ALBERT

Description: This is an A.I. - based workflow for detecting megabenthic fauna from a sequence of underwater optical images. The workflow (semi) automatically generates weak annotations through the analysis of superpixels, and uses these (refined and semantically labeled) annotations to train a Faster R-CNN model. Currently, the workflow has been tested with images of the Clarion-Clipperton Zone in the Pacific Ocean

Description: The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in AI-SCW have been implemented using the python programming language, specifically using libraries such as scikit-image for image processing, scikit-learn for machine learning and dimensionality reduction, keras for computer vision with deep learning, and matplotlib for generating visualizations. Therefore, AI-SCW modularized implementation allows users to accomplish a variety of underwater computer vision tasks, which include: detecting laser points from the underwater images for use in scale determination; performing contrast enhancement and color normalization to improve the visual quality of the images; semi-automated generation of annotations to be used downstream during supervised classification; training a convolutional neural network (Inception v3) using the generated annotations to semantically classify each image into one of pre-defined seafloor habitat categories; evaluating sampling strategies for generation of balanced training images to be used for fitting an unsupervised k-means classifier; and visualization of classification results in both feature space view and in map view geospatial co-ordinates. Thus, the workflow is useful for a quick but objective generation of image-based seafloor habitat maps to support monitoring of remote benthic ecosystems.

Description: Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically based on acoustic methods, and ground truthing through direct sampling and optical imaging. With the increasing capabilities to record high-resolution underwater images, manual approaches for analyzing these images to create seafloor classifications are no longer feasible. Automated workflows have been proposed as a solution, in which algorithms assign pre-defined seafloor categories to each image. However, in order to provide consistent and repeatable analysis, these automated workflows need to address e.g., underwater illumination artefacts, variances in resolution and class-imbalances, which could bias the classification. Here, we present a generic implementation of an Automated and Integrated Seafloor Classification Workflow (AI-SCW). The workflow aims to classify the seafloor into habitat categories based on automated analysis of optical underwater images with only minimal amount of human annotations. AI-SCW incorporates laser point detection for scale determination and color normalization. It further includes semi-automatic generation of the training data set for fitting the seafloor classifier. As a case study, we applied the workflow to an example seafloor image dataset from the Belgian and German contract areas for Manganese-nodule exploration in the Pacific Ocean. Based on this, we provide seafloor classifications along the camera deployment tracks, and discuss results in the context of seafloor multibeam bathymetry. Our results show that the seafloor in the Belgian area predominantly comprises densely distributed nodules, which are intermingled with qualitatively larger-sized nodules at local elevations and within depressions. On the other hand, the German area primarily comprises nodules that only partly cover the seabed, and these occur alongside turned-over sediment (artificial seafloor) that were caused by the settling plume following a dredging experiment conducted in the area.

Description: We provide a sequence of analysis-ready optical underwater images from the Clarion-Clipperton Zone (CCZ) of the Pacific Ocean. The images were originally recorded using a towed camera sledge that photographed a seabed covered with polymetallic manganese-nodules, at an average water depth of 4,250 meters. The original degradation in visual quality and inconsistent scale among individual raw images due to different altitude implies that they are not scientifically comparable in their original form. Here, we present analysis-ready images that have already been pre-processed to account for this degradation. We also provide accompanying metadata for each image, which includes their geographic coordinates, depth of the seafloor, absolute scale (cm/pixel), and seafloor habitat class obtained from a previous study. The provided images are thus directly usable by the marine scientific community e.g., to train machine learning models for seafloor substrate classification and megafauna detection.

Description: Recent advances in optical underwater imaging technologies enable the acquisition of huge numbers of high-resolution seafloor images during scientific expeditions. While these images contain valuable information for non-invasive monitoring of megabenthic fauna, flora and the marine ecosystem, traditional labor-intensive manual approaches for analyzing them are neither feasible nor scalable. Therefore, machine learning has been proposed as a solution, but training the respective models still requires substantial manual annotation. Here, we present an automated image-based workflow for Megabenthic Fauna Detection with Faster R-CNN (FaunD-Fast). The workflow significantly reduces the required annotation effort by automating the detection of anomalous superpixels, which are regions in underwater images that have unusual properties relative to the background seafloor. The bounding box coordinates of the detected anomalous superpixels are proposed as a set of weak annotations, which are then assigned semantic morphotype labels and used to train a Faster R-CNN object detection model. We applied this workflow to example underwater images recorded during cruise SO268 to the German and Belgian contract areas for Manganese-nodule exploration, within the Clarion–Clipperton Zone (CCZ). A performance assessment of our FaunD-Fast model showed a mean average precision of 78.1% at an intersection-over-union threshold of 0.5, which is on a par with competing models that use costly-to-acquire annotations. In more detail, the analysis of the megafauna detection results revealed that ophiuroids and xenophyophores were among the most abundant morphotypes, accounting for 62% of all the detections within the surveyed area. Investigating the regional differences between the two contract areas further revealed that both megafaunal abundance and diversity was higher in the shallower German area, which might be explainable by the higher food availability in form of sinking organic material that decreases from east-to-west across the CCZ. Since these findings are consistent with studies based on conventional image-based methods, we conclude that our automated workflow significantly reduces the required human effort, while still providing accurate estimates of megafaunal abundance and their spatial distribution. The workflow is thus useful for a quick but objective generation of baseline information to enable monitoring of remote benthic ecosystems.

Description: Modern digital scientific workflows - often implying Big Data challenges - require data infrastructures and innovative data science methods across disciplines and technologies. Diverse activities within and outside HGF deal with these challenges, on all levels. The series of Data Science Symposia fosters knowledge exchange and collaboration in the Earth and Environment research community. We invited contributions to the overarching topics of data management, data science and data infrastructures. The series of Data Science Symposia is a joint initiative by the three Helmholtz Centers HZG, AWI and GEOMAR Organization: Hela Mehrtens and Daniela Henkel (GEOMAR)

Description: This dataset comprises a sequence of analysis-ready seafloor optical images of the Clarion-Clipperton Zone (CCZ) in the Pacific Ocean. These images have been pre-processed and are directly usable by the marine imaging community. The raw images were acquired along 12 OFOS (camera) deployment tracks, during SONNE expeditions SO268/1 and SO268/2 to the Mn-nodule covered deep seabed areas of the CCZ in the Pacific Ocean; the raw images have been published here https://doi.org/10.1594/PANGAEA.935856. We subjected these raw images through a series of transformations aimed at correcting for the degradation in visual quality that is typical of underwater images of the deep sea. In particular, the transformations we applied to the raw images include: correction for illumination drop-off radially from the image center towards the edges; contrast enhancements though contrast limited adaptive histogram equalization; color normalization to correct for uneven scene brightness among individual images; and finally, standardization of the varying spatial footprint size due to the inability of the imaging platform (OFOS) to maintain a consistent altitude above the seafloor. Therefore, the provided images are ready to be used in seafloor substrate classification studies.

Description: This dataset comprises a sequence of analysis-ready seafloor optical images of the Clarion-Clipperton Zone (CCZ) in the Pacific Ocean. These images have been pre-processed and are directly usable by the marine imaging community. The raw images were acquired along 12 OFOS (camera) deployment tracks, during SONNE expeditions SO268/1 and SO268/2 to the Mn-nodule covered deep seabed areas of the CCZ in the Pacific Ocean; the raw images have been published here https://doi.org/10.1594/PANGAEA.935856. We subjected these raw images through a series of transformations aimed at correcting for the degradation in visual quality that is typical of underwater images of the deep sea. In particular, the transformations we applied to the raw images include: correction for illumination drop-off radially from the image center towards the edges; contrast enhancements though contrast limited adaptive histogram equalization; color normalization to correct for uneven scene brightness among individual images; and finally, standardization of the varying spatial footprint size due to the inability of the imaging platform (OFOS) to maintain a consistent altitude above the seafloor. Therefore, the provided images are ready to be used in seafloor substrate classification studies.

Description: This dataset comprises a sequence of analysis-ready seafloor optical images of the Clarion-Clipperton Zone (CCZ) in the Pacific Ocean. These images have been pre-processed and are directly usable by the marine imaging community. The raw images were acquired along 12 OFOS (camera) deployment tracks, during SONNE expeditions SO268/1 and SO268/2 to the Mn-nodule covered deep seabed areas of the CCZ in the Pacific Ocean; the raw images have been published here https://doi.org/10.1594/PANGAEA.935856. We subjected these raw images through a series of transformations aimed at correcting for the degradation in visual quality that is typical of underwater images of the deep sea. In particular, the transformations we applied to the raw images include: correction for illumination drop-off radially from the image center towards the edges; contrast enhancements though contrast limited adaptive histogram equalization; color normalization to correct for uneven scene brightness among individual images; and finally, standardization of the varying spatial footprint size due to the inability of the imaging platform (OFOS) to maintain a consistent altitude above the seafloor. Therefore, the provided images are ready to be used in seafloor substrate classification studies.

Description: This dataset comprises a sequence of analysis-ready seafloor optical images of the Clarion-Clipperton Zone (CCZ) in the Pacific Ocean. These images have been pre-processed and are directly usable by the marine imaging community. The raw images were acquired along 12 OFOS (camera) deployment tracks, during SONNE expeditions SO268/1 and SO268/2 to the Mn-nodule covered deep seabed areas of the CCZ in the Pacific Ocean; the raw images have been published here https://doi.org/10.1594/PANGAEA.935856. We subjected these raw images through a series of transformations aimed at correcting for the degradation in visual quality that is typical of underwater images of the deep sea. In particular, the transformations we applied to the raw images include: correction for illumination drop-off radially from the image center towards the edges; contrast enhancements though contrast limited adaptive histogram equalization; color normalization to correct for uneven scene brightness among individual images; and finally, standardization of the varying spatial footprint size due to the inability of the imaging platform (OFOS) to maintain a consistent altitude above the seafloor. Therefore, the provided images are ready to be used in seafloor substrate classification studies.