Gridmapping the northern plains of Mars: Geomorphological, Radar and Water-Equivalent Hydrogen results from Arcadia Plantia

A project of mapping ice-related landforms was undertaken to understand the role of sub-surface ice in the northern plains. This work is the first continuous regional mapping from CTX (“ConTeXt Camera”, 6 m/pixel; Malin et al., 2007) imagery in Arcadia Planitia along a strip 300 km across stretching from 30°N to 80°N centred on the 170° West line of longitude. The distribution and morphotypes of these landforms were used to understand the permafrost cryolithology. The mantled and textured signatures occur almost ubiquitously between 35° N and 78° N and have a positive spatial correlation with inferred ice stability based on thermal modelling, neutron spectroscopy and radar data. The degradational features into the LDM (Latitude Dependent Mantle) include pits, scallops and 100 m polygons and provide supporting evidence for sub-surface ice and volatile loss between 35-70° N in Arcadia with the mantle between 70-78° N appearing much more intact. Pitted terrain appears to be much more pervasive in Arcadia than in Acidalia and Utopia suggesting that the Arcadia study area had more wide-spread near-surface sub-surface ice, and thus was more susceptible to pitting, or that the ice was less well-buried by sediments. Correlations with ice stability models suggest that lack of pits north of 65-70° N could indicate a relatively young age (~1Ma), however this could also be explained through regional variations in degradation rates. The deposition of the LDM is consistent with an airfall hypothesis however there appears to be substantial evidence for fluvial processes in southern Arcadia with older, underlying processes being equally dominant with the LDM and degradation thereof in shaping the landscape

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

Towards automated sample collection and return in extreme underwater environments

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Billings, G., Walter, M., Pizarro, O., Johnson-Roberson, M., & Camilli, R. Towards automated sample collection and return in extreme underwater environments. Journal of Field Robotics, 2(1), (2022): 1351–1385, https://doi.org/10.55417/fr.2022045.In this report, we present the system design, operational strategy, and results of coordinated multivehicle field demonstrations of autonomous marine robotic technologies in search-for-life missions within the Pacific shelf margin of Costa Rica and the Santorini-Kolumbo caldera complex, which serve as analogs to environments that may exist in oceans beyond Earth. This report focuses on the automation of remotely operated vehicle (ROV) manipulator operations for targeted biological sample-collection-and-return from the seafloor. In the context of future extraterrestrial exploration missions to ocean worlds, an ROV is an analog to a planetary lander, which must be capable of high-level autonomy. Our field trials involve two underwater vehicles, the SuBastian ROV and the Nereid Under Ice (NUI) hybrid ROV for mixed initiative (i.e., teleoperated or autonomous) missions, both equipped seven-degrees-of-freedom hydraulic manipulators. We describe an adaptable, hardware-independent computer vision architecture that enables high-level automated manipulation. The vision system provides a three-dimensional understanding of the workspace to inform manipulator motion planning in complex unstructured environments. We demonstrate the effectiveness of the vision system and control framework through field trials in increasingly challenging environments, including the automated collection and return of biological samples from within the active undersea volcano Kolumbo. Based on our experiences in the field, we discuss the performance of our system and identify promising directions for future research.This work was funded under a NASA PSTAR grant, number NNX16AL08G, and by the National Science Foundation under grants IIS-1830660 and IIS-1830500. The authors would like to thank the Costa Rican Ministry of Environment and Energy and National System of Conservation Areas for permitting research operations at the Costa Rican shelf margin, and the Schmidt Ocean Institute (including the captain and crew of the R/V Falkor and ROV SuBastian) for their generous support and making the FK181210 expedition safe and highly successful. Additionally, the authors would like to thank the Greek Ministry of Foreign Affairs for permitting the 2019 Kolumbo Expedition to the Kolumbo and Santorini calderas, as well as Prof. Evi Nomikou and Dr. Aggelos Mallios for their expert guidance and tireless contributions to the expedition

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Southern Ocean warming: Increase in basal melting and grounded ice loss

We apply a global finite element sea ice/ice shelf/ocean model (FESOM) to the Antarctic marginal seas to analyze projections of ice shelf basal melting in a warmer climate. The model is forced with the atmospheric output from two climate models: (1) the Hadley Centre Climate Model (HadCM3) and (2) Max Planck Institute’s ECHAM5/MPI-OM. Results from their 20th-century simulations are used to evaluate the modeled present-day ocean state. Sea-ice coverage is largely realistic in both simulations. Modeled ice shelf basal melt rates compare well with observations in both cases, but are consistently smaller for ECHAM5/MPI-OM. Projections for future ice shelf basal melting are computed using atmospheric output for IPCC scenarios E1 and A1B. While trends in sea ice coverage, ocean heat content, and ice shelf basal melting are small in simulations forced with ECHAM5 data, a substantial shift towards a warmer regime is found in experiments forced with HadCM3 output. A strong sensitivity of basal melting to increased ocean temperatures is found for the ice shelves in the Amundsen Sea. For the cold-water ice shelves in the Ross and Weddell Seas,decreasing convection on the continental shelf in the HadCM3 scenarios leads to an erosion of the continental slope front and to warm water of open ocean origin entering the continental shelf. As this water reaches deep into the Filchner-Ronne Ice Shelf (FRIS) cavity, basal melting increases by a factor of three to six compared to the present value of about 100 Gt/yr. Highest melt rates at the deep FRIS grounding line causes a retreat of > 200km, equivalent to an land ice loss of 110 Gt/yr

Bayesian geoacoustic parameters inversion for multi-layer seabed in shallow sea using underwater acoustic field

Seabed geoacoustic parameters play an important role in underwater acoustic channel modeling. Traditional methods to determine these parameters, for example, drilling, are expensive and are being replaced by acoustic inverse technology. An inversion method based on Bayesian theory is presented to derive the structure and geoacoustic parameters of a layered seabed in a shallow sea. The seabed was considered a layered elastic medium. The objective of this research was to use the sound pressure detected by underwater acoustic sensors at different positions and to use nonlinear Bayesian inversion to estimate the geoacoustic parameters and their uncertainties in the multi-layer seabed. Specifically, the thickness, density, compression wave speed, shear wave speed, and the attenuation of these two wave speeds were determined. The maximum a posterior (MAP) model and posterior probability distribution of each parameter were estimated using the optimized simulated annealing (OSA) and Metropolis-Hastings sampling (MHS) methods. Model selection was carried out using the Bayesian information criterion (BIC) to determine the optimal model that thoroughly explained the experimental data for different parameterizations. The results showed that the OSA is much more capable of delivering high-accuracy results in multi-layer seabed models. The compression wave speed and shear wave speed were less uncertain than the other parameters, and the parameters in the upper layer had less uncertainty than those in the lower layer

Applications of Side Scan and Parametric Echosounders for Mapping Shallow Seagrass Habitats and Their Associated Organic Carbon

Despite a global valuation of $1.9 trillion seagrass habitats world-wide are in decline—directly impacting the large soil carbon stocks associated with seagrasses. Many methods exist to measure the health of seagrass habitats, yet few apply to shallow coastal ecosystems. Those that do lack spatial resolution (satellite surveys) or do not provide continuous data across large areas (point-based surveys). Furthermore, carbon content of these ecosystems is largely limited to destructive and time-consuming soil core sampling. Side scan and parametric acoustics represent a unique technological opportunity to study habitat coverage and carbon content of vegetated coastal habitats (\u3c 3 m depth). This study presents proof of concept for applications of recreational side scan and parametric sub-bottom profiling sonars in mapping both habitat coverage and organic carbon distribution in shallow seagrass habitats, and explores how these methods might be improved in future applications

Detailed temperature mapping-Warming characterizes archipelago zones

Rapidly warming shallow archipelago areas have the best energetic options for high ecological production. We analyzed and visualized the spring and summer temperature development in the Finnish coastal areas of the Northern Baltic Sea. Typical for the Baltic is a high annual periodicity and variability in water temperatures. The maximum difference between a single day average temperatures across the study area was 28.3 °C. During wintertime the littoral water temperature can decrease below zero in outer archipelago or open water areas when the protective ice cover is not present and the lowest observed value was −0.5 °C. The depth and exposition are the most important variables explaining the coastal temperature gradients from the innermost to the outermost areas in springtime when water is heated by increasing solar radiation. Temperature differs more within coastal area than between the basins. Water temperature sum was highest in innermost areas, lowest in open water areas and the variation in daily averages was highest in the middle region. At the end of the warming period, the difference in surface water temperatures between the innermost and outermost areas had diminished at the time when the cooling began in August–September. These clear temperature gradients enabled us use the cumulative water temperature to classify the coastal zones in a biologically sensible manner into five regions. Our study shows a novel approach to study detailed spatial variations in water temperatures. The results can further be used, for example, to model and predict the spatial distribution of aquatic biota and to determine appropriate spatio-temporal designs for aquatic biota surveys. The new spatial knowledge of temperature regions will also help the evaluation of possible causes of larger scale climatological changes in a biological context including productivity.Peer reviewe

An Unsupervised Approach to Modelling Visual Data

For very large visual datasets, producing expert ground-truth data for training supervised algorithms can represent a substantial human effort. In these situations there is scope for the use of unsupervised approaches that can model collections of images and automatically summarise their content. The primary motivation for this thesis comes from the problem of labelling large visual datasets of the seafloor obtained by an Autonomous Underwater Vehicle (AUV) for ecological analysis. It is expensive to label this data, as taxonomical experts for the specific region are required, whereas automatically generated summaries can be used to focus the efforts of experts, and inform decisions on additional sampling. The contributions in this thesis arise from modelling this visual data in entirely unsupervised ways to obtain comprehensive visual summaries. Firstly, popular unsupervised image feature learning approaches are adapted to work with large datasets and unsupervised clustering algorithms. Next, using Bayesian models the performance of rudimentary scene clustering is boosted by sharing clusters between multiple related datasets, such as regular photo albums or AUV surveys. These Bayesian scene clustering models are extended to simultaneously cluster sub-image segments to form unsupervised notions of “objects” within scenes. The frequency distribution of these objects within scenes is used as the scene descriptor for simultaneous scene clustering. Finally, this simultaneous clustering model is extended to make use of whole image descriptors, which encode rudimentary spatial information, as well as object frequency distributions to describe scenes. This is achieved by unifying the previously presented Bayesian clustering models, and in so doing rectifies some of their weaknesses and limitations. Hence, the final contribution of this thesis is a practical unsupervised algorithm for modelling images from the super-pixel to album levels, and is applicable to large datasets

Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions.Comment: 23 page