Scientific Paper · 2025-03-27
AI for Ocean Conservation: The Role of Artificial Intelligence in Marine Sustainability
This study explores the role of AI for ocean conservation measures and the rise of marine sustainability.
Scientific Paper · source-verified
Unlocking the Soundscape of Coral Reefs With Artificial Intelligence: Pretrained Networks and Unsupervised Learning Win Out
Ben Williams; Santiago M. Balvanera; Sarab S. Sethi; Timothy A.C. Lamont; Jamaluddin Jompa; Mochyudho Prasetya; Laura Richardson; Lucille Chapuis; Emma Weschke; Andrew Hoey; Ricardo Beldade; Suzanne C. Mills; Anne Haguenauer; Frederic Zuberer; Stephen D. Simpson; David Curnick; Kate E. Jones · 2025-04-28
This study demonstrates that machine learning, especially pretrained and unsupervised models, can efficiently analyze whole coral reef soundscapes to classify ecological conditions, offering scalable, low-cost insights into reef health and biodiversity across diverse environments.
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Ben Williams; Santiago M. Balvanera; Sarab S. Sethi; Timothy A.C. Lamont; Jamaluddin Jompa; Mochyudho Prasetya; Laura Richardson; Lucille Chapuis; Emma Weschke; Andrew Hoey; Ricardo Beldade; Suzanne C. Mills; Anne Haguenauer; Frederic Zuberer; Stephen D. Simpson; David Curnick; Kate E. Jones. 2025-04-28. Unlocking the Soundscape of Coral Reefs With Artificial Intelligence: Pretrained Networks and Unsupervised Learning Win Out. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013029 (AI & Environment Resource Hub; record paper-311; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
lifecycle
orientation
Available source metadata
- Author or creator
- Ben Williams; Santiago M. Balvanera; Sarab S. Sethi; Timothy A.C. Lamont; Jamaluddin Jompa; Mochyudho Prasetya; Laura Richardson; Lucille Chapuis; Emma Weschke; Andrew Hoey; Ricardo Beldade; Suzanne C. Mills; Anne Haguenauer; Frederic Zuberer; Stephen D. Simpson; David Curnick; Kate E. Jones
- Publisher
- PLOS Computational Biology
- Publication date
- 2025-04-28
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-15T16:44:45.877Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Water & Oceans
- Legacy subdomain
- Bioacoustics & AI-Driven Ecosystem Monitoring
- journal
- PLOS Computational Biology
Provenance and review
Source sheet: Scientific Paper · Row 315 · Original ID: paper-311.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Cross-cutting sustainability; Water; Biodiversity and ecosystems; Oceans and coasts
Shared topics: Cross-cutting sustainability; Water; Biodiversity and ecosystems; Oceans and coasts
Tool
European Digital Twin Ocean (EDITO)
Welcome to the core infrastructure of the European Digital Twin Ocean. EDITO offers cutting edge tools to develop digital twins, support science-based decision making, and ensure maximum impact for research and innovation actions across the key objectives of the EU Mission Ocean & Waters: protect biodiversity, stop marine pollution, and support a sustainable blue economy.
Shared topics: Cross-cutting sustainability; Water; Biodiversity and ecosystems; Oceans and coasts
Scientific Paper · 2025-03-10
Lightweight Multimodal Artificial Intelligence Framework for Maritime Multi-Scene Recognition
This paper presents a multimodal AI framework integrating image data, textual descriptions, and classification vectors from a Multimodal Large Language Model (MLLM) to enhance maritime multi-scene recognition, achieving 98% accuracy while optimizing deployment for resource-constrained Autonomous Surface Vehicles (ASVs) in marine conservation, environmental monitoring, and disaster response.
Shared topics: Cross-cutting sustainability; Water; Biodiversity and ecosystems; Oceans and coasts
Scientific Paper · 2024-10-01
SLAGREEF: Preliminary Results on Slag-Based Artificial Reef Design
The SLAGREEF project explores the use of slag furnace residues and inert waste to 3D-print artificial reefs, aiming to protect coastal areas and restore marine ecosystems, with ongoing monitoring at the Obsea underwater observatory.