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
-
Fauna diversity in Madracis spp. coral patches in the Colombian Caribbean
by: C. Cedeño-Posso, et al.
Published: (2023-10-01) -
Integrative taxonomy of new xenophyophores (Rhizaria, Foraminifera) from the abyssal northwest Pacific
by: Andrew J. Gooday, et al.
Published: (2025-07-01) -
Deep-sea benthic ecosystems waste nothing and recycle everything, even viruses
by: Cinzia Corinaldesi
Published: (2022-12-01) -
Evaluation of Deep Learning Models for Polymetallic Nodule Detection and Segmentation in Seafloor Imagery
by: Gabriel Loureiro, et al.
Published: (2025-02-01) -
Ecosystem changes in deep Eastern Mediterranean: a comparison between 1989 and 2023
by: Alessandra Kostantchouk, et al.
Published: (2025-07-01)