Automated recognition of deep-sea benthic megafauna in polymetallic nodule mining areas based on deep learning
The exploitation of deep-sea polymetallic nodules has attracted global attention. To mitigate its impact on deep-sea ecosystems, accurate identification of benthic megafauna is essential for developing science-based mining strategies. Deep learning has emerged as an promising approach in biological...
Saved in:
| Main Authors: | Guofan Long, Wei Song, Xiangchun Liu, Ziyao Fang, Jinqi An, Kun Liu, Yaqin Huang, Xuebao He |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003280 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrative taxonomy of new xenophyophores (Rhizaria, Foraminifera) from the abyssal northwest Pacific
by: Andrew J. Gooday, et al.
Published: (2025-07-01) -
Prospects for the application of underwater image restoration methods to facilitate marine geological exploration
by: I.V. Semernik, et al.
Published: (2025-06-01) -
Benthos-DETR: a high-precision efficient network for benthic organisms detection
by: Weibo Rao, et al.
Published: (2025-08-01) -
Fine-scale spatial organisation of deep-sea sea pens in a NE atlantic submarine canyon conservation area
by: Irene Susini, et al.
Published: (2025-08-01) -
Xenophyophore-associated mitogenomes: genomic investigations of two specimens from the Clarion-Clipperton Zone
by: Romain Gastineau, et al.
Published: (2025-07-01)