Vote for your favorite mineral in #MinCup25! - Cuprosklodowskite vs. Ikaite
It's radioactive #cuprosklodowskite vs ephemeral #ikaite for today's match.
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Kumar, Ajay, Gorai, Amit Kumar (2023) Design of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region. Computers & Geosciences, 170. 105251 doi:10.1016/j.cageo.2022.105251

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleDesign of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region
JournalComputers & Geosciences
AuthorsKumar, AjayAuthor
Gorai, Amit KumarAuthor
Year2023 (January)Volume170
PublisherElsevier BV
DOIdoi:10.1016/j.cageo.2022.105251Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID15511163Long-form Identifiermindat:1:5:15511163:1
GUID0
Full ReferenceKumar, Ajay, Gorai, Amit Kumar (2023) Design of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region. Computers & Geosciences, 170. 105251 doi:10.1016/j.cageo.2022.105251
Plain TextKumar, Ajay, Gorai, Amit Kumar (2023) Design of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region. Computers & Geosciences, 170. 105251 doi:10.1016/j.cageo.2022.105251
In(2023) Computers & Geosciences Vol. 170. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Kumar, Ajay, Kumar Gorai, Amit (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. Advances in Space Research, 72 (11) 4663-4676 doi:10.1016/j.asr.2023.08.057
Olgun, Sevda, Üstün, Aydın, Akyılmaz, Orhan (2023) tc-cylinder: An optimized algorithm for accurate topography effect from high-resolution digital elevation models. Computers & Geosciences, 170. 105264 doi:10.1016/j.cageo.2022.105264
Bansal, Kamakhya, Tripathi, Ashish Kumar (2024) Dual level attention based lightweight vision transformer for streambed land use change classification using remote sensing. Computers & Geosciences, 191. 105676 doi:10.1016/j.cageo.2024.105676
Kumar, Ajay, Gorai, Amit Kumar (2023) Development of a deep convolutional neural network model for detection and delineation of coal mining regions. Earth Science Informatics, 16 (2) 1151-1171 doi:10.1007/s12145-023-00955-3
Wang, Tingting, Bian, Yinju, Zhang, Yixiao, Hou, Xiaolin (2023) Classification of earthquakes, explosions and mining-induced earthquakes based on XGBoost algorithm. Computers & Geosciences, 170. 105242 doi:10.1016/j.cageo.2022.105242
Patel, Ashok Kumar, Chatterjee, Snehamoy, Gorai, Amit Kumar (2018) Development of an expert system for iron ore classification. Arabian Journal of Geosciences, 11 (15) doi:10.1007/s12517-018-3733-x
Linwei, Li, Yiping, Wu, Yepiao, Huang, Bo, Li, Fasheng, Miao, Ziqiang, Deng (2023) Optimized Apriori algorithm for deformation response analysis of landslide hazards. Computers & Geosciences, 170. 105261 doi:10.1016/j.cageo.2022.105261
Khazaei, Morteza, Hamzeh, Saeid, Samani, Najmeh Neysani, Muhuri, Arnab, Goïta, Kalifa, Weng, Qihao (2023) A web-based system for satellite-based high-resolution global soil moisture maps. Computers & Geosciences, 170. 105250 doi:10.1016/j.cageo.2022.105250
Dawson, Harriet L., Dubrule, Olivier, John, Cédric M. (2023) Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification. Computers & Geosciences, 171. 105284 doi:10.1016/j.cageo.2022.105284
Lv, Zhonghui, Nunez, Karinna, Brewer, Ethan, Runfola, Dan (2023) pyShore: A deep learning toolkit for shoreline structure mapping with high-resolution orthographic imagery and convolutional neural networks. Computers & Geosciences, 171. 105296 doi:10.1016/j.cageo.2022.105296
Zheng, Dongyu, Zhong, Hanting, Camps-Valls, Gustau, Cao, Zhisong, Ma, Xiaogang, Mills, Benjamin, Hu, Xiumian, Hou, Mingcai, Ma, Chao (2024) Explainable deep learning for automatic rock classification. Computers & Geosciences, 184. 105511 doi:10.1016/j.cageo.2023.105511
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 14, 2025 13:12:11
Go to top of page