35 citations as recorded by crossref.

1. Subtidal Natural Hard Substrate Quantitative Habitat Mapping: Interlinking Underwater Acoustics and Optical Imagery with Machine Learning G. Montereale Gavazzi et al. 10.3390/rs13224608
2. A possible link between seamount sector collapse and manganese nodule occurrence in the abyssal plains, NW Pacific Ocean Z. Li et al. 10.1016/j.oregeorev.2021.104378
3. Quantitative Expression of the Burial Phenomenon of Deep Seafloor Manganese Nodules A. Tsune 10.3390/min11020227
4. Using the random forest algorithm to integrate hydroacoustic data with satellite images to improve the mapping of shallow nearshore benthic features in a marine protected area in Jamaica K. McLaren et al. 10.1080/15481603.2019.1613803
5. Environmental predictors of deep-sea polymetallic nodule occurrence in the global ocean A. Dutkiewicz et al. 10.1130/G46836.1
6. Ecology of a polymetallic nodule occurrence gradient: Implications for deep‐sea mining E. Simon‐Lledó et al. 10.1002/lno.11157
7. Making marine image data FAIR T. Schoening et al. 10.1038/s41597-022-01491-3
8. Aspects of Estimation and Reporting of Mineral Resources of Seabed Polymetallic Nodules: A Contemporaneous Case Study J. Parianos et al. 10.3390/min11020200
9. An online path planning algorithm for autonomous marine geomorphological surveys based on AUV Y. Zhang et al. 10.1016/j.engappai.2022.105548
10. Accurate Identification Method of Small-Size Polymetallic Nodules Based on Seafloor Hyperspectral Data K. Sun et al. 10.3390/jmse12020333
11. Estimation Accuracy and Classification of Polymetallic Nodule Resources Based on Classical Sampling Supported by Seafloor Photography (Pacific Ocean, Clarion-Clipperton Fracture Zone, IOM Area) J. Mucha & M. Wasilewska-Błaszczyk 10.3390/min10030263
12. Extensive Coverage of Marine Mineral Concretions Revealed in Shallow Shelf Sea Areas L. Kaikkonen et al. 10.3389/fmars.2019.00541
13. Application of Soft Data in Nodule Resource Estimation S. Ellefmo & T. Kuhn 10.1007/s11053-020-09777-2
14. The potential of uncrewed and autonomous ships N. Agarwala 10.1080/18366503.2023.2172035
15. Deep learning–assisted biodiversity assessment in deep-sea benthic megafauna communities: a case study in the context of polymetallic nodule mining D. Cuvelier et al. 10.3389/fmars.2024.1366078
16. The application of fully unmanned robotic systems for inspection of subsea pipelines A. Rumson 10.1016/j.oceaneng.2021.109214
17. Possibilities and Limitations of the Use of Seafloor Photographs for Estimating Polymetallic Nodule Resources—Case Study from IOM Area, Pacific Ocean M. Wasilewska-Błaszczyk & J. Mucha 10.3390/min10121123
18. Importance of Spatial Autocorrelation in Machine Learning Modeling of Polymetallic Nodules, Model Uncertainty and Transferability at Local Scale I. Gazis & J. Greinert 10.3390/min11111172
19. Acoustic Seafloor Classification Using the Weyl Transform of Multibeam Echosounder Backscatter Mosaic T. Zhao et al. 10.3390/rs13091760
20. Fully convolutional neural networks applied to large-scale marine morphology mapping R. Arosio et al. 10.3389/fmars.2023.1228867
21. Analysis-ready optical underwater images of Manganese-nodule covered seafloor of the Clarion-Clipperton Zone B. Mbani & J. Greinert 10.1038/s41597-023-02245-5
22. THE MODERN TRENDS IN THE DEVELOPMENT OF EQUIPMENT AND TECHNOLOGY EXPLORATION AND MINING OF MANGANESE NODULES AND COBALT-RICH FERROMANGANESE CRUSTS IN THE WORLD OCEAN V. Yubko et al. 10.29006/1564-2291.JOR-2023.51(4).8
23. Linkages between sediment thickness, geomorphology and Mn nodule occurrence: New evidence from AUV geophysical mapping in the Clarion-Clipperton Zone E. Alevizos et al. 10.1016/j.dsr.2021.103645
24. Research on the transportation and flow characteristics of deep-sea ore transportation equipment W. Chen et al. 10.1016/j.apor.2021.102765
25. Acoustic backscattering properties of manganese nodules: Numerical and laboratory experiments based on Sub-bottom acoustic profile surveys J. Matsushima et al. 10.1080/1064119X.2022.2112789
26. Investigating the benthic megafauna in the eastern Clarion Clipperton Fracture Zone (north-east Pacific) based on distribution models predicted with random forest K. Uhlenkott et al. 10.1038/s41598-022-12323-0
27. Large-scale bedrock outcrop mapping on the NE Atlantic Irish continental margin A. Recouvreur et al. 10.3389/fmars.2023.1258070
28. Acoustic estimation of ferromanganese crust exposure and sediment cover in a Northwest Pacific seamount using statistical analyses of shipboard multibeam acoustic data T. Kaji et al. 10.1080/1064119X.2023.2261015
29. Environment, ecology, and potential effectiveness of an area protected from deep-sea mining (Clarion Clipperton Zone, abyssal Pacific) D. Jones et al. 10.1016/j.pocean.2021.102653
30. Automated Seafloor Massive Sulfide Detection Through Integrated Image Segmentation and Geophysical Data Analysis: Revisiting the TAG Hydrothermal Field A. Haroon et al. 10.1029/2023GC011250
31. Deep-ocean polymetallic nodules as a resource for critical materials J. Hein et al. 10.1038/s43017-020-0027-0
32. Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean P. Josso et al. 10.1016/j.oregeorev.2023.105671
33. Using multibeam backscatter strength to analyze the distribution of manganese nodules: A case study of seamounts in the Western Pacific Ocean M. Wang et al. 10.1016/j.apacoust.2020.107729
34. AUV Navigation Correction Based on Automated Multibeam Tile Matching J. Mohrmann & J. Greinert 10.3390/s22030954
35. Using Robotics to Achieve Ocean Sustainability During the Exploration Phase of Deep Seabed Mining N. Agarwala 10.4031/MTSJ.57.1.15