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

Hansen, Signe Schilling, Ernstsen, Verner Brandbyge, Andersen, Mikkel Skovgaard, Al-Hamdani, Zyad, Baran, Ramona, Niederwieser, Manfred, Steinbacher, Frank, Kroon, Aart (2022) Evaluation of Boulder Characteristics for Improved Boulder Detection Based on Machine Learning Techniques. Geosciences, 12 (11) 421 doi:10.3390/geosciences12110421

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleEvaluation of Boulder Characteristics for Improved Boulder Detection Based on Machine Learning Techniques
JournalGeosciences
AuthorsHansen, Signe SchillingAuthor
Ernstsen, Verner BrandbygeAuthor
Andersen, Mikkel SkovgaardAuthor
Al-Hamdani, ZyadAuthor
Baran, RamonaAuthor
Niederwieser, ManfredAuthor
Steinbacher, FrankAuthor
Kroon, AartAuthor
Year2022 (November 16)Volume12
Issue11
PublisherMDPI AG
DOIdoi:10.3390/geosciences12110421Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID15525818Long-form Identifiermindat:1:5:15525818:3
GUID0
Full ReferenceHansen, Signe Schilling, Ernstsen, Verner Brandbyge, Andersen, Mikkel Skovgaard, Al-Hamdani, Zyad, Baran, Ramona, Niederwieser, Manfred, Steinbacher, Frank, Kroon, Aart (2022) Evaluation of Boulder Characteristics for Improved Boulder Detection Based on Machine Learning Techniques. Geosciences, 12 (11) 421 doi:10.3390/geosciences12110421
Plain TextHansen, Signe Schilling, Ernstsen, Verner Brandbyge, Andersen, Mikkel Skovgaard, Al-Hamdani, Zyad, Baran, Ramona, Niederwieser, Manfred, Steinbacher, Frank, Kroon, Aart (2022) Evaluation of Boulder Characteristics for Improved Boulder Detection Based on Machine Learning Techniques. Geosciences, 12 (11) 421 doi:10.3390/geosciences12110421
In(2022, October) Geosciences Vol. 12 (11) MDPI AG

See Also

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

Hansen, Signe Schilling, Ernstsen, Verner Brandbyge, Andersen, Mikkel Skovgaard, Al-Hamdani, Zyad, Baran, Ramona, Niederwieser, Manfred, Steinbacher, Frank, Kroon, Aart (2021) Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data. Remote Sensing, 13 (20). doi:10.3390/rs13204101
Andersen, Mikkel Skovgaard, Gergely, Áron, Al-Hamdani, Zyad, Steinbacher, Frank, Larsen, Laurids Rolighed, Ernstsen, Verner Brandbyge (2017) Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment. Hydrology and Earth System Sciences, 21 (1) 43-63 doi:10.5194/hess-21-43-2017
Hansen, Lars Ø., Ernstsen, Verner B., Andersen, Mikkel S., Al-Hamdani, Zyad, Becker, Marius, Andresen, Katrine J., Trinhammer, Per L., Vang, Torben, Bartholdy, Jesper, Kroon, Aart (2022) A multi-scale geomorphometric approach to semi-automated classification of seabed morphology of a dynamic and complex marine meander bend. Geomorphology, 413. 108371 doi:10.1016/j.geomorph.2022.108371
Hansen, Lars Ø., Ernstsen, Verner B., Clemmensen, Lars B., Al‐Hamdani, Zyad, Kroon, Aart (2021) A method for estimating sediment budgets of washover deposits using digital terrain models. Earth Surface Processes and Landforms, 46 (4) 804-821 doi:10.1002/esp.5066
Baran, Ramona, Dobler, Wolfgang, Steinbacher, Frank, Ritter, Marcel, Niederwieser, Manfred, Benger, Werner, Aufleger, Markus (2013) Verschneidung von Airborne Hydromapping und Fächerecholotvermessung bei Rheinfelden. WASSERWIRTSCHAFT, 103 (9) 18-25 doi:10.1365/s35147-013-0711-9
Luetzenburg, Gregor, Kroon, Aart, Bjørk, Anders A. (2021) Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences. Scientific Reports, 11 (1) doi:10.1038/s41598-021-01763-9
Mohebzadeh, Hamid, Biswas, Asim, Rudra, Ramesh, Daggupati, Prasad (2022) Machine Learning Techniques for Gully Erosion Susceptibility Mapping: A Review. Geosciences, 12 (12) 429 doi:10.3390/geosciences12120429
Feldens, Peter (2020) Super Resolution by Deep Learning Improves Boulder Detection in Side Scan Sonar Backscatter Mosaics. Remote Sensing, 12 (14). doi:10.3390/rs12142284
Evdochim, Lucian, Dobrescu, Dragoș, Halichidis, Stela, Dobrescu, Lidia, Stanciu, Silviu (2022) Hypertension Detection Based on Photoplethysmography Signal Morphology and Machine Learning Techniques. Applied Sciences, 12 (16) 8380 doi:10.3390/app12168380
Bold, Robert, Al-Khateeb, Haider, Ersotelos, Nikolaos (2022) Reducing False Negatives in Ransomware Detection: A Critical Evaluation of Machine Learning Algorithms. Applied Sciences, 12 (24) 12941 doi:10.3390/app122412941
Keohane, Isaac, White, Scott (2022) Chimney Identification Tool for Automated Detection of Hydrothermal Chimneys from High-Resolution Bathymetry Using Machine Learning. Geosciences, 12 (4) 176 doi:10.3390/geosciences12040176
 
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: August 31, 2025 21:47:19
Go to top of page