The potential of artificial intelligence and machine learning is harnessed by MBARI and other research organizations in a new joint project to accelerate marine research. The database, called FathomNet, a collection of data will be used to train artificial intelligence (AI) algorithms for ocean exploration. FathomNet contains over 200,000 images and videos of underwater creatures, as well as data about their behavior and habitat. The database for Artificial Intelligence for Ocean Exploration will be used to teach AI algorithms. This will identify and track marine life. This is an important step in ocean conservation, as it will allow us to better understand and protect the creatures that inhabit our oceans. In the past, it has been difficult to track marine life due to the vastness of the ocean and the difficulty of access. With robotics and artificial intelligence, we will be able to monitor and study ocean life more effectively than ever before.
What is FathomNet?
The ocean is changing at an unprecedented rate, making it difficult to monitor marine biota at the spatial and temporal scale needed for responsible stewardship. The amount and speed of data collection needed to exceed our ability to process and analyze it quickly as the research community searches for baseline data. Due to the lack of data consistency, inadequate formatting, and the need for huge, labeled data sets, recent breakthroughs in machine learning are enabling rapid, intelligent analysis of visual data. To address this need, we developed FathomNet, an open-source image database that harmonizes and compiles carefully selected and categorized data.
How FathomNet is changing ocean exploration?
FathomNet is indeed the platform that will change the way we explore the oceans.
The oceans are home to some of our planet’s most beautiful creatures, but what lies beneath is still largely unknown. The sheer size of the oceans and the depths they reach make exploring them a costly and complicated endeavor. FathomNet, a new technology, is changing that challenge by bringing ocean exploration into the 21st century.
FathomNet is a network of submersible robots called hydrophones. These robots map the ocean floor in real time using sonar and then share that information with a base station through radio waves. FathomNet’s ability to transmit information through radio waves. This means that these robots can’t travel very far from each other. However, this also has major benefits for the exploration of the oceans because it means that together these robots can cover a larger area than one robot could on its own. It also means that these robots never have to leave the water and don’t need any air to run. This network of autonomous underwater machines allows us to study marine ecology and animal behavior in unprecedented detail. FathomNet also helps mitigate some of our carbon emissions by reducing the need for research vessels that have to use fuel and other resources. As emerging technologies continue to grow, FathomNet will allow us to get closer than ever before to explore what lies beneath the surface of our oceans.
Why Fathom Net requires Ocean exploration?
- Traditional, resource-intensive (e.g., time, person-hours, cost)
- Sampling methodologies are constrained in their capacity to scale in spatiotemporal resolution and engage diverse communities when monitoring an area as vast as the ocean which is teeming with unknown species.
However, we are starting to witness a paradigm shift in ocean exploration and discovery as a result of the development of contemporary robotics. Modern Robotics and artificial intelligence are more affordable as it has the following features in them. They also push the chemical and remote sensing communities to new scales of observation.
- low-cost observation platforms
- Distributed sensing.
- Distributed platforms and open data architectures
It is without the use of artificial intelligence this large-scale sampling of biological communities or activities below the surface is difficult because of several issues.
How machine learning is changing the way we study marine life
FathomNet is a new project that is using machine learning to study marine life. This technology is providing a new way to identify and track marine life.
Machine learning AI is helping us to better understand the ocean environment and the creatures that call it home. This project is still in its early stages, but it has already provided some interesting results. Image-based sampling is becoming increasingly popular for sampling biological populations in a variety of situations due to its ease of implementation.
For example, the team has been able to use machine learning to identify whale calls. This is a significant achievement, as it can help us to better understand how these creatures communicate.
The team is also working on using machine learning to identify other marine life, such as dolphins and fish. This will help us to better understand the ecology of the oceans and the interactions between different species.
Ultimately, the goal of FathomNet is to use machine learning to create a “digital fisheries observer”. This robotic system deployed on fishing boats to automatically record data about catches. This would provide a more accurate and efficient way to monitor fishing activity, and could help to reduce overfishing.
Underwater vehicles having Machine learning AI has also utilized imaging techniques to operate in real-time and navigate in complex environments while completing challenging tasks
A powerful communication tool for educating larger communities about marine species and marine issues is photography. FathomNet is an exciting new project that is using cutting-edge technology to study marine life. Recent marine surveys and AI Articles of Oceanography will comprise Fathom Net as the major tool to bring marine life to the surface.
Photography is a powerful engagement tool for communicating knowledge about marine species and the problems confronting the ocean with larger communities. A visual data set is an integral part of gaining a better understanding of the ocean and disseminating that knowledge.
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