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.

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.

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.

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.

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.

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.

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.

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

AIS Ship Tracking System Market Outlook

The global AIS Ship Tracking System market size was valued at approximately USD 300 million in 2023 and is forecasted to reach around USD 650 million by 2032, growing at a Compound Annual Growth Rate (CAGR) of around 9%. The significant growth in this market is primarily driven by the increasing need for maritime safety, security, and efficient fleet management.

The growth factors driving the AIS Ship Tracking System market include the rising volume of international seaborne trade and stringent regulations pertaining to maritime safety and environmental protection. The surge in global trade activities has necessitated the adoption of advanced tracking systems to ensure navigational safety and operational efficiency. Furthermore, the implementation of international maritime laws and policies aimed at preventing maritime accidents and environmental hazards has bolstered the demand for AIS ship tracking systems.

Technological advancements in AIS systems have also played a crucial role in market expansion. The integration of satellite AIS systems and real-time data analytics has greatly enhanced the accuracy and reliability of vessel tracking, resulting in higher adoption rates across the maritime industry. Additionally, the development of sophisticated software solutions that offer comprehensive fleet management capabilities, including route optimization, fuel consumption monitoring, and predictive maintenance, has further fueled market growth.

The increasing investments in maritime infrastructure, particularly in emerging economies, are expected to propel the demand for AIS ship tracking systems over the forecast period. Governments and private sector entities are investing heavily in the development of ports, harbors, and other maritime facilities, thereby driving the need for advanced tracking and monitoring solutions. Moreover, the growth of the maritime tourism industry, with an increasing number of cruise ships and recreational vessels, is also contributing to market growth.

The Maritime Traffic System plays a pivotal role in enhancing the safety and efficiency of global shipping operations. By providing comprehensive data on vessel movements and maritime conditions, these systems enable better coordination and management of maritime traffic, reducing the risk of collisions and environmental incidents. As international trade continues to expand, the integration of advanced Maritime Traffic Systems is becoming increasingly crucial for ensuring smooth and secure maritime operations. These systems not only support regulatory compliance but also facilitate the optimization of shipping routes and schedules, contributing to cost savings and improved operational efficiency.

Regionally, North America is anticipated to hold a substantial share of the AIS Ship Tracking System market due to the presence of major maritime trade routes and stringent regulatory frameworks. Europe is also expected to witness significant growth owing to the region's extensive maritime activities and the adoption of advanced tracking technologies. The Asia Pacific region, with its burgeoning maritime trade, is projected to exhibit the highest growth rate during the forecast period. The increasing investments in port infrastructure and the rising demand for maritime security solutions in countries such as China, India, and Japan are key growth drivers in this region.

Component Analysis

The AIS Ship Tracking System market can be segmented by component into hardware, software, and services. The hardware segment includes transceivers, antennas, and other essential equipment required for AIS functionality. The software segment encompasses the applications and platforms used for data collection, analysis, and visualization. The services segment includes installation, maintenance, and training services.

The hardware segment is expected to dominate the market throughout the forecast period due to the continuous demand for transceivers and antennas which are integral to AIS systems. The need for upgrading existing hardware to more advanced and reliable systems is also driving growth in this segment. Technological advancements, such as the development of more compact and efficient transceivers, are further propelling the market.

The software segment is anticipated to witness the highest growth rate, driven by the increasing demand for sophisticated software solutions that o

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. This dataset represents the density of vessel traffic in 2013 for the contiguous United States offshore waters from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

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.

This dataset consists of 107 days of vessel tracking using the Automatic Identification System (AIS) at 1 hour intervals extracted for the Queensland region from the Spatial@AMSA Historic Vessel Tracking website (AMSA 2013). It has been converted to Shapefile format and contains just under 1 million points.

Note: The Spatial@AMSA Historic Vessel Tracking website is no longer available, however similar and more recent data is now available from Spatial@AMSA Vessel Tracking Data website (https://www.operations.amsa.gov.au/Spatial/DataServices/DigitalData).

Vessel tracking data is used to support coastal traffic management, search and rescue response and to meet requirements for safety and protection of the maritime environment. A valuable data set for marine use studies.

The Automatic Identification System (AIS) is an automatic tracking system used on ships and by vessel traffic services (VTS) for identifying and locating vessels by electronically exchanging data with other nearby ships, AIS base stations, and satellites. Each vessel regularly transmits its position ranging from 3 minutes for anchored or moored vessels, to 2 seconds for fast moving or manoeuvring vessels.

This dataset only contains vessel positions at approximately 1 hour internals. Even so each vessel can contribute many data points.

Class A transceivers have been mandated by the International Maritime Organization (IMO) for vessels of 300 gross tonnage and upwards engaged on international voyages, cargo ships of 500 gross tonnage and upwards not engaged on international voyages, as well as passenger ships (more than 12 passengers), irrespective of size. Class A transceivers are stronger, have priority transmissions and transmit more frequently then Class B transceivers.

Class B transceivers provide limited functionality and is intended for non-SOLAS vessels. It is not mandated by the International Maritime Organization (IMO) and has been developed for non-SOLAS commercial and recreational vessels.

The Historic Vessel Tracking Spatial@AMSA website provides a data download of historic vessel positions from April 2009 to current time minus 2 weeks.

The original data was downloaded through the Spatial@AMSA Historic Vessel Tracking Request website (https://www.operations.amsa.gov.au/Spatial/DataServices/CraftTrackingRequest). Due to limitations in the maximum size of the download, the data was requested in 3 day lots, using CSV download format and "Select by State" of QLD. All of the CSV files were compiled together using Notepad++, then loaded into ArcMap using Add Data / Add XY. This was then exported as a shapefile. This resulted in a very large shapefile as each of the columns were made excessively large (254 characters) by this process. To reduce the size of the shapefile a duplicate column was setup for each of the text attributes, except this time the size was set to just fit the data in. The data was copied to the new column using the field calculator and the original field deleted. This process reduced the database file of the shapefile from 1.4 GB to 170 MB.

Note that due to a limitation of the shapefile format the high resolution time stamps of vessels did not come out in the shapefile. This information is however available in the CSV version.

This dataset only contains information available from the Historic Vessel Tracking Spatial@AMSA website and only contains the ships course, speed, heading, ship name and if it is piloted. It does not contain information about the ship's length, breadth, cargo or status.

The e-Atlas has not confirmed what types of vessels this dataset contains however it probably contains most AIS Class A and some Class B vessels.

Format:

This dataset is available in Comma Separated Value (CSV) (80 MB) and Shapefile format (178 MB).

Data Dictionary:

CSV file: Not a lot is known about the fields of this dataset as they come from AMSA undocumented. Values in brackets are typical values. - CourseDegrees: (0, 331.3, 184) - CraftType: (Vessel) - FixTime: (9/05/2013 21:13) - Heading: Heading of the vessel in integer degrees, sometime there is no value (284) - IsPilotedVoyage: Boolean (FALSE, TRUE) - Latitude, Longitude: Vessel position in decimal degrees (-23.75092333, 151.1676367) - Name: Vessel id (NOMADIC MILDE, SMIT KULLAROO, HYUNDAI SUCCESS) - ReportingAgentName: (AMSA, AIS) - Speed: unknown units (0, 13.2)

Shapefile: These are the same values as for the CSV but renamed to fit limitations of shapefiles: CouseDegr, Heading, Latitude, Longitude, Speed, NameB, IsPiloted, FixTimeB, Reporting.

References:

Australian Maritime Safety Authority. (2013) Historical Vessel Tracking. Spatial@AMSA, [CSV data file]. License: Creative Commons Attribution-Noncommercial 3.0 Australia. Available: https://www.operations.amsa.gov.au/Spatial/DataServices/CraftTrackingRequest. Accessed 6 September 2013

Automatic identification system (AIS) data are used to identify and track vessels for various purposes (primarily navigational safety). These data can be used to study vessel traffic, such as ship routing and speed over ground (SOG). Source data were obtained from the United States Coast Guard Navigation Center (USCG NAVCEN) for the period from June 2008 to December 2015. Derived data resulting from the processing of the source data are described here. This data set presents annual raster data (1 square kilometer grid size) off California from 2008-2015 for cumulative ship traffic density (kilometers/day) and mean SOG (knots; distance-weighted). The universe of data is limited to vessels with a length greater than or equal to 80 meters. The data are analyzed in three groups: freight vessels (container, general cargo, bulk carrier, refrigerated cargo, vehicle carrier, etc.), tanker vessels (crude oil, chemical/products, liquid petroleum gas, etc.) and all vessels (the previously noted vessels, plus passenger vessels and other vessel classes). The data are contained in a file geodatabase format as raster data sets. Metadata for the overall data set are contained at the level of the file geodatabase. The data were generated and used for a research article (Moore et al. 2018): Moore, T.J., Redfern, J.V., Carver, M., Hastings, S., Adams, J.D., Silber, G.K., 2018. Exploring Ship Traffic Variability off California. Ocean and Coastal Management. https://doi.org/10.1016/j.ocecoaman.2018.03.010 See this manuscript for more information on the data description, issues, and processing methods.

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

The Automatic Identification System (AIS) allows vessels to share identification, characteristics, and location data through self-reporting. This information is periodically broadcast and can be received by other vessels with AIS transceivers, as well as ground or satellite sensors. Since the International Maritime Organisation (IMO) mandated AIS for vessels above 300 gross tonnage, extensive datasets have emerged, becoming a valuable resource for maritime intelligence.

Maritime collisions occur when two vessels collide or when a vessel collides with a floating or stationary object, such as an iceberg. Maritime collisions hold significant importance in the realm of marine accidents for several reasons:

Injuries and fatalities of vessel crew members and passengers.

Environmental effects, especially in cases involving large tanker ships and oil spills.

Direct and indirect economic losses on local communities near the accident area.

Adverse financial consequences for ship owners, insurance companies and cargo owners including vessel loss and penalties.

As sea routes become more congested and vessel speeds increase, the likelihood of significant accidents during a ship's operational life rises. The increasing congestion on sea lanes elevates the probability of accidents and especially collisions between vessels.

The development of solutions and models for the analysis, early detection and mitigation of vessel collision events is a significant step towards ensuring future maritime safety. In this context, a synthetic vessel proximity event dataset is created using real vessel AIS messages. The synthetic dataset of trajectories with reconstructed timestamps is generated so that a pair of trajectories reach simultaneously their intersection point, simulating an unintended proximity event (collision close call). The dataset aims to provide a basis for the development of methods for the detection and mitigation of maritime collisions and proximity events, as well as the study and training of vessel crews in simulator environments.

The dataset consists of 4658 samples/AIS messages of 213 unique vessels from the Aegean Sea. The steps that were followed to create the collision dataset are:

Given 2 vessels X (vessel_id1) and Y (vessel_id2) with their current known location (LATITUDE [lat], LONGITUDE [lon]):

Check if the trajectories of vessels X and Y are spatially intersecting.

If the trajectories of vessels X and Y are intersecting, then align temporally the timestamp of vessel Y at the intersect point according to X’s timestamp at the intersect point. The temporal alignment is performed so the spatial intersection (nearest proximity point) occurs at the same time for both vessels.

Also for each vessel pair the timestamp of the proximity event is different from a proximity event that occurs later so that different vessel trajectory pairs do not overlap temporarily.

Two csv files are provided. vessel_positions.csv includes the AIS positions vessel_id, t, lon, lat, heading, course, speed of all vessels. Simulated_vessel_proximity_events.csv includes the id, position and timestamp of each identified proximity event along with the vessel_id number of the associated vessels. The final sum of unintended proximity events in the dataset is 237. Examples of unintended vessel proximity events are visualized in the respective png and gif files.

The research leading to these results has received funding from the European Union's Horizon Europe Programme under the CREXDATA Project, grant agreement n° 101092749.

This dataset contains the anonymised transit line data used to create the UK shipping density grid produced as part of MMO1066. See AIS data processing methodology developed by ABPmer under MMO project number 1066, entitled Mapping UK Shipping Density and Routes from AIS Open Source Data and Methods

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.

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 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 | MetadataThis item is curated by the MarineCadastre.gov team. Find more information at marinecadastre.gov.