An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis

Schoening, Timm; Köser, Kevin; Greinert, Jens

doi:10.1038/sdata.2018.181

Cited by 24 publications

(17 citation statements)

References 26 publications

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“…Commercial and open source solutions exist for this task but were neglected until now to keep the system simple to setup. Within the full image analysis pipeline, from acquisition to understanding, there are many bottlenecks that require new tools and workflows (Schoening et al, 2018). Acquisition can only be sped-up by multiplying the number of acquisition devices.…”

Section: Discussionmentioning

confidence: 99%

SHiPCC—A Sea-going High-Performance Compute Cluster for Image Analysis

Schoening

2019

Front. Mar. Sci.

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Marine image analysis faces a multitude of challenges: data set size easily reaches Terabyte-scale; the underwater visual signal is often impaired to the point where information content becomes negligible; human interpreters are scarce and can only focus on subsets of the available data due to the annotation effort involved etc. Solutions to speed-up the analysis process have been presented in the literature in the form of semi-automation with artificial intelligence methods like machine learning. But the algorithms employed to automate the analysis commonly rely on large-scale compute infrastructure. So far, such an infrastructure has only been available onshore. Here, a mobile compute cluster is presented to bring big image data analysis capabilities out to sea. The Seagoing High-Performance Compute Cluster (SHiPCC) units are mobile, robustly designed to operate with electrically impure ship-based power supplies and based on off-the-shelf computer hardware. Each unit comprises of up to eight compute nodes with graphics processing units for efficient image analysis and an internal storage to manage the big image data sets. The first SHiPCC unit has been successfully deployed at sea. It allowed us to extract semantic and quantitative information from a Terabyte-sized image data set within 1.5 h (a relative speedup of 97% compared to a single four-core CPU computer). Enabling such compute capability out at sea allows to include image-derived information into the cruise research plan, for example by determining promising sampling locations. The SHiPCC units are envisioned to generally improve the relevance and importance of optical imagery for marine science.

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Section: Discussionmentioning

confidence: 99%

SHiPCC—A Sea-going High-Performance Compute Cluster for Image Analysis

Schoening

2019

Front. Mar. Sci.

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“…In the case of monitoring activities utilising directly collected fauna, from box core, multicore or ROV collection, much of the material will be processed once, by one lab, and then either be destroyed or degraded during the processing steps -preventing further studies. Image data also facilitates the straightforward archiving of collected data (Schoening et al (2018)), for later comparison with subsequent images, which may potentially be collected decades after experimental or industrial disturbance, with the aim of gauging long-term recovery rates. Given the extremely long lifespans of many deep sea fauna (Norse et al (2012); Roark et al (2009)), this is an important consideration when developing monitoring strategies for efficient and useful impact assessment within these ecosystems.…”

Section: Methodologies For Fauna Abundance Assessmentmentioning

confidence: 99%

Megafauna community assessment of polymetallic nodule fields with cameras: Platform and methodology comparison

Schoening¹,

Purser²,

Langenkämper³

et al. 2019

Preprint

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Abstract. With the mining of polymetallic nodules from the deep sea seafloor again approaching commercial viability, decisions must be taken on how to most efficiently regulate and monitor physical and community disturbance in these remote ecosystems. Image based approaches allow non-destructive assessment of larger fauna abundances to be derived from survey data, with repeat surveys of areas possible to allow time series data collection. At time of writing key underwater imaging platforms commonly used to map seafloor fauna abundances are Automated Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs) and towed camera Ocean Floor Observation Systems (OFOSs). These systems are highly customisable, with mounted cameras, illumination systems and deployment protocols rapidly changing over time, and even within survey cruises. In this study 8 image datasets were collected from a discrete area of polymetallic nodule rich seafloor by an AUV and several OFOSs deployed at various altitudes above the seafloor. A fauna identification catalogue was used by 5 annotators to estimate the abundances of 20 fauna categories from the different data sets. Results show that for many categories of megafauna differences in image resolution greatly influenced the estimations of fauna abundance determined by the annotators. This is an important finding for the development of future monitoring legislation for these areas. When and if commercial exploitation of these marine resources commences, to ensure best monitoring practice, unambiguous rules on how camera-based monitoring surveys should be conducted, and with what equipment, must be put in place.

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“…Each image I s i ∈ I s has a width of w i and a height of h i pixels. To apply scale transfer to an image I s i , the scale transfer factor d s→t i is calculated first as defined in (2). Next, each image I s i is scaled to the width w s→t i and height h s→t i as can be seen in (3) and (4), respectively, resulting in the set of scale-transferred images I s→t (see Fig.…”

Section: A Scale Transfermentioning

confidence: 99%

“…Thanks to recent technological advances in high-resolution digital imaging and digital storage technology, mobile marine observation platforms such as autonomous underwater vehicles (AUV) or ocean floor observation systems (OFOS) are capable to acquire large volumes of imaging data in a short time [1]. The sustainable curation and management of terabyte-scale volumes of marine imaging data is a challenge that has only recently been addressed [2].…”

Section: Introductionmentioning

confidence: 99%

“…Common scenarios in marine environmental monitoring and exploration are research cruises, where images of the sea floor are captured during multiple deployments of AUVs or OFOSs. Often the image datasets collected on a single cruise cover a specific geographical area and show similar habitats and OOI [22]- [25]. Between different deployments, the visual properties of the photographed habitats and OOI may change due to different cruising speeds of the observation platforms, different camera gear or different distances of the observation platforms to the sea floor.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Knowledge Transfer for Object Detection in Marine Environmental Monitoring and Exploration

Zurowietz

Nattkemper

2020

IEEE Access

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The volume of digital image data collected in the field of marine environmental monitoring and exploration has been growing in rapidly increasing rates in recent years. Computational support is essential for the timely evaluation of the high volume of marine imaging data, but often modern techniques such as deep learning cannot be applied due to the lack of training data. In this paper, we present Unsupervised Knowledge Transfer (UnKnoT), a new method to use the limited amount of training data more efficiently. In order to avoid time-consuming annotation, it employs a technique we call "scale transfer" and enhanced data augmentation to reuse existing training data for object detection of the same object classes in new image datasets. We introduce four fully annotated marine image datasets acquired in the same geographical area but with different gear and distance to the sea floor. We evaluate the new method on the four datasets and show that it can greatly improve the object detection performance in the relevant cases compared to object detection without knowledge transfer. We conclude with a recommendation for an image acquisition and annotation scheme that ensures a good applicability of modern machine learning methods in the field of marine environmental monitoring and exploration.

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An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis

Cited by 24 publications

References 26 publications

SHiPCC—A Sea-going High-Performance Compute Cluster for Image Analysis

SHiPCC—A Sea-going High-Performance Compute Cluster for Image Analysis

Megafauna community assessment of polymetallic nodule fields with cameras: Platform and methodology comparison

Unsupervised Knowledge Transfer for Object Detection in Marine Environmental Monitoring and Exploration

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