Satellite imagery has become a fundamental part for maritime monitoring and safety. Correctly estimating a ship's identity is a vital tool. We present a method based on facial recognition for identifying ships in satellite images. A large ship dataset is constructed from Sentinel-2 multispectral images and annotated by matching to the Automatic Identification System. Our dataset contains 7.000 unique ships, for which a total of 16.000 images are acquired. The method uses a convolutional neural network to extract a feature vector from the ship images and embed it on a hypersphere. Distances between ships can then be calculated via the embedding vectors. The network is trained using a triplet loss function, such that minimum distances are achieved for identical ships and maximum distances to different ships. Comparing a ship image to a reference set of ship images yields a set of distances. Ranking the distances provides a list of the most similar ships. The method correctly identifies a ship on average 60 % of the time as the first in the list. Larger ships are easier to identify than small ships, where the image resolution is a limitation.
Background: Autonomous ships have the potential to increase operational efficiency and reduce carbon footprints through technology and innovation. However, there is no comprehensive literature review of all the different types of papers related to autonomous ships, especially with regard to their integration with ports. This paper takes a systematic review approach to extract and summarize the main topics related to autonomous ships in the fields of container shipping and port management. Methods: A machine learning method is used to extract the main topics from more than 2000 journal publications indexed in WoS and Scopus. Results: The research findings highlight key issues related to technology, cybersecurity, data governance, regulations, and legal frameworks, providing a different perspective compared to human manual reviews of papers. Conclusions: Our search results confirm several recommendations. First, from a technological perspective, it is advised to increase support for the research and development of autonomous underwater vehicles and unmanned aerial vehicles, establish safety standards, mandate testing of wave model evaluation systems, and promote international standardization. Second, from a cyber–physical systems perspective, efforts should be made to strengthen logistics and supply chains for autonomous ships, establish data governance protocols, enforce strict control over IoT device data, and strengthen cybersecurity measures. Third, from an environmental perspective, measures should be implemented to address the environmental impact of autonomous ships. This can be achieved by promoting international agreements from a global societal standpoint and clarifying the legal framework regarding liability in the event of accidents.
Non-indigenous species (NIS) pose a major threat to biodiversity and the functioning and services of ecosystems. Despite their rapid spread in coastal waters worldwide, biotic invasions are widely disregarded in marine conservation planning. To guide conservation actions, a better understanding of the underlying mechanisms determining the success of NIS are therefore needed. Here we develop a combined modelling approach to identify the key drivers and community assembly processes determining the occurrence of invasive benthic invertebrates, using Danish coastal waters as a case study. To reflect factors affecting the introduction, establishment and spread of NIS throughout the area, we compiled long-term monitoring data on NIS, as well as information on commercial shipping, environmental conditions and estimates of larvae settling densities derived from drift model simulations informed by species traits. We then applied a set of species distribution models to identify the key drivers determining the occurrence of NIS. Our results demonstrate a significant positive effect of vessel activity, a negative effect of depth and bottom salinity, as well as a positive effect of the simulated settling densities on the probability of presence. Taken together, our results highlight the role of commercial shipping, habitat characteristics and passive advection of early-life stages on the presence of NIS. Our combined modelling approach provide improved process understanding on the key community assembly processes determining the presence of NIS and may serve to guide monitoring, management and conservation planning in order to limit future invasions and their negative consequences on coastal ecosystems.
This study investigates the appropriate port governance model for implementation of green port management (GPM) practices. Relying on social systems engineering principles, we propose a multi-criteria decision-making (MCDM) framework considering four port governance models and five major GPM practice indicators. We validate the MCDM framework using survey data collected from top management executives of three ports in the Indian Ocean Rim — Bangladesh, Sri Lanka and Tanzania. We compare the Analytic Network Process (ANP) method with more recently developed Best-Worst Method (BWM) in analysis of the MCDM problem of finding the right port governance model for GPM. We collect data using the ANP and BWM survey in January 2019 and August 2019, respectively, from the same respondents. While participating in the study in January 2019, the respondents did not know that they would respond to the same MCDM problem using a different model, which corresponds to a repeated measures experimental design. In both analyses, we find that increasing privatization in port governance would enhance the implementation of GPM practices. Our study furthermore suggests that BWM is a reliable MCDM method with greater applicability than ANP, as it requires significantly lower number of judgement comparisons.
Entrepreneurship is often understood as an individualistic endeavour. This article investigates how cultural communities shape entrepreneurial activity through the process of envisioning competing imagined futures. By deploying a microhistorical approach, it explores a public debate about the transition from sail to steam in a late nineteenth-century Danish maritime community. In the debate, local actors evaluated and negotiated future entrepreneurial actions as embedded in existing norms, interpretations of the past, and socio-technical systems rather than independent, non-conformist ventures. The article demonstrates the potential role of community when we attempt to understand better how entrepreneurs construct and dispute over imagined futures.
The purpose of this paper is to describe the impact assessment of a mandatory operational goal-based short-term measure to reduce green house gas (GHG) emissions from ships. The specific measure has been proposed by Denmark and other co-sponsors in the context of the relevant discussion at the International Maritime Organization (IMO) and in particular the so-called Initial IMO Strategy. The IMO is a specialized United Nations agency that regulates shipping. The Initial IMO Strategy, adopted in 2018, has been the most recent major international environmental agreement on how to reduce GHG emissions from ships at a global level. The central research question in this paper is to ascertain the potential impacts of the aforementioned measure to least developed countries (LDCs) and small island developing states (SIDS). There are concerns that such states may be negatively impacted, or even disproportionately negatively impacted, by whatever measure is decided by the IMO. After gaps in the literature and data are identified, our methodology develops a list of potential negative impacts, and looks at a set of factors that may influence these impacts. Then, we discuss how the goal-based measure may impact LDCs/SIDs as regards each of the identified negative impacts. The analysis argues that for LDCs and SIDS a risk for negative and disproportionately negative impacts exists. The only negative impact of which both the probability and the consequence are considered high is the difficulty to finance retrofitting of old ships or investment in new ships. As such, this is likely a disproportionally negative impact. At the same time, the degree of share (or responsibility) of the goal-based measure with respect to such potential negative impacts, vis-à-vis the share of other factors contributing to these impacts, cannot be precisely ascertained, even though we conjecture this share to be low.
The maritime industry is a crucial hard-to-abate sector that is expected to depend on high-energy density renewable liquid fuels in the future. Traditionally, decarbonization pathways have been assessed assuming exogenous cost trajectories for renewable liquid fuels based on an exogenous learning curve. While past studies have looked at the impact of endogenizing learning curves for a specific technology utilizing linear approximation, a fully endogenous direct non-linear implementation of learning curves in a detailed sectoral model (maritime industry) that explores dynamics concerning sensitive parameters does not yet exist. Here, we apply an open-source optimization model for decarbonizing the maritime industry and further develop the model by encompassing a nonconvex mixed-integer quadratically constrained programming approach to analyze the impact of endogenized learning curves for renewable fuel costs following an experience curve approach. We find that global greenhouse gas emissions are significantly lower (up to 25% over a 30 year horizon) when utilizing endogenously modeled prices for renewable fuels compared to commonly used exogenous learning frameworks. Furthermore, we find that conventional modeling approaches overestimate the cost of climate mitigation, which can have significant policy implication related to carbon pricing and fuel efficiency requirements. In a broader context, this emphasizes the potential opportunities that can be achieved if policymakers and companies accelerate investments that drive down the costs of renewable technologies efficiently and thus trigger endogenous experience-based learning in real life.
The offshore oilfields in the North Sea area are increasingly employed for projects beyond oil production, like carbon capture and storage (CCS). Still, the fossil fuel production from mature fields is significant. It has raised environmental concerns associated with discharging produced waters (PW) and drilling mud into the sea. These discharges, which may be highly saline and contain production chemicals, vary significantly in metals and particulate content. Due to density and release depth, the plume is assumed to sink towards the seafloor. Also, a single oilfield can input up to 7.5 tons of Ba, 675 kg of Fe, and 619 kg of P into the water column through PW. Therefore, this study investigates the impact of these discharges on seafloor sediments around two Danish oilfields, assesses the mobility of metals within these sediments, and evaluates the environmental status. PW samples were collected at the discharge outlets from the platforms. Sediment cores were taken near the two oil platforms and from control sites. Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and an optimized BCR sequential extraction, we analyzed the composition and distribution of 24 elements in sediment samples. The results revealed significant differences in total extracted concentrations between sediments near the platforms and those from distant locations and stratigraphically older samples, with relevant levels of Br, Ba, and Sn near the platforms (averaged 14, 27, and 0.1 ppb, respectively). Sediment quality indices showed considerable enrichment and geo-accumulation of toxic metals, particularly at one of the platform sites. However, cumulative indices did not display significant pollution anomalies. Therefore, our findings suggest that oil extraction activities may increase the availability of toxic metals in nearby sediments, potentially impacting marine ecosystems.
Offshore wind power offers a viable solution to the challenge of reducing fossil fuel dependency. However, certain offshore wind projects encounter challenges in meeting expected returns, particularly over the medium to long term. This study addresses the discrepancy between assumed and actual cost behaviors in techno-economic assessments of wind farm projects. The present study evaluates their impact of operational loss trends (eg increased failure rates, aging, potential curtailment) on project viability through a comprehensive techno-economic assessment. To this end, key metrics including Net Present Value and Levelized Cost of Energy, complemented by stochastic analyzes are explored through Monte Carlo Simulation and sensitivity analysis. Results indicate that costs may exceed those of the reference scenario by up to 21.6% in the worst-case scenario, highlighting the critical need for proactive monitoring and management of operational losses.
The impact of the growing cruise ship industry on air quality levels was investigated at the port of Copenhagen, Denmark. In 2018, 345 cruise ships visited Copenhagen, emitting 291 tons of NOx near the city centre. A spatiotemporal cruise ship emission inventory was developed for 2018 based on port list information, engine data, main and auxiliary engine power functions, and NOx emission factors, and was implemented in the OML-Multi atmospheric dispersion model. Evident plume effects from the cruise ships, which were traced by introducing the concept of likely concentration contribution, were obtained in the modelled and measured concentrations at Langelinie Quay, which is the busiest cruise ship terminal in Copenhagen port. Hourly peak values of NOx well above 200 μg m−3 were obtained at the top of a residential building at Langelinie Quay. The emissions from cruise ships were increasing the annual concentration of NO2 in the port area by up to 31% at ground level, and 86% 50 m above the ground in comparison to the urban background level. No exceedance of the European annual limit value of NO2 was obtained. The short-term impact of cruise ships was more pronounced with local exceedances of the hourly European limit value for NO2. Increasing cruise ship activity in Copenhagen port leads to air quality deterioration on short time scales with implications for human health.