These instruments are capable of collecting many hundreds of thousands of high-quality seafloor images in one expedition, which are essential in surveying unexplored areas of the seafloor, assessing ecosystem health, and for monitoring the impacts of stressors such as climate change, pollution and fishing activity. However, the challenge of seafloor and habitat surveying does not stop at image collection: these images need to be processed and analysed – a task that demands an enormous amount of specialist expertise and time. The development of machine learning algorithms and artificial intelligence can assist with this task, by using automated image recognition to identify seafloor habitats and animal species, as well as creating 3D reconstructions of seafloor features such as hydrothermal vent chimneys to examine how they change over time. Not only can machine learning techniques greatly speed up the image processing element of ecosystem mapping, the latest developments aim to enable local-scale observations to be combined with other seafloor information such as bathymetry and oceanographic observations to predict and thus map habitat and species distribution at much bigger scales. This information is essential for understanding basin-scale impacts of environmental change without the vast expense and time associated with traditional research vessel-based surveys.
This satellite event will showcase a number of new marine imaging technologies developed and trialled in the iAtlantic project, demonstrating how these instruments contribute to our knowledge and understanding of the marine environment, and how new low-cost, easy-to-deploy instruments can enable much wider access to marine environmental information and help facilitate better marine monitoring and management, especially in developing countries without access to research vessel facilities. We will also demonstrate novel image processing techniques that enable us to visualise marine ecosystems in new ways, and how artificial intelligence and machine learning is fast becoming an important new tool in understanding the impacts of global change on marine ecosystems.
This event is open to all, particularly those with an interest in marine environmental surveying and monitoring, ecosystem assessment, and the innovative use of artificial intelligence and modelling techniques in scaling up local observations to generate a bigger-picture view of the distribution of ecosystems and habitats across ocean basins. The technology exhibited in this session is relevant for all sea basins and at a range of water depths. It is our hope that showcasing these instruments and techniques will expand access to such technology, broaden the scientific discourse and collaboration on improving such techniques, and also stimulate ideas and discussion on potential new applications and uses.
If you missed the live event, you can watch the session recording below…
17:30 | Murray Roberts, iAtlantic Coordinator Welcome; Introduction to iAtlantic |
17:40 | Veerle Huvenne, National Oceanography Centre UK The Big Picture: Using imagery in marine science |
17:50 | Danielle De Jonge, Heriot-Watt University Taking the bait: monitoring scavengers using a camera lander |
18:00 | Touria Bajjouk, Ifremer Seeing the deep seafloor with a hyperspectral camera |
18:10 | Carlos Dominguez Carrio, IMAR The Azor Drift-Cam – a new, low-cost camera system for marine science |
18:20 | Audience Q&A |
18:35 | Timm Schoening, GEOMAR Using machine learning and AI to advance basin-scale science |
18:45 | Loic Van Audenhaege, Ifremer New dimensions in photogrammetry |
18:55 | Jose Manuel Gonzalez, IEO Predictive habitat modelling |
19:05 | Q&A with audience; discussion |
19:20 | Session wrap up |
Murray is the iAtlantic Coordinator and Professor of Marine Biology at the University of Edinburgh’s School of GeoSciences where he leads the Changing Oceans research group. His research on cold-water corals and deep-sea biology has taken him to sites off the UK, Norway, Ireland and the SE United States in order to advance understanding of the biology and ecology of cold-water corals and provide the information needed for their long-term management and conservation. Murray has led or participated in 23 offshore research cruises.
Veerle is Principal Researcher at the National Oceanography Centre, UK, where she coordinates the Seafloor & Habitat Mapping team. She has more than 20 years of experience in the fields of habitat mapping and sediment dynamics, aiming to develop an understanding of complex deep-sea environments. Veerle has extensive expertise working with new technologies and marine robotic systems such as AUVs and ROVs, and often works closely with the engineering teams on the development of new sensor or vehicle capabilities.
Danielle obtained her MSc in Marine Biology from the University of Groningen (the Netherlands) and now works as a PhD researcher in the Deep-Sea Ecology and Biogeochemistry research group at Heriot-Watt University. As iAtlantic fellow she is studying soft-sediment ecosystem function with autonomous seafloor landers, studying respiration rates, nutrient cycling, scavenging activity, and food-web dynamics. Additionally, she’s involved in an experiment to study soft-sediment ecosystem functioning under future climate scenarios.
Touria joined the DYNECO (Dynamics of Coastal Ecosystems) research unit at IFREMER to coordinate projects in support of public policies. During her career, Touria has developed expertise in the field of optical image processing and GIS for the spatial characterisation of benthic habitats and their ecological status assessment.
Carlos is a junior researcher at the Okeanos Research Center of the University of the Azores. His research mainly focuses in better understanding the ecology and distribution of deep-sea benthic communities using underwater video images. He has been involved in several national and international research projects to explore shelf, canyon and seamount habitats of the Mediterranean and the Atlantic Ocean, having participated in a large number of research expeditions. Carlos has also worked on the development of methodologies to facilitate the annotation and analysis of video images, and more recently has co-led the development of low-cost imaging systems for the exploration of deep-sea habitats, such as the Azor drift-cam and stereo-BRUVs.
Timm is an early career researcher at GEOMAR in Germany. His research is to transform imagery into scientific data. He applies methods of machine learning and image processing to monitor the oceans, with a focus on the exploration and exploitation of big data archives of 2D and 3D images of benthic and pelagic images. He also goes to sea and deploys deep-diving robots to acquire new high-resolution imagery. Timm particularly likes to work at the interface between different disciplines: biology, geology, etc. to provide method-expertise on machine learning to various natural science partners.
Loic is a PhD student at Ifremer in France. His research aims to characterise the temporal dynamics and the spatial distribution of the fauna inhabiting the hydrothermal vent field of Lucky Strike (Azores, Portugal). Loic participates in the annual MoMARSAT research expeditions to the Mid-Atlantic Ridge in July 2020, where he is responsible for the image acquisition used to map habitats and distribution of vent fauna, and also assists with deploying environmental monitoring technologies around the vent site.
José is permanent researcher at the Centro Nacional Instituto Español de Oceanografía (CNIEO), Spain, where he works on the management of benthic ecosystems. During the last 15 years his work has been focused on understanding the drivers (natural and anthropogenic) of benthic biodiversity, with a focus on the application of distribution models. He has used these models to map the distribution of habitat forming species, essential fish habitats and more recently to forecast the impact of climate change on the suitability of benthic species, using a mix of statistical techniques and working at very different geographical scales.
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This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 818123 (iAtlantic). This output reflects only the author’s view and the European Union cannot be held responsible for any use that may be made of the information contained therein.