National implementation of a PLatform for ANalysis of SUBSea IMages (PLAN-SUBSIM)

NAISS 2023/7-30



Principal Investigator:

Matthias Obst


Göteborgs universitet

Start Date:


End Date:


Primary Classification:

10611: Ecology

Secondary Classification:

10201: Computer Sciences



The increasing access to autonomously-operated technologies offer vast opportunities to sample large volumes of biological data from the ocean. This is especially the case for high-definition optical imagery coming from remotely operated vehicles (ROV’s), autonomously operating vehicles (AUVs), drones, drop-cameras, camera-traps, and video plankton recorders. The scientific potential of these technologies will change the modus operandi of the ecological research community in the near future as it allows scientists to extract biological information from remote ecosystems with unprecedented quantity and quality. However, these technologies also impose novel demands on ecologists who need to apply tools for data management and processing that are efficient, publicly available and easy to use. Such tools are starting to be developed in the community, but they are seldom connected to e-infrastructures for data management, archiving, and computation. PLAN-SUBSIM will leverage existing methods, knowledge, and infrastructure services in the field of subsea image analysis and implement these for applications in marine science and ecosystem management. Specifically, we plan to develop seamless linkage between existing data archives, thematic eLabs for data processing and machine learning, HPC infrastructure, and thematic data portals and thereby make valuable data and data products available to scientific users. The suggested activities include i) formulation of service specification, ii) implementation trials and pilots, and iii) scaling operations. PLAN-SUBSIM is financed by FORMAS as well as the VINNOVA-funded Ocean Data Factory ( and the VR-funded infrastructure program Swedish Biodiversity Data Infrastructure ( This application is linked to already approved SNIC projects for storage (SNIC 2021/23-745) and AI/ML resources (SNIC 2021/7-161)