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219 results on '"Katija, Kakani"'

1. FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data

Author

Khanal, Nabin, Yu, Chun Meng, Chiu, Jui-Cheng, Chaudhary, Anav, Zhang, Ziyue, Katija, Kakani, and Forbes, Angus G.

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, H.5.2, I.2.7, I.7.10
Abstract

We introduce FathomGPT, an open source system for the interactive investigation of ocean science data via a natural language interface. FathomGPT was developed in close collaboration with marine scientists to enable researchers to explore and analyze the FathomNet image database. FathomGPT provides a custom information retrieval pipeline that leverages OpenAI's large language models to enable: the creation of complex queries to retrieve images, taxonomic information, and scientific measurements; mapping common names and morphological features to scientific names; generating interactive charts on demand; and searching by image or specified patterns within an image. In designing FathomGPT, particular emphasis was placed on enhancing the user's experience by facilitating free-form exploration and optimizing response times. We present an architectural overview and implementation details of FathomGPT, along with a series of ablation studies that demonstrate the effectiveness of our approach to name resolution, fine tuning, and prompt modification. We also present usage scenarios of interactive data exploration sessions and document feedback from ocean scientists and machine learning experts., Comment: The first two authors contributed equally to this work. Accepted to the 37th Annual ACM Symposium on User Interface Software and Technology (UIST 2024)

Published
2024
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2. FathomVerse: A community science dataset for ocean animal discovery

Author

Patterson, Genevieve, Daniels, Joost, Woodward, Benjamin, Barnard, Kevin, Sainz, Giovanna, Lundsten, Lonny, and Katija, Kakani

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction
Abstract

Can computer vision help us explore the ocean? The ultimate challenge for computer vision is to recognize any visual phenomena, more than only the objects and animals humans encounter in their terrestrial lives. Previous datasets have explored everyday objects and fine-grained categories humans see frequently. We present the FathomVerse v0 detection dataset to push the limits of our field by exploring animals that rarely come in contact with people in the deep sea. These animals present a novel vision challenge. The FathomVerse v0 dataset consists of 3843 images with 8092 bounding boxes from 12 distinct morphological groups recorded at two locations on the deep seafloor that are new to computer vision. It features visually perplexing scenarios such as an octopus intertwined with a sea star, and confounding categories like vampire squids and sea spiders. This dataset can push forward research on topics like fine-grained transfer learning, novel category discovery, species distribution modeling, and carbon cycle analysis, all of which are important to the care and husbandry of our planet., Comment: 10 pages, 14 figures

Published
2024

5. The FathomNet2023 Competition Dataset

Author

Orenstein, Eric, Barnard, Kevin, Lundsten, Lonny, Patterson, Geneviève, Woodward, Benjamin, and Katija, Kakani

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Ocean scientists have been collecting visual data to study marine organisms for decades. These images and videos are extremely valuable both for basic science and environmental monitoring tasks. There are tools for automatically processing these data, but none that are capable of handling the extreme variability in sample populations, image quality, and habitat characteristics that are common in visual sampling of the ocean. Such distribution shifts can occur over very short physical distances and in narrow time windows. Creating models that are able to recognize when an image or video sequence contains a new organism, an unusual collection of animals, or is otherwise out-of-sample is critical to fully leverage visual data in the ocean. The FathomNet2023 competition dataset presents a realistic scenario where the set of animals in the target data differs from the training data. The challenge is both to identify the organisms in a target image and assess whether it is out-of-sample., Comment: Competition was presented as part of the 10th Fine Grained Visual Categorization workshop at the 2023 Computer Vision and Pattern Recognition conference. 4 pages, 4 figures

Published
2023

6. Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation

Author

Crosby, Alison, Orenstein, Eric C., Poulton, Susan E., Bell, Katherine L. C., Woodward, Benjamin, Ruhl, Henry, Katija, Kakani, and Forbes, Angus G.

Subjects
Computer Science - Human-Computer Interaction
Abstract

Ocean scientists studying diverse organisms and phenomena increasingly rely on imaging devices for their research. These scientists have many tools to collect their data, but few resources for automated analysis. In this paper, we report on discussions with diverse stakeholders to identify community needs and develop a set of functional requirements for the ongoing development of ocean science-specific analysis tools. We conducted 36 in-depth interviews with individuals working in the Blue Economy space, revealing four central issues inhibiting the development of effective imaging analysis monitoring tools for marine science. We also identified twelve user archetypes that will engage with these services. Additionally, we held a workshop with 246 participants from 35 countries centered around FathomNet, a web-based open-source annotated image database for marine research. Findings from these discussions are being used to define the feature set and interface design of Ocean Vision AI, a suite of tools and services to advance observational capabilities of life in the ocean., Comment: Accepted to ACM CHI 2023

Published
2023
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7. A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles

Author

Masmitja, Ivan, Martin, Mario, Katija, Kakani, Gomariz, Spartacus, and Navarro, Joan

Subjects
Computer Science - Robotics, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract

Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-short baseline systems. Nonetheless, in ROSB target localization methods, the trajectory of the tracking vehicle near the localized target plays an important role in obtaining the best accuracy of the predicted target position. Here, we investigate a Reinforcement Learning (RL) approach to find the optimal path that an autonomous vehicle should follow in order to increase and optimize the overall accuracy of the predicted target localization, while reducing time and power consumption. To accomplish this objective, different experimental tests have been designed using state-of-the-art deep RL algorithms. Our study also compares the results obtained with the analytical Fisher information matrix approach used in previous studies. The results revealed that the policy learned by the RL agent outperforms trajectories based on these analytical solutions, e.g. the median predicted error at the beginning of the target's localisation is 17% less. These findings suggest that using deep RL for localizing acoustic targets could be successfully applied to in-water applications that include tracking of acoustically tagged marine animals by autonomous underwater vehicles. This is envisioned as a first necessary step to validate the use of RL to tackle such problems, which could be used later on in a more complex scenarios, Comment: Accepted at CASE2022. Code at this Github repository https://github.com/imasmitja/RLforUTracking

Published
2023
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8. FathomNet: A global image database for enabling artificial intelligence in the ocean

Author

Katija, Kakani, Orenstein, Eric, Schlining, Brian, Lundsten, Lonny, Barnard, Kevin, Sainz, Giovanna, Boulais, Oceane, Cromwell, Megan, Butler, Erin, Woodward, Benjamin, and Bell, Katy Croff

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As baselines are sought by the research community, the volume and rate of this required data collection rapidly outpaces our abilities to process and analyze them. Recent advances in machine learning enables fast, sophisticated analysis of visual data, but have had limited success in the ocean due to lack of data standardization, insufficient formatting, and demand for large, labeled datasets. To address this need, we built FathomNet, an open-source image database that standardizes and aggregates expertly curated labeled data. FathomNet has been seeded with existing iconic and non-iconic imagery of marine animals, underwater equipment, debris, and other concepts, and allows for future contributions from distributed data sources. We demonstrate how FathomNet data can be used to train and deploy models on other institutional video to reduce annotation effort, and enable automated tracking of underwater concepts when integrated with robotic vehicles. As FathomNet continues to grow and incorporate more labeled data from the community, we can accelerate the processing of visual data to achieve a healthy and sustainable global ocean.

Published
2021

9. FathomNet: An underwater image training database for ocean exploration and discovery

Author

Boulais, Océane, Woodward, Ben, Schlining, Brian, Lundsten, Lonny, Barnard, Kevin, Bell, Katy Croff, and Katija, Kakani

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Databases
Abstract

Thousands of hours of marine video data are collected annually from remotely operated vehicles (ROVs) and other underwater assets. However, current manual methods of analysis impede the full utilization of collected data for real time algorithms for ROV and large biodiversity analyses. FathomNet is a novel baseline image training set, optimized to accelerate development of modern, intelligent, and automated analysis of underwater imagery. Our seed data set consists of an expertly annotated and continuously maintained database with more than 26,000 hours of videotape, 6.8 million annotations, and 4,349 terms in the knowledge base. FathomNet leverages this data set by providing imagery, localizations, and class labels of underwater concepts in order to enable machine learning algorithm development. To date, there are more than 80,000 images and 106,000 localizations for 233 different classes, including midwater and benthic organisms. Our experiments consisted of training various deep learning algorithms with approaches to address weakly supervised localization, image labeling, object detection and classification which prove to be promising. While we find quality results on prediction for this new dataset, our results indicate that we are ultimately in need of a larger data set for ocean exploration., Comment: 8 pages, 6 figures

Published
2020

11. The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column.

Author

Choy, C Anela, Robison, Bruce H, Gagne, Tyler O, Erwin, Benjamin, Firl, Evan, Halden, Rolf U, Hamilton, J Andrew, Katija, Kakani, Lisin, Susan E, Rolsky, Charles, and S Van Houtan, Kyle

Abstract

Plastic waste has been documented in nearly all types of marine environments and has been found in species spanning all levels of marine food webs. Within these marine environments, deep pelagic waters encompass the largest ecosystems on Earth. We lack a comprehensive understanding of the concentrations, cycling, and fate of plastic waste in sub-surface waters, constraining our ability to implement effective, large-scale policy and conservation strategies. We used remotely operated vehicles and engineered purpose-built samplers to collect and examine the distribution of microplastics in the Monterey Bay pelagic ecosystem at water column depths ranging from 5 to 1000 m. Laser Raman spectroscopy was used to identify microplastic particles collected from throughout the deep pelagic water column, with the highest concentrations present at depths between 200 and 600 m. Examination of two abundant particle feeders in this ecosystem, pelagic red crabs (Pleuroncodes planipes) and giant larvaceans (Bathochordaeus stygius), showed that microplastic particles readily flow from the environment into coupled water column and seafloor food webs. Our findings suggest that one of the largest and currently underappreciated reservoirs of marine microplastics may be contained within the water column and animal communities of the deep sea.

Published
2019

13. From the surface to the seafloor: How giant larvaceans transport microplastics into the deep sea.

Author

Katija, Kakani, Choy, C Anela, Sherlock, Rob E, Sherman, Alana D, and Robison, Bruce H

Subjects
Plastics, Water Pollutants, Chemical, Seawater, Environmental Monitoring, Waste Products
Abstract

Plastic waste is a pervasive feature of marine environments, yet little is empirically known about the biological and physical processes that transport plastics through marine ecosystems. To address this need, we conducted in situ feeding studies of microplastic particles (10 to 600 μm in diameter) with the giant larvacean Bathochordaeus stygius. Larvaceans are abundant components of global zooplankton assemblages, regularly build mucus "houses" to filter particulate matter from the surrounding water, and later abandon these structures when clogged. By conducting in situ feeding experiments with remotely operated vehicles, we show that giant larvaceans are able to filter a range of microplastic particles from the water column, ingest, and then package microplastics into their fecal pellets. Microplastics also readily affix to their houses, which have been shown to sink quickly to the seafloor and deliver pulses of carbon to benthic ecosystems. Thus, giant larvaceans can contribute to the vertical flux of microplastics through the rapid sinking of fecal pellets and discarded houses. Larvaceans, and potentially other abundant pelagic filter feeders, may thus comprise a novel biological transport mechanism delivering microplastics from surface waters, through the water column, and to the seafloor. Our findings necessitate the development of tools and sampling methodologies to quantify concentrations and identify environmental microplastics throughout the water column.

Published
2017

14. Report on the Marine Imaging Workshop 2022

Author

Borremans, Catherine, Durden, Jennifer, Schoening, Timm, Curtis, Emma, Adams, Luther, Branzan Albu, Alexandra, Arnaubec, Aurélien, Ayata, Sakina-Dorothée, Baburaj, Reshma, Bassin, Corinne, Beck, Miriam, Bigham, Katharine, Boschen-Rose, Rachel, Collett, Chad, Contini, Matteo, Correa, Paulo, Dominguez-Carrió, Carlos, Dreyfus, Gautier, Duncan, Graeme, Ferrera, Maxime, Foulon, Valentin, Friedman, Ariell, Gaikwad, Santosh, Game, Chloe, Gaytán-Caballero, Adriana, Girard, Fanny, Giusti, Michela, Hanafi-Portier, Mélissa, Howell, Kerry, Hulevata, Iryna, Itiowe, Kiamuke, Jackett, Chris, Jansen, Jan, Karthäuser, Clarissa, Katija, Kakani, Kernec, Maxime, Kim, Gabriel, Kitahara, Marcelo, Langenkämper, Daniel, Langlois, Tim, Lanteri, Nadine, Jianping Li, Claude, Li, Qi-Ran, Liabot, Pierre-Olivier, Lindsay, Dhugal, Loulidi, Ali, Marcon, Yann, Marini, Simone, Marranzino, Ashley, Massot-Campos, Miquel, Matabos, Marjolaine, Menot, Lenaick, Moreno, Bernabé, Morrissey, Marcus, Nakath, David, Nattkemper, Tim, Neufeld, Monika, Obst, Matthias, Olu, Karine, Parimbelli, Alexa, Pasotti, Francesca, Pelletier, Dominique, Perhirin, Margaux, Piechaud, Nils, Pizarro, Oscar, Purser, Autun, Rodrigues, Clara, Ceballos Romero, Elena, Schlining, Brian, Song, Yifan, Sosik, Heidi, Sourisseau, Marc, Taormina, Bastien, Taucher, Jan, Thornton, Blair, Van Audenhaege, Loïc, von der Meden, Charles, Wacquet, Guillaume, Williams, Jack, Witting, Kea, Zurowietz, Martin, Borremans, Catherine, Durden, Jennifer, Schoening, Timm, Curtis, Emma, Adams, Luther, Branzan Albu, Alexandra, Arnaubec, Aurélien, Ayata, Sakina-Dorothée, Baburaj, Reshma, Bassin, Corinne, Beck, Miriam, Bigham, Katharine, Boschen-Rose, Rachel, Collett, Chad, Contini, Matteo, Correa, Paulo, Dominguez-Carrió, Carlos, Dreyfus, Gautier, Duncan, Graeme, Ferrera, Maxime, Foulon, Valentin, Friedman, Ariell, Gaikwad, Santosh, Game, Chloe, Gaytán-Caballero, Adriana, Girard, Fanny, Giusti, Michela, Hanafi-Portier, Mélissa, Howell, Kerry, Hulevata, Iryna, Itiowe, Kiamuke, Jackett, Chris, Jansen, Jan, Karthäuser, Clarissa, Katija, Kakani, Kernec, Maxime, Kim, Gabriel, Kitahara, Marcelo, Langenkämper, Daniel, Langlois, Tim, Lanteri, Nadine, Jianping Li, Claude, Li, Qi-Ran, Liabot, Pierre-Olivier, Lindsay, Dhugal, Loulidi, Ali, Marcon, Yann, Marini, Simone, Marranzino, Ashley, Massot-Campos, Miquel, Matabos, Marjolaine, Menot, Lenaick, Moreno, Bernabé, Morrissey, Marcus, Nakath, David, Nattkemper, Tim, Neufeld, Monika, Obst, Matthias, Olu, Karine, Parimbelli, Alexa, Pasotti, Francesca, Pelletier, Dominique, Perhirin, Margaux, Piechaud, Nils, Pizarro, Oscar, Purser, Autun, Rodrigues, Clara, Ceballos Romero, Elena, Schlining, Brian, Song, Yifan, Sosik, Heidi, Sourisseau, Marc, Taormina, Bastien, Taucher, Jan, Thornton, Blair, Van Audenhaege, Loïc, von der Meden, Charles, Wacquet, Guillaume, Williams, Jack, Witting, Kea, and Zurowietz, Martin

Abstract

Imaging is increasingly used to capture information on the marine environment thanks to the improvements in imaging equipment, devices for carrying cameras and data storage in recent years. In that context, biologists, geologists, computer specialists and end-users must gather to discuss the methods and procedures for optimising the quality and quantity of data collected from images. The 4 Marine Imaging Workshop was organised from 3-6 October 2022 in Brest (France) in a hybrid mode. More than a hundred participants were welcomed in person and about 80 people attended the online sessions. The workshop was organised in a single plenary session of presentations followed by discussion sessions. These were based on dynamic polls and open questions that allowed recording of the imaging community’s current and future ideas. In addition, a whole day was dedicated to practical sessions on image analysis, data standardisation and communication tools. The format of this edition allowed the participation of a wider community, including lower income countries, early career scientists, all working on laboratory, benthic and pelagic imaging. This article summarises the topics addressed during the workshop, particularly the outcomes of the discussion sessions for future reference and to make the workshop results available to the open public.

Published
2024

15. DeepSTARia: enabling autonomous, targeted observations of ocean life in the deep sea

Author

European Commission, Agencia Estatal de Investigación (España), Barnard, Kevin, Daniels, Joost, Roberts, Paul L. D., Orenstein, Eric C., Masmitja, Ivan, Takahashi, Jonathan, Woodward, Benjamin, Katija, Kakani, European Commission, Agencia Estatal de Investigación (España), Barnard, Kevin, Daniels, Joost, Roberts, Paul L. D., Orenstein, Eric C., Masmitja, Ivan, Takahashi, Jonathan, Woodward, Benjamin, and Katija, Kakani

Abstract

The ocean remains one of the least explored places on our planet, containing myriad life that are either unknown to science or poorly understood. Given the technological challenges and limited resources available for exploring this vast space, more targeted approaches are required to scale spatiotemporal observations and monitoring of ocean life. The promise of autonomous underwater vehicles to fulfill these needs has largely been hindered by their inability to adapt their behavior in real-time based on what they are observing. To overcome this challenge, we developed Deep Search and Tracking Autonomously with Robotics (DeepSTARia), a class of tracking-by-detection algorithms that integrate machine learning models with imaging and vehicle controllers to enable autonomous underwater vehicles to make targeted visual observations of ocean life. We show that these algorithms enable new, scalable sampling strategies that build on traditional operational modes, permitting more detailed (e.g., sharper imagery, temporal resolution) autonomous observations of underwater concepts without supervision and robust long-duration object tracking to observe animal behavior. This integration is critical to scale undersea exploration and represents a significant advance toward more intelligent approaches to understanding the ocean and its inhabitants

Published
2024

16. Figure 2 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

Published
2024
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17. Figure 3 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

Published
2024
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18. Figure 6 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

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2024
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19. Figure 10 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

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2024
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20. Figure 5 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

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2024
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21. Supplementary material 1 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

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2024
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22. Report on the Marine Imaging Workshop 2022

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

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2024
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23. Figure 7 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

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2024
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24. Figure 4 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

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2024
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25. Figure 9 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

Published
2024
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26. Figure 8 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782

Author

Borremans, Catherine, primary, Durden, Jennifer, additional, Schoening, Timm, additional, Curtis, Emma, additional, Adams, Luther, additional, Branzan Albu, Alexandra, additional, Arnaubec, Aurélien, additional, Ayata, Sakina-Dorothée, additional, Baburaj, Reshma, additional, Bassin, Corinne, additional, Beck, Miriam, additional, Bigham, Katharine, additional, Boschen-Rose, Rachel, additional, Collett, Chad, additional, Contini, Matteo, additional, Correa, Paulo, additional, Dominguez-Carrió, Carlos, additional, Dreyfus, Gautier, additional, Duncan, Graeme, additional, Ferrera, Maxime, additional, Foulon, Valentin, additional, Friedman, Ariell, additional, Gaikwad, Santosh, additional, Game, Chloe, additional, Gaytán-Caballero, Adriana, additional, Girard, Fanny, additional, Giusti, Michela, additional, Hanafi-Portier, Mélissa, additional, Howell, Kerry, additional, Hulevata, Iryna, additional, Itiowe, Kiamuke, additional, Jackett, Chris, additional, Jansen, Jan, additional, Karthäuser, Clarissa, additional, Katija, Kakani, additional, Kernec, Maxime, additional, Kim, Gabriel, additional, Kitahara, Marcelo, additional, Langenkämper, Daniel, additional, Langlois, Tim, additional, Lanteri, Nadine, additional, Jianping Li, Claude, additional, Li, Qi-Ran, additional, Liabot, Pierre-Olivier, additional, Lindsay, Dhugal, additional, Loulidi, Ali, additional, Marcon, Yann, additional, Marini, Simone, additional, Marranzino, Ashley, additional, Massot-Campos, Miquel, additional, Matabos, Marjolaine, additional, Menot, Lenaick, additional, Moreno, Bernabé, additional, Morrissey, Marcus, additional, Nakath, David, additional, Nattkemper, Tim, additional, Neufeld, Monika, additional, Obst, Matthias, additional, Olu, Karine, additional, Parimbelli, Alexa, additional, Pasotti, Francesca, additional, Pelletier, Dominique, additional, Perhirin, Margaux, additional, Piechaud, Nils, additional, Pizarro, Oscar, additional, Purser, Autun, additional, Rodrigues, Clara, additional, Ceballos Romero, Elena, additional, Schlining, Brian, additional, Song, Yifan, additional, Sosik, Heidi, additional, Sourisseau, Marc, additional, Taormina, Bastien, additional, Taucher, Jan, additional, Thornton, Blair, additional, Van Audenhaege, Loïc, additional, von der Meden, Charles, additional, Wacquet, Guillaume, additional, Williams, Jack, additional, Witting, Kea, additional, and Zurowietz, Martin, additional

Published
2024
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27. An in situ digital synthesis strategy for the discovery and description of ocean life

Author

Burns, John A., primary, Becker, Kaitlyn P., additional, Casagrande, David, additional, Daniels, Joost, additional, Roberts, Paul, additional, Orenstein, Eric, additional, Vogt, Daniel M., additional, Teoh, Zhi Ern, additional, Wood, Ryan, additional, Yin, Alexander H., additional, Genot, Baptiste, additional, Gruber, David F., additional, Katija, Kakani, additional, Wood, Robert J., additional, and Phillips, Brennan T., additional

Published
2024
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28. DeepSTARia: enabling autonomous, targeted observations of ocean life in the deep sea.

Author

Barnard, Kevin, Daniels, Joost, Roberts, Paul L. D., Orenstein, Eric C., Masmitja, Ivan, Takahashi, Jonathan, Woodward, Benjamin, and Katija, Kakani

Subjects
MARINE biology, MACHINE learning, OCEAN, AUTONOMOUS underwater vehicles, ANIMAL tracks, UNDERWATER exploration
Abstract

The ocean remains one of the least explored places on our planet, containingmyriad life that are either unknown to science or poorly understood. Given the technological challenges and limited resources available for exploring this vast space, more targeted approaches are required to scale spatiotemporal observations and monitoring of ocean life. The promise of autonomous underwater vehicles to fulfill these needs has largely been hindered by their inability to adapt their behavior in real-time based on what they are observing. To overcome this challenge, we developed Deep Search and Tracking Autonomously with Robotics (DeepSTARia), a class of tracking-bydetection algorithms that integrate machine learning models with imaging and vehicle controllers to enable autonomous underwater vehicles to make targeted visual observations of ocean life. We show that these algorithms enable new, scalable sampling strategies that build on traditional operational modes, permitting more detailed (e.g., sharper imagery, temporal resolution) autonomous observations of underwater concepts without supervision and robust long-duration object tracking to observe animal behavior. This integration is critical to scale undersea exploration and represents a significant advance toward more intelligent approaches to understanding the ocean and its inhabitants. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Dynamic robotic tracking of underwater targets using reinforcement learning

Author

Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Masmitjà Rusiñol, Ivan, Martín Muñoz, Mario, O’Reilly, Tom, Kieft, Brian, Palomeras Rovira, Narcís, Navarro Bernabé, Joan, Katija, Kakani, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Masmitjà Rusiñol, Ivan, Martín Muñoz, Mario, O’Reilly, Tom, Kieft, Brian, Palomeras Rovira, Narcís, Navarro Bernabé, Joan, and Katija, Kakani

Abstract

To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future., Funding: The European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie, grant agreement No 893089 (IM). The Spanish Ministerio de Economia y Competitividad under the project SASES, grant agreement No RTI2018-095112-B-I00 (IM). The Spanish Ministerio de Economia y Competitividad under the project BITER-ECO, grant agreement No PID2020-114732RB-C31 (IM, JN). The Spanish Ministerio de Economia y Competitividad under the project BITER-AUV, grant agreement No PID2020-114732RB-C33 (NP)., Peer Reviewed, Postprint (author's final draft)

Published
2023

35. Platform-portable reinforcement learning methods to localize underwater targets

Author

European Commission, Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), David and Lucile Packard Foundation, Masmitja, Ivan, Martin, Mario, O'Reilly, Tom, Palomeras, Narcís, Katija, Kakani, Navarro, Joan, European Commission, Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), David and Lucile Packard Foundation, Masmitja, Ivan, Martin, Mario, O'Reilly, Tom, Palomeras, Narcís, Katija, Kakani, and Navarro, Joan

Abstract

Subsea residential autonomous systems will reduce the cost and CO 2 footprint of future subsea operations. This concept includes subsea docking stations (SDS) and Autonomous Underwater Vehicles (AUVs). To ensure the AUVs are capable of interfacing with the SDS, a driftless and accurate close-range navigation schema is required. Thus, this paper proposes a visual method for navigation utilizing a defined set of ArUco markers printed on the SDS. To increase the accuracy of the system, the set of ArUco markers is defined to a common frame of reference, introducing more points in the camera frame for a more accurate solution to the Perspective-n-Point problem. This makes the system robust against partial occlusion. The paper has also implemented a modified Line of Sight (LOS) schema for the guidance of the vehicle. The results show stable pose estimates at up to 6 meters above the SDS, and successful autonomous docking of an underwater vehicle. The navigational estimates provided by the system get more stable when the vehicle is closer to the SDS

Published
2023

36. Platform-portable reinforcement learning methods to localize underwater targets

Author

Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Masmitjà Rusiñol, Ivan, Martín Muñoz, Mario, O'Reilly, Thomas C., Palomeras Rovira, Narcis, Katija, Kakani, Navarro Bernabé, Joan, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, Masmitjà Rusiñol, Ivan, Martín Muñoz, Mario, O'Reilly, Thomas C., Palomeras Rovira, Narcis, Katija, Kakani, and Navarro Bernabé, Joan

Abstract

In this study, we present a platform-portable deep reinforcement learning method that has been used as a path-planning system to localize underwater objects with autonomous vehicles., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 893089. This work also received financial support from the Spanish Ministerio de Economía y Competitividad (BITERECO: PID2020-114732RBC31). This work acknowledges the ’Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)., Peer Reviewed, Postprint (author's final draft)

Published
2023

37. Evolutionary traces of miniaturization in a giant—Comparative anatomy of brain and brain nerves in Bathochordaeus stygius (Tunicata, Appendicularia)

Author

Zemann, Berit, Le, Mai-Lee Van, Sherlock, Robert, Baum, Daniel, Katija, Kakani, Stach, Thomas, Zemann, Berit, Le, Mai-Lee Van, Sherlock, Robert, Baum, Daniel, Katija, Kakani, and Stach, Thomas

Abstract

Appendicularia comprises 70 marine, invertebrate, chordate species. Appendicularians play important ecological and evolutionary roles, yet their morphological disparity remains understudied. Most appendicularians are small, develop rapidly, and with a stereotyped cell lineage, leading to the hypothesis that Appendicularia derived progenetically from an ascidian-like ancestor. Here, we describe the detailed anatomy of the central nervous system of Bathochordaeus stygius, a giant appendicularian from the mesopelagic. We show that the brain consists of a forebrain with on average smaller and more uniform cells and a hindbrain, in which cell shapes and sizes vary to a greater extent. Cell count for the brain was 102. We demonstrate the presence of three paired brain nerves. Brain nerve 1 traces into the epidermis of the upper lip region and consists of several fibers with some supportive bulb cells in its course. Brain nerve 2 innervates oral sensory organs and brain nerve 3 innervates the ciliary ring of the gill slits and lateral epidermis. Brain nerve 3 is asymmetric, with the right nerve consisting of two neurites originating posterior to the left one that contains three neurites. Similarities and differences to the anatomy of the brain of the model species Oikopleura dioica are discussed. We interpret the small number of cells in the brain of B. stygius as an evolutionary trace of miniaturization and conclude that giant appendicularians evolved from a small, progenetic ancestor that secondarily increased in size within Appendicularia., Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659, David and Lucile Packard Foundation http://dx.doi.org/10.13039/100000008, Peer Reviewed

Published
2023

38. Dynamic robotic tracking of underwater targets using reinforcement learning

Author

European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Masmitja, Ivan, Martin, Mario, O'Reilly, Tom, Kieft, Brian, Palomeras, Narcís, Navarro, Joan, Katija, Kakani, European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), Masmitja, Ivan, Martin, Mario, O'Reilly, Tom, Kieft, Brian, Palomeras, Narcís, Navarro, Joan, and Katija, Kakani

Abstract

To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future

Published
2023

39. Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation

Author

Massachusetts Institute of Technology. Media Laboratory, Crosby, Alison, Orenstein, Eric, Poulton, Susan, Bell, Katherine, Woodward, Benjamin, Ruhl, Henry, Katija, Kakani, Forbes, Angus, Massachusetts Institute of Technology. Media Laboratory, Crosby, Alison, Orenstein, Eric, Poulton, Susan, Bell, Katherine, Woodward, Benjamin, Ruhl, Henry, Katija, Kakani, and Forbes, Angus

Published
2023

41. Platform-portable reinforcement learning methods to localize underwater targets

Author

Masmitjà Rusiñol, Ivan, Martín Muñoz, Mario, O'Reilly, Thomas C., Palomeras Rovira, Narcis, Katija, Kakani, Navarro Bernabé, Joan, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, and Universitat Politècnica de Catalunya. IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group

Subjects
Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC], Reinforcement learning, Aprenentatge per reforç, Vehicles submergibles autònoms, Autonomous underwater vehicles, Informàtica::Robòtica [Àrees temàtiques de la UPC]
Abstract

In this study, we present a platform-portable deep reinforcement learning method that has been used as a path-planning system to localize underwater objects with autonomous vehicles. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 893089. This work also received financial support from the Spanish Ministerio de Economía y Competitividad (BITERECO: PID2020-114732RBC31). This work acknowledges the ’Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S).

Published
2023

43. AI for the Ocean Fish and Squid Detector

Author

Belcher, Byron T., Bower, Eliana H., Burford, Benjamin, Celis, Maria Rosa, Fahimipour, Ashkaan K., Guevara, Isabella L., Katija, Kakani, Manjunath, Anjana, Nelson, Samuel, Olivetti, Simone, Orenstein, Eric, Saleh, Mohamad H., Vaca, Brayan, Valladares, Salma, Hein, Stella A., and Hein, Andrew M.

Subjects
Image Analysis, Machine Learning, Computer Vision, Artificial Intelligence, Image Annotation, Distribution Shift
Abstract

Different models were trained with YOLOv5 on 6 classes using over 5600 annotated images from the FathomNet database collected in the Monterey Bay and surrounding regions of the coastal eastern Pacific. These classes were chosen due to the abundance of annotated images and the local interest in these species. Five classes of squid and fish were used in primary analysis and reserved images of a siphonophore were used in a later analysis. Data are partitioned into domains to examine the effects of distribution shifts on model performance. Partitions were designed to yield similar numbers of annotations for each focal class in each partition. The first is a temporal partition. Annotated images of each of our focal classes are divided into images collected prior to 2012, and images collected from 2012 through the present. This partitioning resulted in Pre 2012 and Post 2012 domains. The second partition is a spatial partition. For each class, images are divided either by depth or by latitude and longitude to ensure that images of each class were divided into distinct spatial “regions,” defined arbitrarily as Region 1, and Region 2. Spatial Partition Region 1 Region 2 Chiroteuthis calyx depth < 450m depth > 450m Dosidicus gigas depth > 500m depth < 500m Gonatus onyx depth < 1000m depth > 1000m Sebastes latitude < 32o latitude > 32o Sebastes diploproa latitude < 36o latitude > 36o Sebastes melanostomus latitude < 36o latitude > 36o Sebastolobus latitude < 38o latitude > 38o These partitions yield four models, Pre 2012, Post 2012, Regiion1, and Region 2. A fifth model consists of data from only two classes, a squid (Chiroteuthis calyx) and a siphonophore (Nanomia bijuga). The goal was to determine whether YOLO could detect and distinguish images of juvenile Chiroteuthis calyx and Nanomia bijuga. Detailed information and code can be found at https://github.com/heinsense2/AIO_CaseStudy., This work was supported by National Science Foundation grant IOS-1855956 to AMH. ( https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855956&HistoricalAwards=false )

Published
2022
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44. Demystifying image-based machine learning: A practical guide to automated analysis of field imagery using modern machine learning tools

Author

Belcher, Byron T., primary, Bower, Eliana H., additional, Burford, Benjamin, additional, Celis, Maria Rosa, additional, Fahimipour, Ashkaan K., additional, Guevara, Isabella L., additional, Katija, Kakani, additional, Khokhar, Zulekha, additional, Manjunath, Anjana, additional, Nelson, Samuel, additional, Olivetti, Simone, additional, Orenstein, Eric, additional, Saleh, Mohamad H., additional, Vaca, Brayan, additional, Valladares, Salma, additional, Hein, Stella A., additional, and Hein, Andrew M., additional

Published
2022
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47. Working with imperfect machines: managing error while using ML in the field

Author

Orenstein, Eric C., Barnard, Kevin, Roberts, Paul L. D., Daniels, Joost, Masmitja, Ivan, Takahashi, Jonathan, Woodward, Benjamin, Raanan, Ben Yair, Kieft, Brian, Hobson, Brett, Katija, Kakani, Orenstein, Eric C., Barnard, Kevin, Roberts, Paul L. D., Daniels, Joost, Masmitja, Ivan, Takahashi, Jonathan, Woodward, Benjamin, Raanan, Ben Yair, Kieft, Brian, Hobson, Brett, and Katija, Kakani

Abstract

Our vast and dynamic ocean is largely undersampled and its inhabitants poorly understood. Imaging systems, supported by machine learning-based automated analysis, promise to help us establish a more consistent presence in this difficult to access habitat. But on-going challenges with ML-systems keep the dream of an ocean teeming with robots capable of autonomously reporting observations or tracking rare individual organisms just out of reach. Indeed, the very signals we wish to measure – rapidly fluctuating, spatially and temporally heterogeneous biological distributions – confound supervised machine learning systems that are encoded with a static view of the world. Bridging the gap between these imperfect systems and deployable instrumentation requires supervised autonomy: keeping a human-in-the-loop to ensure the machine is doing the right thing. Here we describe two supervised autonomy experiments that push the limits of allowing in situ platforms to make decisions base on imagery. In the first, we couple a multi-object detector and a 3D stereo tracker on a Remotely Operated Vehicle to find and track midwater organisms while a human observer confirms the chosen target. In the second, we demonstrate transmitting selected Regions of Interest, chosen by a real time binary detector, from an Autonomous Underwater Vehicle at depth via an acoustic modem. Together, these experiments represent steps toward allowing a computer to trigger a behaviour change based on visual data to track a salient feature. Both fundamentally rely on a human operator to confirm the machine’s choice. Enabling this type of constructive interaction between potentially inaccurate machines and human supervisors is critical to ensure that our in situ systems operate in an efficient and unbiased manner.

Published
2022

48. A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles

Author

European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Generalitat de Catalunya, Masmitja, Ivan, Martin, Mario, Katija, Kakani, Gomáriz, Spartacus, Navarro, Joan, European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Generalitat de Catalunya, Masmitja, Ivan, Martin, Mario, Katija, Kakani, Gomáriz, Spartacus, and Navarro, Joan

Abstract

Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-short baseline systems. Nonetheless, in ROSB target localization methods, the trajectory of the tracking vehicle near the localized target plays an important role in obtaining the best accuracy of the predicted target position. Here, we investigate a Reinforcement Learning (RL) approach to find the optimal path that an autonomous vehicle should follow in order to increase and optimize the overall accuracy of the predicted target localization, while reducing time and power consumption. To accomplish this objective, different experimental tests have been designed using state-of-the-art deep RL algorithms. Our study also compares the results obtained with the analytical Fisher information matrix approach used in previous studies. The results revealed that the policy learned by the RL agent outperforms trajectories based on these analytical solutions, e.g. the median predicted error at the beginning of the target’s localisation is 17% less. These findings suggest that using deep RL for localizing acoustic targets could be successfully applied to in-water applications that include tracking of acoustically tagged marine animals by autonomous underwater vehicles. This is envisioned as a first necessary step to validate the use of RL to tackle such problems, which could be used later on in a more complex scenarios

Published
2022

49. A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles

Author

Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic, Universitat Politècnica de Catalunya. SARTI-MAR - Sistemes d'Adquisició Remota de dades i Tractament de la Informació en el Medi Marí, Masmitjà Rusiñol, Ivan, Martín Muñoz, Mario, Katija, Kakani, Gomáriz Castro, Spartacus, Navarro Bernabé, Joan, Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic, Universitat Politècnica de Catalunya. SARTI-MAR - Sistemes d'Adquisició Remota de dades i Tractament de la Informació en el Medi Marí, Masmitjà Rusiñol, Ivan, Martín Muñoz, Mario, Katija, Kakani, Gomáriz Castro, Spartacus, and Navarro Bernabé, Joan

Abstract

Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-short baseline systems. Nonetheless, in ROSB target localization methods, the trajectory of the tracking vehicle near the localized target plays an important role in obtaining the best accuracy of the predicted target position. Here, we investigate a Reinforcement Learning (RL) approach to find the optimal path that an autonomous vehicle should follow in order to increase and optimize the overall accuracy of the predicted target localization, while reducing time and power consumption. To accomplish this objective, different experimental tests have been designed using state-of-the-art deep RL algorithms. Our study also compares the results obtained with the analytical Fisher information matrix approach used in previous studies. The results revealed that the policy learned by the RL agent outperforms trajectories based on these analytical solutions, e.g. the median predicted error at the beginning of the target’s localisation is 17% less. These findings suggest that using deep RL for localizing acoustic targets could be successfully applied to in-water applications that include tracking of acoustically tagged marine animals by autonomous underwater vehicles. This is envisioned as a first necessary step to validate the use of RL to tackle such problems, which could be used later on in a more complex scenarios., This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 893089. This work also received financial support from the Spanish Ministerio de Economía y Competitividad (SASES: RTI2018-095112-B-I00; BITER-ECO: PID2020-114732RB C31). This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), and from the Generalitat de Catalunya ”Sistemas de Adquisicion Remota de datos y Tratamiento de la Informacion en el Medio Marino (SARTI-MAR)” 2017 SGR 376. We gratefully acknowledge the David and Lucile Packard Foundation., Peer Reviewed, Postprint (author's final draft)

Published
2022

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