AI & Environment Resource HubMohamed Elsamahy · 2025-03-27
This study explores the role of AI for ocean conservation measures and the rise of marine sustainability.
Mohamed Elsamahy. 2025-03-27. AI for Ocean Conservation: The Role of Artificial Intelligence in Marine Sustainability. https://www.researchgate.net/publication/390177530_AI_for_Ocean_Conservation_The_Role_of_Artificial_Intelligence_in_Marine_Sustainability?channel=doi&linkId=67e378b9e2c0ea36cd9fb58d&showFulltext=true (AI & Environment Resource Hub; record paper-177; collection snapshot 2026-09-15).
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Source sheet: Scientific Paper · Row 181 · Original ID: paper-177.
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Shared topics: Cross-cutting sustainability; Water; Biodiversity and ecosystems; Oceans and coasts
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
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
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.
Shared topics: Cross-cutting sustainability; Water; Biodiversity and ecosystems; Oceans and coasts
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.