Vessel traffic data, or Automatic Identification System (AIS) data, are collected by the U.S. Coast Guard through an onboard navigation safety device that transmits and monitors the location and characteristics of large vessels in U.S. and international waters in real time. In the U.S., the Coast Guard and commercial vendors collect AIS data, which can also be used for a variety of coastal planning purposes.The Bureau of Ocean Energy Management (BOEM) and the National Oceanic and Atmospheric Administration (NOAA) have worked jointly to repurpose and make available some of the most important records from the U.S. Coast Guard’s national network of AIS receivers. Information such as location, time, ship type, speed, length, beam, and draft have been extracted from the raw data and prepared for analyses in desktop GIS software.Vessel tracks show the location and characteristics of commercial, recreational, and other marine vessels as a sequence of positions transmitted by AIS. AIS signals are susceptible to interference, and this can result in a gap within a vessel track. Vessels can have one or more tracks of any length. Furthermore, tracks will not necessarily start or stop at a well-defined port, or when a vessel is not in motion.The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a certain geographic area and are an efficient and spatially unbiased indicator of vessel traffic. These tracks are used to build respective AIS Vessel Transit Counts layers, summarized at a 100-meter grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell.This item is curated by the MarineCadastre.gov team. Find more information at marinecadastre.gov.

Vessel traffic data or Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal management purposes. NOAA and BOEM have worked jointly to make available these data from the U.S. Coast Guards national network of AIS receivers. The original records were filtered to a one-minute frequency rate and were subsetted to depict the location and description of vessels broadcasting within the Exclusive Economic Zone. MarineCadastre.gov AIS data are divided by month and Universal Transverse Mercator (UTM) zone.

The automatic identification system (AIS) is a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real time. In the U.S., the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to add value and make available some of the most important historical records from the U.S. Coast Guard's national network of AIS receivers. This data set represents annual vessel transit counts summarized at a 100-meter by 100-meter geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100-meter grid cell.Direct data download | Metadata

A vessel track shows the location and characteristics of commercial and recreational boats as a sequence of positions transmitted by an Automatic Identification System (AIS). AIS signals are susceptible to interference and this can result in a gap within a vessel track. The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a certain geographic area. The vessel track positions in this data set are collected and recorded from land-based antennas as part of a national network operated by the U.S. Coast Guard.

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.

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].

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.

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.

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.

The schema of the dataset is provided below:

· t: the time at which the message was received (UTC)

· shipid: the anonymized id of the ship

· lon: the longitude of the current ship position

· lat: the latitude of the current ship position

· heading: (see: https://en.wikipedia.org/wiki/Course_(navigation))

· course: the direction in which the ship moves (see: https://en.wikipedia.org/wiki/Course_(navigation))

· speed: the speed of the ship (measured in knots)

· shiptype: AIS reported ship-type

· destination: AIS reported destination

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.

This dataset contains vessel traffic data within the United States Exclusive Economic Zone (US EEZ). Data were collected from onboard navigation safety devices that transmit and monitor the location and characteristics of large vessels that transited U.S waters. The dataset is composed of vessel traffic heatmap grids that are segmented by region, ship type, month, and year, and describe aggregate traffic information extracted from the raw AIS data. The grids are 500 meter resolution and in an Albers Equal Area projection.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. This dataset represents annual vessel transit counts summarized at a 100 m by 100 m geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100 m grid cell.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning. In 2023 the process was applied to the years 2013 through to 2022 and were made available using the same processes that were applied to the original 2019 datasets.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. Information such as location, time, ship type, length, width, and draft have been extracted from the raw data and prepared as track lines for analyses in desktop GIS software. The data represented in this dataset is a subset of the 2011 Gulf of Mexico Vessel Traffic showing only fishing vessel traffic.

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Terrestrial vessel automatic identification system (AIS) data was collected around Ålesund, Norway in 2020, from multiple receiving stations with unsynchronized clocks. Features are 'mmsi', 'imo', 'length', 'latitude', 'longitude', 'sog', 'cog', 'true_heading', 'datetime UTC', 'navigational status', and 'message number'. Compact parquet files can be turned into data frames with python's pandas library. Data is irregularly sampled because of the navigational status. The preprocessing script for training the machine learning models can be found here. There you will find gathered dozen of trainable models and hundreds of datasets. Visit this website for more information about the data. If you have additional questions, please find our information in the links below:

Luka Grgičević

Ottar Laurits Osen

Ship position data from a satellite-based Automatic Identification System (AIS) were obtained jointly by PacIOOS (J. Potemra), SOEST/ORE of the University of Hawaii (E. Roth), and the Papahanaumokuakea Marine National Monument (PNMN) (D. Graham) through a one-time purchase from ORBCOMM LLC. The purchase agreement was made in late 2012 and was for a 30-by-30 degree section of historical AIS data that included the region of the Mariana Islands. The data include AIS long and unchecked reports for a one year period: August 2011 through mid-August 2012. The raw, monthly GPS files were locally converted to NetCDF for the PacIOOS data servers. Due to vendor constraints, release of the raw data is limited.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning. In 2023 the process was applied to the years 2013 through to 2022 and were made available using the same processes that were applied to the original 2019 datasets.

These data are a spatially explicit representation of monthly shipping intensity in the Pacific Arctic region from January 1, 2015 to December 31, 2020. We calculated shipping intensity based on Automatic Identification System (AIS) data, a type of Global Positioning System (GPS) transmitter required by the International Maritime Organization on all ships over 300 gross tonnes on an international voyage, all cargo ships over 500 gross tonnes, and all passenger ships. We used AIS data received by the exactEarth satellite constellation (64 satellites as of 2020), ensuring spatial coverage regardless of national jurisdiction or remoteness. Our analytical approach converted raw AIS input into monthly hex datasets. We first filtered raw AIS messages to remove spurious records and GPS errors, then joined remaining vessel positional records with static messages including descriptive attributes. We further categorized these messages into one of four general ship types (cargo; tanker; fishing; and other). For the vector dataset, we spatially intersected AIS messages with a hexagon (hex) grid and calculated the number of unique ships, the number of unique ships per day (summed over each month), and the average and standard deviation of the speed over ground. We calculated these values for each month for all vessels as well as vessels subdivided by ship type and for messages from vessels greater than 65 feet long and traveling at greater than 10 knots. These monthly datasets provide a critical snapshot of dynamic commercial and natural systems in the Pacific Arctic region. Recent declines in sea ice have lengthened the duration of the shipping season and have expanded the spatial coverage of large vessel routes, from the Aleutian Islands through the Bering Strait and into the southern Chukchi Sea. As vessel traffic has increased, so has exposure to the myriad environmental risks posed by large ships, including oil spills, underwater noise pollution, large cetacean ship-strikes, and discharges of pollutants. This dataset provides scientific researchers, local community members, mariners, and decision-makers with a quantitative means to evaluate the distribution and intensity of shipping across space and through time. In addition to these hex data, we also produced data products in 25- and 10-km raster format as well as a 1-km coastal data subset. To find these products, search for “North Pacific and Arctic Marine Vessel Traffic Dataset” in the Arctic Data Center’s data repository.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines for each vessel. Vessels can have one or more tracks of any length, and can be separated by gaps due to intermittent loss of the AIS signal. Tracks will not necessarily start or stop at a well defined port, or when a vessel is not in motion. Vessel tracks are an efficient and spatially unbiased indicator of vessel traffic.

Dataset

This dataset was created by Dhiraj Patra

Contents

Kongsfjorden Vessel presence derived from AIS data 2017-2018

Quality

Data from the Automatic Identification System (AIS) for vessels was obtained from The Norwegian Coastal Administration. Three different distance buffers (10, 25 and 50 km) were plotted around Kongsfjorden AURAL location. AIS location points contained information such as the Maritime Mobile Service Identity (MMSI, conveying the ship ID), position and time. These were combined with the distance buffers, and the points falling on land, related to buoys or not relatable to vessels, were eliminated (QGIS 3.16 Hannover). Then, csv files from the resulting interceptions were exported and analyzed in R (R version 4.0.5) to calculate the number of vessels per day in each area.

Variables: Date: date Vessel_10: Number of vessels within 10 km radius from the recorder per day Vessel_25: Number of vessels within 25 km radius from the recorder per day Vessel_50: Number of vessels within 50 km radius from the recorder per day V10: Number of vessels within 10 km radius from the recorder per day V25: Number of vessels between 10 to 25 km radius from the recorder per day V50: Number of vessels between 25 to 50 km radius from the recorder per day Acoustic: Number of acoustic detections of vessels per day

Ship traffic for the State of Hawaii, identifying the number of times a vessel occupied each square kilometer during the period 2008-2009. The Automatic Identification System (AIS) is an internationally-recognized shipboard broadcast system that communicates information to shore-based stations and other AIS-equipped ships. The U.S. Coast Guard (USCG) has developed rules applicable to both U.S. and foreign vessels that require owners and operators of most commercial vessels to install and use AIS to increase security and safety of maritime transportation. PacIOOS obtained AIS data from the USCG Nationwide AIS (NAIS) project. While specific times for ship locations were redacted, the data represent a cumulation over the two-year period 2008-2009 from which ship frequency was computed at 1-km resolution.