41 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. Analyzing Archive Transit Multibeam Data for Nodule Occurrences M. Mussett et al. 10.3390/jmse12122322
6. Environmental predictors of deep-sea polymetallic nodule occurrence in the global ocean A. Dutkiewicz et al. 10.1130/G46836.1
7. Ecology of a polymetallic nodule occurrence gradient: Implications for deep‐sea mining E. Simon‐Lledó et al. 10.1002/lno.11157
8. Making marine image data FAIR T. Schoening et al. 10.1038/s41597-022-01491-3
9. Image segmentation and coverage estimation of deep-sea polymetallic nodules based on lightweight deep learning model Y. Hao et al. 10.1038/s41598-025-89952-8
10. Aspects of Estimation and Reporting of Mineral Resources of Seabed Polymetallic Nodules: A Contemporaneous Case Study J. Parianos et al. 10.3390/min11020200
11. An Interpretable Multi-Model Machine Learning Approach for Spatial Mapping of Deep-Sea Polymetallic Nodule Occurrences I. Gazis et al. 10.1007/s11053-024-10393-7
12. An online path planning algorithm for autonomous marine geomorphological surveys based on AUV Y. Zhang et al. 10.1016/j.engappai.2022.105548
13. Accurate Identification Method of Small-Size Polymetallic Nodules Based on Seafloor Hyperspectral Data K. Sun et al. 10.3390/jmse12020333
14. 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
15. Extensive Coverage of Marine Mineral Concretions Revealed in Shallow Shelf Sea Areas L. Kaikkonen et al. 10.3389/fmars.2019.00541
16. Application of Soft Data in Nodule Resource Estimation S. Ellefmo & T. Kuhn 10.1007/s11053-020-09777-2
17. The potential of uncrewed and autonomous ships N. Agarwala 10.1080/18366503.2023.2172035
18. 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
19. The application of fully unmanned robotic systems for inspection of subsea pipelines A. Rumson 10.1016/j.oceaneng.2021.109214
20. 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
21. 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
22. Acoustic Seafloor Classification Using the Weyl Transform of Multibeam Echosounder Backscatter Mosaic T. Zhao et al. 10.3390/rs13091760
23. Fully convolutional neural networks applied to large-scale marine morphology mapping R. Arosio et al. 10.3389/fmars.2023.1228867
24. 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
25. Automated estimation of offshore polymetallic nodule abundance based on seafloor imagery using deep learning A. Tomczak et al. 10.1016/j.scitotenv.2024.177225
26. Assessment of polymetallic nodules in the Central Indian Ocean Basin: factors influencing distribution patterns through high‑resolution autonomous underwater vehicle (AUV) survey V. Subin Raj et al. 10.1007/s00367-024-00794-1
27. In-situ geotechnical properties of abyssal sediments and implications for environmental impacts of polymetallic nodule mining Z. Li et al. 10.1016/j.margeo.2025.107546
28. 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
29. 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
30. Research on the transportation and flow characteristics of deep-sea ore transportation equipment W. Chen et al. 10.1016/j.apor.2021.102765
31. 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
32. 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
33. Large-scale bedrock outcrop mapping on the NE Atlantic Irish continental margin A. Recouvreur et al. 10.3389/fmars.2023.1258070
34. 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
35. 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
36. 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
37. Deep-ocean polymetallic nodules as a resource for critical materials J. Hein et al. 10.1038/s43017-020-0027-0
38. 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
39. 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
40. AUV Navigation Correction Based on Automated Multibeam Tile Matching J. Mohrmann & J. Greinert 10.3390/s22030954
41. Using Robotics to Achieve Ocean Sustainability During the Exploration Phase of Deep Seabed Mining N. Agarwala 10.4031/MTSJ.57.1.15