology for Sustainable Development. Springer, Singapore, pp 239\u2013247. https:\/\/doi.org\/10.1007\/978-981-13-7166-0_23"},{"key":"1686_CR23","doi-asserted-by":"publisher","first-page":"8223","DOI":"10.1007\/s00500-023-09614-7","volume":"28","author":"A Kumar","year":"2024","unstructured":"Kumar A et al (2024) A new method for crop image segmentation based on 2D histogram using multi-strategy shuffled frog leaping algorithm. Soft Comput 28:8223\u20138246. https:\/\/doi.org\/10.1007\/s00500-023-09614-7","journal-title":"Soft Comput"},{"issue":"11","key":"1686_CR24","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1080\/10106049.2019.1641560","volume":"36","author":"E Kutlug Sahin","year":"2021","unstructured":"Kutlug Sahin E, Colkesen I (2021) Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping. Geocarto Int 36(11):1253\u20131275. https:\/\/doi.org\/10.1080\/10106049.2019.1641560","journal-title":"Geocarto Int"},{"key":"1686_CR25","doi-asserted-by":"publisher","unstructured":"Li J (2020) and Yasunori Endo. Fuzzy c-Means with Improved Particle Swarm Optimization. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE. https:\/\/doi.org\/10.1109\/FUZZ48607.2020.9177673","DOI":"10.1109\/FUZZ48607.2020.9177673"},{"key":"1686_CR26","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.inffus.2020.10.008","volume":"67","author":"Y Li","year":"2021","unstructured":"Li Y, Ma J, Zhang Y (2021) Image retrieval from remote sensing big data: a survey. Inform Fusion 67:94\u2013115. https:\/\/doi.org\/10.1016\/j.inffus.2020.10.008","journal-title":"Inform Fusion"},{"key":"1686_CR27","doi-asserted-by":"publisher","first-page":"3875","DOI":"10.3390\/rs16203875","volume":"16","author":"Z Liang","year":"2024","unstructured":"Liang Z, Ruochen Sun, and, Duan Q (2024) Attribution of Vegetation dynamics in the Yellow River Water Conservation Area Based on the Deep ConvLSTM Model. Remote Sens 16:3875. https:\/\/doi.org\/10.3390\/rs16203875","journal-title":"Remote Sens"},{"issue":"12","key":"1686_CR28","doi-asserted-by":"publisher","first-page":"1755","DOI":"10.3390\/w16121755","volume":"16","author":"S Liu","year":"2024","unstructured":"Liu S, Jun Qiu, and, Li F (2024) A remote sensing water information extraction method based on unsupervised form using probability function to describe the frequency Histogram of NDWI: a case study of Qinghai Lake in China. Water 16(12):1755. https:\/\/doi.org\/10.3390\/w16121755","journal-title":"Water"},{"key":"1686_CR29","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.egyr.2021.01.001","volume":"7","author":"Y Liu","year":"2021","unstructured":"Liu Y et al (2021) Evolutionary shuffled frog leaping with memory pool for parameter optimization. Energy Rep 7:584\u2013606. https:\/\/doi.org\/10.1016\/j.egyr.2021.01.001","journal-title":"Energy Rep"},{"issue":"3","key":"1686_CR30","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1016\/j.ejrs.2021.11.002","volume":"24","author":"S Mallick","year":"2021","unstructured":"Mallick S, Kumar, Rudra S (2021) Land use changes and its impact on biophysical environment: study on a river bank. Egypt J Remote Sens Space Sci 24(3):1037\u20131049. https:\/\/doi.org\/10.1016\/j.ejrs.2021.11.002","journal-title":"Egypt J Remote Sens Space Sci"},{"key":"1686_CR31","doi-asserted-by":"publisher","unstructured":"Mauro F et al (2023) SEN2DWATER: A Novel Multispectral and Multitemporal Dataset and Deep Learning Benchmark for Water Resources Analysis. IGARSS 2023\u20132023 IEEE International Geoscience and Remote Sensing Symposium. IEEE. https:\/\/doi.org\/10.1109\/IGARSS52108.2023.10282352","DOI":"10.1109\/IGARSS52108.2023.10282352"},{"key":"1686_CR32","doi-asserted-by":"publisher","unstructured":"Mishra S (2020) and Suraiya Jabin. Land Use Land Cover Change Detection using LANDSAT images: A Case Study. 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA). IEEE. https:\/\/doi.org\/10.1109\/ICCCA49541.2020.9250801","DOI":"10.1109\/ICCCA49541.2020.9250801"},{"key":"1686_CR33","doi-asserted-by":"publisher","first-page":"e3998","DOI":"10.1002\/ett.3998","volume":"32","author":"A Mohan","year":"2021","unstructured":"Mohan A et al (2021) Review on remote sensing methods for landslide detection using machine and deep learning. Trans Emerg Telecommunications Technol 32:e3998. https:\/\/doi.org\/10.1002\/ett.3998","journal-title":"Trans Emerg Telecommunications Technol"},{"issue":"4","key":"1686_CR34","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s12524-020-01258-6","volume":"49","author":"SN MohanRajan","year":"2021","unstructured":"MohanRajan SN, Loganathan A (2021) Modelling spatial drivers for LU\/LC change prediction using hybrid machine learning methods in Javadi Hills, Tamil Nadu, India. J Indian Soc Remote Sens 49(4):913\u2013934. https:\/\/doi.org\/10.1007\/s12524-020-01258-6","journal-title":"J Indian Soc Remote Sens"},{"key":"1686_CR35","doi-asserted-by":"publisher","first-page":"6387","DOI":"10.3390\/app12136387","volume":"12","author":"SN Mohanrajan","year":"2022","unstructured":"Mohanrajan SN, Loganathan A (2022) Novel vision transformer\u2013based bi-LSTM model for LU\/LC prediction\u2014Javadi Hills, India. Appl Sci 12:6387. https:\/\/doi.org\/10.3390\/app12136387","journal-title":"Appl Sci"},{"key":"1686_CR36","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1007\/s12517-023-11538-3","volume":"16","author":"SN MohanRajan","year":"2023","unstructured":"MohanRajan SN, Loganathan A (2023) A novel fuzzy Harris hawks optimization-based supervised vegetation and bare soil prediction system for Javadi Hills, India. Arab J Geosci 16:478. https:\/\/doi.org\/10.1007\/s12517-023-11538-3","journal-title":"Arab J Geosci"},{"key":"1686_CR37","doi-asserted-by":"publisher","first-page":"29900","DOI":"10.1007\/s11356-020-09091-7","volume":"27","author":"S MohanRajan","year":"2020","unstructured":"MohanRajan S, Navin A, Loganathan, Manoharan P (2020) Survey on Land Use\/Land Cover (LU\/LC) change analysis in remote sensing and GIS environment: techniques and challenges. Environ Sci Pollut Res 27:29900\u201329926. https:\/\/doi.org\/10.1007\/s11356-020-09091-7","journal-title":"Environ Sci Pollut Res"},{"issue":"2","key":"1686_CR38","doi-asserted-by":"publisher","first-page":"1745","DOI":"10.1007\/s12145-023-01208-z","volume":"17","author":"S MohanRajan","year":"2024","unstructured":"MohanRajan S, Navin et al (2024) Fuzzy swin transformer for land use\/land cover change detection using LISS-III Satellite data. Earth Sci Inf 17(2):1745\u20131764. https:\/\/doi.org\/10.1007\/s12145-023-01208-z","journal-title":"Earth Sci Inf"},{"issue":"39","key":"1686_CR39","doi-asserted-by":"publisher","first-page":"29751","DOI":"10.1007\/s11042-020-09531-z","volume":"79","author":"M Navin","year":"2020","unstructured":"Navin M, Sam (2020) Multispectral and hyperspectral images based land use\/land cover change prediction analysis: an extensive review. Multimedia Tools Appl 79(39):29751\u201329774. https:\/\/doi.org\/10.1007\/s11042-020-09531-z","journal-title":"Multimedia Tools Appl"},{"key":"1686_CR40","doi-asserted-by":"publisher","unstructured":"Navin M, Sam, Agilandeeswari L (2020) Comprehensive review on land use\/land cover change classification in remote sensing. J Spectr Imaging 9. https:\/\/doi.org\/10.1255\/jsi.2020.a8","DOI":"10.1255\/jsi.2020.a8"},{"key":"1686_CR41","doi-asserted-by":"publisher","unstructured":"Phan D, Cao et al (2021) Ensemble learning updating classifier for accurate land cover assessment in tropical cloudy areas. Geocarto Int 1\u201318. https:\/\/doi.org\/10.1080\/10106049.2021.1878292","DOI":"10.1080\/10106049.2021.1878292"},{"key":"1686_CR42","doi-asserted-by":"publisher","unstructured":"Phaneendra Kumar BLN et al (2024) A new band selection framework for hyperspectral remote sensing image classification. Sci Rep 14:31836. https:\/\/doi.org\/10.1038\/s41598-024-83118-8","DOI":"10.1038\/s41598-024-83118-8"},{"key":"1686_CR43","doi-asserted-by":"publisher","unstructured":"Reddy G, Kumar KC (2022) Machine learning algorithms for Optical Remote Sensing Data classification and analysis. Data Sci Agric Nat Resource Manage 195\u2013220. https:\/\/doi.org\/10.1007\/978-981-16-5847-1_10","DOI":"10.1007\/978-981-16-5847-1_10"},{"key":"1686_CR44","doi-asserted-by":"publisher","unstructured":"Ridding LE et al (2020) Modelling historical landscape changes. Landscape Ecology 35.12 : 2695\u20132712. https:\/\/doi.org\/10.1007\/s10980-020-01059-9","DOI":"10.1007\/s10980-020-01059-9"},{"key":"1686_CR45","doi-asserted-by":"publisher","unstructured":"Said M et al (2021) Predicting land use\/cover changes and its association to agricultural production on the slopes of Mount Kilimanjaro, Tanzania. Annals of GIS. 1\u201321. https:\/\/doi.org\/10.1080\/19475683.2020.1871406","DOI":"10.1080\/19475683.2020.1871406"},{"key":"1686_CR46","doi-asserted-by":"publisher","unstructured":"Sangeetha V, Agilandeeswari L (2025) Enhanced hyperspectral image analysis via 2D-3D CNN fusion and hybrid moth-flame optimization for optimal band selection in remote sensing. Earth Sci Inform 18(1). https:\/\/doi.org\/10.1007\/s12145-024-01527-9","DOI":"10.1007\/s12145-024-01527-9"},{"key":"1686_CR47","doi-asserted-by":"publisher","first-page":"103295","DOI":"10.1016\/j.infrared.2020.103295","volume":"107","author":"S Sawant","year":"2020","unstructured":"Sawant S, Manoharan P (2020) Hyperspectral band selection based on metaheuristic optimization approach. Infrared Phys Technol 107:103295. https:\/\/doi.org\/10.1016\/j.infrared.2020.103295","journal-title":"Infrared Phys Technol"},{"issue":"2","key":"1686_CR48","doi-asserted-by":"publisher","first-page":"220","DOI":"10.3390\/rs13020220","volume":"13","author":"S Seydi","year":"2021","unstructured":"Seydi S, Teymoor et al (2021) Wildfire damage assessment over Australia using sentinel-2 imagery and MODIS land cover product within the Google earth engine cloud platform. Remote Sens 13(2):220. https:\/\/doi.org\/10.3390\/rs13020220","journal-title":"Remote Sens"},{"issue":"4","key":"1686_CR49","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1080\/15481603.2020.1736857","volume":"57","author":"H Shafizadeh-Moghadam","year":"2020","unstructured":"Shafizadeh-Moghadam H et al (2020) Modeling the spatial variation of urban land surface temperature in relation to environmental and anthropogenic factors: a case study of Tehran. Iran GIScience Remote Sens 57(4):483\u2013496. https:\/\/doi.org\/10.1080\/15481603.2020.1736857","journal-title":"Iran GIScience Remote Sens"},{"issue":"6","key":"1686_CR50","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1007\/s10596-021-10094-7","volume":"25","author":"M Sharifipour","year":"2021","unstructured":"Sharifipour M et al (2021) Well placement optimization using shuffled frog leaping algorithm. Comput GeoSci 25(6):1939\u20131956. https:\/\/doi.org\/10.1007\/s10596-021-10094-7","journal-title":"Comput GeoSci"},{"issue":"6","key":"1686_CR51","doi-asserted-by":"publisher","first-page":"7423","DOI":"10.1007\/s40747-023-01129-w","volume":"9","author":"R Sharma","year":"2023","unstructured":"Sharma R, Ravinder M (2023) Remote sensing image segmentation using feature based fusion on FCM clustering algorithm. Complex Intell Syst 9(6):7423\u20137437. https:\/\/doi.org\/10.1007\/s40747-023-01129-w","journal-title":"Complex Intell Syst"},{"issue":"1","key":"1686_CR52","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s12145-019-00417-9","volume":"13","author":"S Sheoran","year":"2020","unstructured":"Sheoran S, Mittal N, Gelbukh A (2020) Analysis on application of swarm-based techniques in processing remote sensed data. Earth Sci Inf 13(1):97\u2013113. https:\/\/doi.org\/10.1007\/s12145-019-00417-9","journal-title":"Earth Sci Inf"},{"key":"1686_CR53","doi-asserted-by":"publisher","unstructured":"Sheoran S, Mittal N, Gelbukh A (2021) Improved change detection in Remote sensed images by Artificial Intelligence techniques. J Indian Soc Remote Sens 1\u201314. https:\/\/doi.org\/10.1007\/s12524-021-01374-x","DOI":"10.1007\/s12524-021-01374-x"},{"key":"1686_CR54","doi-asserted-by":"publisher","first-page":"100624","DOI":"10.1016\/j.rsase.2021.100624","volume":"24","author":"R Singh","year":"2021","unstructured":"Singh R, Kumar et al (2021) A machine learning-based classification of LANDSAT images to map land use and land cover of India. Remote Sens Applications: Soc Environ 24:100624. https:\/\/doi.org\/10.1016\/j.rsase.2021.100624","journal-title":"Remote Sens Applications: Soc Environ"},{"key":"1686_CR55","doi-asserted-by":"publisher","unstructured":"Su Z, Zhong Y (2010) Land classification based on high resolution remote sensing images. Journal of Physics: Conference Series. Vol. No. 1. IOP Publishing (2021): 012140. https:\/\/doi.org\/10.1088\/1742-6596\/2010\/1\/012140","DOI":"10.1088\/1742-6596\/2010\/1\/012140"},{"key":"1686_CR56","doi-asserted-by":"publisher","unstructured":"Subhahan D, Abdus, Vinoth Kumar CNS (2023) A Fuzzy logic Clustering Classification of Satellite Images Using K-Means Clustering. 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE. https:\/\/doi.org\/10.1109\/ICSES60034.2023.10465362","DOI":"10.1109\/ICSES60034.2023.10465362"},{"key":"1686_CR57","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","volume":"164","author":"H Tamiminia","year":"2020","unstructured":"Tamiminia H et al (2020) Google Earth Engine for geo-big data applications: a meta-analysis and systematic review. ISPRS J Photogrammetry Remote Sens 164:152\u2013170. https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.04.001","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"1686_CR58","doi-asserted-by":"publisher","unstructured":"Tao Y et al (2021) (2021) Real-time multipath mitigation in multi-GNSS short baseline positioning via CNN-LSTM method. Mathematical Problems in Engineering https:\/\/doi.org\/10.1155\/2021\/6573230","DOI":"10.1155\/2021\/6573230"},{"issue":"1","key":"1686_CR59","doi-asserted-by":"publisher","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 Inf 17(1):593\u2013616. https:\/\/doi.org\/10.1007\/s12145-023-01109-1","journal-title":"Earth Sci Inf"},{"key":"1686_CR60","doi-asserted-by":"publisher","unstructured":"Tejasree G, Agilandeeswari L (2024a) An extensive review of hyperspectral image classification and prediction: techniques and challenges. Multimedia Tools Appl 1\u201398. https:\/\/doi.org\/10.1007\/s11042-024-18562-9","DOI":"10.1007\/s11042-024-18562-9"},{"key":"1686_CR61","doi-asserted-by":"publisher","unstructured":"Tejasree G, Agilandeeswari L (2024b) Enhancing hyperspectral image classification for land use land cover with dilated neighbourhood attention transformer and crow search optimization. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3388457","DOI":"10.1109\/ACCESS.2024.3388457"},{"issue":"1","key":"1686_CR62","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ejrs.2024.01.004","volume":"27","author":"G Tejasree","year":"2024","unstructured":"Tejasree G, Agilandeeswari L (2024c) Land use\/land cover (LULC) classification using deep-LSTM for hyperspectral images. Egypt J Remote Sens Space Sci 27(1):52\u201368. https:\/\/doi.org\/10.1016\/j.ejrs.2024.01.004","journal-title":"Egypt J Remote Sens Space Sci"},{"issue":"2","key":"1686_CR63","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s11831-017-9239-y","volume":"26","author":"KK Thyagharajan","year":"2019","unstructured":"Thyagharajan KK, Vignesh T (2019) Soft computing techniques for land use and land cover monitoring with multispectral remote sensing images: a review. Arch Comput Methods Eng 26(2):275\u2013301. https:\/\/doi.org\/10.1007\/s11831-017-9239-y","journal-title":"Arch Comput Methods Eng"},{"issue":"4","key":"1686_CR64","doi-asserted-by":"publisher","first-page":"122","DOI":"10.3390\/a14040122","volume":"14","author":"F Valdez","year":"2021","unstructured":"Valdez F, Castillo O (2021) Bio-inspired algorithms and its applications for optimization. Fuzzy Clustering Algorithms 14(4):122. https:\/\/doi.org\/10.3390\/a14040122","journal-title":"Fuzzy Clustering Algorithms"},{"key":"1686_CR65","doi-asserted-by":"publisher","unstructured":"Vivekananda GN, Swathi R, Sujith AVLN (2021) Multi-temporal image analysis for LULC classification and change detection. European Journal of Remote Sensing 54.sup2 : 189\u2013199. https:\/\/doi.org\/10.1080\/22797254.2020.1771215","DOI":"10.1080\/22797254.2020.1771215"},{"key":"1686_CR66","doi-asserted-by":"publisher","first-page":"e0177666","DOI":"10.1371\/journal.pone.0177666","volume":"125","author":"X Wang","year":"2017","unstructured":"Wang X, Liu S, Liu Z (2017) Underwater sonar image detection: a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm. PLoS ONE 125:e0177666. https:\/\/doi.org\/10.1371\/journal.pone.0177666","journal-title":"PLoS ONE"},{"key":"1686_CR67","doi-asserted-by":"publisher","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","volume":"236","author":"M Weiss","year":"2020","unstructured":"Weiss M, Jacob Fr\u00e9d\u00e9ric, Duveiller G (2020) Remote sensing for agricultural applications: a meta-review. Remote Sens Environ 236:111402. https:\/\/doi.org\/10.1016\/j.rse.2019.111402","journal-title":"Remote Sens Environ"},{"key":"1686_CR68","doi-asserted-by":"publisher","first-page":"1470320","DOI":"10.3389\/fmars.2024.1470320","volume":"11","author":"J Yang","year":"2024","unstructured":"Yang J et al (2024) A ConvLSTM nearshore water level prediction model with integrated attention mechanism. Front Mar Sci 11:1470320. https:\/\/doi.org\/10.3389\/fmars.2024.1470320","journal-title":"Front Mar Sci"},{"key":"1686_CR69","doi-asserted-by":"publisher","unstructured":"Yashin JF, Mohammed et al (2020) Comparative Analysis of Classification Algorithms for Landuse\/Landcover Change Over A Part of The East Coast Region of Tamil Nadu And Its Environs. 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS). IEEE. https:\/\/doi.org\/10.1109\/InGARSS48198.2020.9358945","DOI":"10.1109\/InGARSS48198.2020.9358945"},{"key":"1686_CR70","doi-asserted-by":"publisher","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","volume":"241","author":"Q Yuan","year":"2020","unstructured":"Yuan Q et al (2020) Deep learning in environmental remote sensing: achievements and challenges. Remote Sens Environ 241:111716. https:\/\/doi.org\/10.1016\/j.rse.2020.111716","journal-title":"Remote Sens Environ"},{"key":"1686_CR71","doi-asserted-by":"publisher","unstructured":"Zeferino L, Bozzi et al (2020) Does environmental data increase the accuracy of land use and land cover classification? Int J Appl Earth Obs Geoinf 91:102128. https:\/\/doi.org\/10.1016\/j.jag.2020.102128","DOI":"10.1016\/j.jag.2020.102128"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01686-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01686-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01686-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T12:42:32Z","timestamp":1753792952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01686-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":71,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1686"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01686-9","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1038\/s41598-025-09247-w","asserted-by":"object"}]},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,27]]},"assertion":[{"value":"9 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"207"}}