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Dataset results
11 results for “vessel tracking”
Data from: Monitoring temporal and spatial trends of illegal and legal fishing in Canada's marine conservation areas using vessel tracking datasets
<p>Expansion of marine conservation areas (CA) necessitates resource-efficient and achievable strategies for monitoring and evaluation of ongoing fishing activity at national levels. To demonstrate and explore such a strategy, we conducted the first extensive analysis of fishing activity within Canada's static, geographically defined marine CAs with fishing regulations (n = 264 areas). We used eight years of Automatic Identification System data to estimate fishing effort across three oceans and conducted temporal and spatial comparisons specific to each CA's regulations and enactment date. We addressed questions on CA effectiveness, fishing displacement, fishing the line behavior, and relationships between fishing activity and spatial CA attributes. We estimated 22,000 hours of fishing activity within CAs after enactments, 22% of which was identified as illegal. CA effectiveness appeared to be lowest for Atlantic CAs based on illegal fishing effort density within CAs. Fishing displacement and fishing the line was generally not apparent as buffer areas around CAs tended to already have higher fishing effort prior to enactments. CA effectiveness and responses to CAs varied considerably, as was visualized using timeseries plots and maps developed for each CA. Our evaluation of a nation's full suite of CAs provides managers with a foundation and approach for continued monitoring and reporting.</p>
Data from: Monitoring temporal and spatial trends of illegal and legal fishing in Canada's marine conservation areas using vessel tracking datasets
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The mysterious oil spill in the northeastern coast of Brazil: tracking offshore seawater and the need for improved vessel facilities
<p>PAHs and aliphatics data obtained in seawater from NE Brazil</p>
Data from: Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries
Recent technological developments facilitate the collection of location data from fishing vessels at an increasing rate. The development of low-cost electronic systems allows tracking of small-scale fishing vessels, a sector of fishing fleets typically characterised by many, relatively small vessels. The imminent production of large spatial datasets for this previously data-poor sector, creates a challenge in terms of data analysis. Several methods have been used to infer the spatial distribution of fishing activities from positional data. Here, we compare five approaches using either vessel speed, or speed and turning angle, to infer fishing activity in the Scottish inshore fleet. We assess the performance of each approach using observational records of true vessel activity. Although results are similar across methods, a trip-based Gaussian mixture model provides the best overall performance and highest computational efficiency for our use-case, allowing accurate estimation of the spatial distribution of active fishing (97% of true area captured). When vessel movement data can be validated, we recommend assessing the performance of different methods. These results illustrate the feasibility of designing a monitoring system to efficiently generate information on fishing grounds, fishing intensity, or monitoring of compliance to regulations at a nationwide scale in near-real time.
Data from: Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries
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Single Ground Based AIS Receiver Vessel Tracking Dataset
<p>Nowadays, a multitude of tracking systems produce massive amounts of maritime data on a daily basis. The most commonly used is the Automatic Identification System (AIS), a collaborative, self-reporting system that allows vessels to broadcast their identification information, characteristics and destination, along with other information originating from on-board devices and sensors, such as location, speed and heading. AIS messages are broadcast periodically and can be received by other vessels equipped with AIS transceivers, as well as by on the ground or satellite-based sensors. </p> <p>Since becoming obligatory by the International Maritime Organisation (IMO) for vessels above 300 gross tonnage to carry AIS transponders, large datasets are gradually becoming available and are now being considered as a valid method for maritime intelligence [4].There is now a growing body of literature on methods of exploiting AIS data for safety and optimisation of seafaring, namely traffic analysis, anomaly detection, route extraction and prediction, collision detection, path planning, weather routing, etc., [5]. </p> <p>As the amount of available AIS data grows to massive scales, researchers are realising that computational techniques must contend with difficulties faced when acquiring, storing, and processing the data. Traditional information systems are incapable of dealing with such firehoses of spatiotemporal data where they are required to ingest thousands of data units per second, while performing sub-second query response times. </p> <p>Processing streaming data seems to exhibit similar characteristics with other big data challenges, such as handling high data volumes and complex data types. While for many applications, big data batch processing techniques are sufficient, for applications such as navigation and others, timeliness is a top priority; making the right decision steering a vessel away from danger, is only useful if it is a decision made in due time. The true challenge lies in the fact that, in order to satisfy real-time application needs, high velocity, unbounded sized data needs to be processed in constraint, in relation to the data size and finite memory. Research on data streams is gaining attention as a subset of the more generic Big Data research field.</p> <p>Research on such topics requires an uncompressed unclean dataset similar to what would be collected in real world conditions. This dataset contains all decoded messages collected within a 24h period (starting from 29/02/2020 10PM UTC) from a single receiver located near the port of Piraeus (Greece). All vessels identifiers such as IMO and MMSI have been anonymised and no down-sampling procedure, filtering or cleaning has been applied.</p> <p> </p> <p>The schema of the dataset is provided below:</p> <p>· t: the time at which the message was received (UTC)</p> <p>· shipid: the anonymized id of the ship</p> <p>· lon: the longitude of the current ship position</p> <p>· lat: the latitude of the current ship position</p> <p>· heading: (see: <a href="https://en.wikipedia.org/wiki/Course_(navigation)">https://en.wikipedia.org/wiki/Course_(navigation)</a>)</p> <p>· course: the direction in which the ship moves (see: <a href="https://en.wikipedia.org/wiki/Course_(navigation)">https://en.wikipedia.org/wiki/Course_(navigation)</a>)</p> <p>· speed: the speed of the ship (measured in knots)</p> <p>· shiptype: AIS reported ship-type</p> <p>· destination: AIS reported destination</p>
SPURS-2 research vessel along track WAMOS wave radar data for the second R/V Revelle cruise in the E. Tropical Pacific
The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The WaMoS wave radar instrument was available during the second R/V Revelle cruise of SPURS-2. WaMoS is a radar-based wave and surface current monitoring system providing wave field imagery and station time series or along track data series for key wave parameter in near near-real time. The single resulting SPURS-2 WaMos data file contains along track wave measurement from the R/V Revelle over the duration of this cruise (5 Oct. to 16 Nov. 2017) for the following essential wave field parameters: wave period, wave length, and wave direction, as well as surface current speed and direction.
SPURS-2 research vessel along track SEA-POL rain radar imaging data for E. Tropical Pacific R/V Revelle-2 cruise
The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. The SEA-POL rain radar instrument was employed over the period 22 Oct.-10 Nov. 2017 during the second SPURS-2 R/V Revelle cruise. SEA-POL (seagoing-polarimetric radar) is a C-band, Doppler polarimetric radar system providing 240-degree sector coverage centered on the ships bow via its 1-degree beam width antenna. SEA-POL was used primarily to map rainfall in SPURS-2. The resulting dataset is a series of gridded netCDF data files for a 20 day period at 5-20 minute intervals comprised of rain rate and rain accumulation fields.
Combining Truck and Vessel Tracking Data to Estimate Performance and Impacts of Inland Waterway Ports
<p>The purpose of this project is to estimate the performance of multi-modal supply chains that use inland waterway ports. This is accomplished by developing a method to fuse publicly available datasets including truck and marine vessel tracking data and lock performance data. The study builds on a growing body of research related to multi-modal freight performance measurement, specifically freight fluidity measures. Freight fluidity measurement attempts to capture freight system performance from a multi-modal supply chain perspective. In this study, we effectively combine marine Automatic Identification System (AIS) data with truck Global Positioning System (GPS) data. Both data sources track vessel and vehicle movements and can be used to determine measures such as travel times, dwell times, and other freight activity characteristics.</p> <p>Two models are developed. These are referred to as the Multi-Commodity Assignment Problem (GMAP) and GMAP +. The GMAP model quantifies annualized commodities transloaded at inland waterway port terminals by fusing two mode-specific datasets, truck GPS and marine AIS. The GMAP+ model then assigns commodity flows to vessel trips. Additionally, as a data product, the GMAP and GMAP+ models are used to generate catchment area maps that depict water and truck flows for inland waterway ports in Arkansas. </p> <p> </p>
Triage of Patients With Acute Ischemic Stroke Due to Large Vessel Occlusions-1 (TRACK-LVO-1)
ClinicalTrials.gov study NCT05885178. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Triage of Patients Presenting Beyond 24 Hours With Acute Ischemic Stroke Due to Large Vessel Occlusions (TRACK-LVO Late)
ClinicalTrials.gov study NCT06200753. IPD Sharing: NO. Countries: 1. Publications: 0.
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