Advancements in Marine Ecosystem Monitoring and Analysis

The field of marine ecosystem monitoring and analysis is moving towards increased automation and accuracy, leveraging deep learning and computer vision techniques to classify coral bleaching, identify fish families, and reconstruct 3D growth trajectories of planktonic foraminifera. These innovative approaches aim to reduce manual effort and provide more efficient and scalable methods for monitoring and understanding marine ecosystems. Noteworthy papers include:

  • A study that presents a novel machine-learning-based coral bleaching classification system, achieving an accuracy of 88% with a CNN model.
  • A paper that introduces HideAndSeg, a minimally supervised AI-based tool for segmenting videos of octopuses in natural habitats, establishing a quantitative baseline for this task.

Sources

Deep Learning Models for Coral Bleaching Classification in Multi-Condition Underwater Image Datasets

Automating Coral Reef Fish Family Identification on Video Transects Using a YOLOv8-Based Deep Learning Pipeline

From Instance Segmentation to 3D Growth Trajectory Reconstruction in Planktonic Foraminifera

OLATverse: A Large-scale Real-world Object Dataset with Precise Lighting Control

HideAndSeg: an AI-based tool with automated prompting for octopus segmentation in natural habitats

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