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.

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.

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

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.

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.

OCM plans to contract for AIS data per the following description. The United States Automatic Identification System Database contains vessel traffic data for planning purposes within the U.S. coastal waters. The database is composed of 216 self-contained File Geodatabases (FGDB). Each FGDB represents one month of data for a single UTM zone. The UTM zones represented cover the entire United States and include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, and 20. Each FGDB consists of one feature class, four tables, and two relationship classes. The Broadcast point feature class contains the position reports, which have been pre-filtered to a one-minute time step. The Voyage table contains elements of the static data reports that are updated for each ship voyage. The Vessel table contains elements of the static data reports that are specific to a particular vessel. The BaseStations table lists the base stations collecting data for a particular month/UTM zone. The AttributeUnits table contains a list of units for each of the attribute fields in the Broadcast, Voyage, and Vessel tables. The BroadcastHasVessel relationship class relates the broadcast points to the vessel table records. The BroadcastHasVoyage relationship class relates the broadcast points to the voyage table records. The Broadcast feature class and the Voyage, Vessel, and BaseStation tables each contain the UTM zone, year, and month in the file name.

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.

AIS data used in artical "Inland Waterway Ship Path Planning Based on Improved RRT Algorithm"

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

This dataset corresponds to 6 months of AIS data of vessels steaming in the area of the Ushant traffic separation scheme (in Brittany, West of France). This is an area with one of the highest traffic density in the world, with a clear separation scheme with two navigation lanes. Different kinds of vessels are present in the area, from cargos and tankers with high velocity and straight routes to sailing boats or fishing vessels with low speed and different sailing directions. As such, the area is highly monitored to avoid collision or grounding, and a better analysis and understanding of the different ship behaviors is of prime importance.The whole trajectory data set consists in 18,603 trajectories, gathering overall more than 7 millions GPS observations. Only trajectories having more than 30 points were kept, time lag between two consecutive observations ranges between 5 seconds and 15 hours, with 95% of time lags below 3 minutes.Authors would like to thank CLS (Collecte Localisation Satellites) and Erwan Guegueniat for providing the raw data that allowed building this dataset.This work has been supported by DGA through the ANR/Astrid SESAME project (ref: ANR-16-ASTR-0026).

AIS nmea sample dataset used for testing AIS decoding library. This dataset was originally published in https://github.com/aduvenhage/ais-decoder as a sample test dataset. The original dataset can be found at https://github.com/aduvenhage/ais-decoder/blob/master/data/nmea-sample.txt

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.

According to Cognitive Market Research, the global Maritime Solution with Satellite AIS Data Market size is USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 4.70% from 2024 to 2031.

North America held the major market, accounting for more than 40% of global revenue and having a market size of USD XX million in 2024. The market will grow at a compound annual growth rate (CAGR) of 2.9% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD XX Million.
Asia Pacific held a market of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2024 to 2031.
Latin America's market will account for more than 5% of global revenue and be worth USD XX million in 2024, growing at a compound annual growth rate (CAGR) of 4.1% from 2024 to 2031.
The Middle East and Africa held the major market, accounting for around 2% of global revenue and having a market size of USD XX million in 2024. This market will grow at a compound annual growth rate (CAGR) of 4.4% from 2024 to 2031.
The Vessel-Based held the highest Maritime Solution with Satellite AIS Data Market revenue share in 2024.

Key Drivers of Maritime Solution with Satellite AIS Data Market

Insurance and Risk Management to Increase the Demand Globally

The global demand for Maritime Solutions with Satellite AIS Data is poised to witness a significant upswing, driven by the escalating focus on insurance and risk management within the maritime industry. Satellite AIS data plays a pivotal role in providing accurate and real-time information on vessel movements, enabling insurance companies and risk management firms to assess and mitigate potential risks effectively. By leveraging this technology, insurers can enhance the accuracy of underwriting, set more informed premiums, and expedite claims processing through improved incident tracking and analysis. The transparency offered by AIS data contributes to a proactive risk management approach, ultimately reducing the overall risk profile for insurers.

As maritime stakeholders increasingly recognize the instrumental role of AIS data in enhancing safety and operational efficiency, the demand for Maritime Solutions is expected to surge globally, marking a transformative shift in the insurance and risk management landscape within the maritime domain.

Port Efficiency and Management to Propel Market Growth

The Maritime Solution with Satellite AIS Data Market is poised for substantial growth, driven by a heightened emphasis on port efficiency and management. As global trade volumes continue to surge, ports are seeking advanced technologies to optimize vessel traffic, streamline operations, and enhance overall efficiency. Satellite AIS data plays a pivotal role in providing real-time insights into vessel movements, enabling ports to allocate berths more effectively, minimize congestion, and improve resource utilization.

The integration of AIS data with port management systems facilitates a seamless flow of information, allowing authorities to respond promptly to changing conditions and optimize logistics. Recognizing the pivotal role of AIS in transforming port operations, the market is witnessing increased global adoption as ports seek to elevate their competitiveness, reduce turnaround times, and enhance overall performance in an increasingly dynamic maritime environment.

Restraint Factors of Maritime Solution with Satellite AIS Data Market

Limited Satellite Coverage in Remote Areas to Limit the Sales

The Maritime Solution with Satellite AIS Data Market faces a constraint in its potential sales due to the challenge of limited satellite coverage in remote areas. While Satellite AIS provides invaluable real-time tracking capabilities, the efficacy of these solutions can be hampered in regions where satellite visibility is compromised. Remote and high-latitude areas, often characterized by harsh weather conditions or challenging topography, may experience gaps in coverage, limiting the ability to monitor vessel movements comprehensively. This limitation poses challenges for industries operating in such remote maritime zones, potentially impacting the market's sales potential.

The consequence of limited satellite coverage in these areas extends beyond mere inconvenience, affecting the abil...

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.

The size and share of the market is categorized based on Type (Monitor up to 160K Vessels, Monitor up to 200K Vessels, Monitor up to 240k Vessels, Others) and Application (Commercial Transport Operator, Government) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Facing an increasing amount of movements at sea and daily impacts on ships, crew and our global ecosystem, many research centers, international organizations, industrials have favored and developed sensors, detection techniques for the monitoring, analysis and visualization of sea movements. Automatic Identification System (AIS) is one of these electronic systems that enable ships to broadcast their dynamic (position, speed, destination...) and static (name, type, international identifier…) information via radio communications.

Having spatially and temporally aligned maritime dataset relying not only on ships' positions but also on a variety of complementary data sources is of great interest for the understanding of maritime activities and their impact on the environment.

This dataset contains ships' information collected though the Automatic Identification System, integrated with a set of complementary data having spatial and temporal dimensions aligned. The dataset contains four categories of data: Navigation data, vessel-oriented data, geographic data, and environmental data. It covers a time span of six months, from October 1st, 2015 to March 31st, 2016 and provides ships positions within Celtic sea, the Channel and Bay of Biscay (France). The dataset is proposed with predefined integration and querying principles for relational databases. These rely on the widespread and free relational database management system PostgreSQL, with the adjunction of the PostGIS extension, for the treatment of all spatial features proposed in the dataset.

the mediterranean sperm whale population is listed as ‘endangered”. the hellenic trench is the core habitat of the eastern mediterranean sperm whale sub-population that numbers two to three hundred individuals. major shipping routes running on or very close to the 1000 m depth contour along the hellenic trench are causing an unsustainable number of ship-strikes with sperm whales reviewed in this paper. sperm whale sighting and density data were combined with specific information on the vessel traffic in the area (e.g., types of vessels, traffic patterns, speed and traffic density), in order to estimate the risk of a whale/ship interaction. routing options to significantly reduce ship strike risk by a small offshore shift in shipping routes were identified. the overall collision risk for sperm whales in the study area would be reduced by around 70%, while a maximum of 11 nautical miles would be added to major routes and only around 5 nautical miles for the majority of ships. no negative impacts were associated with re-routing by shipping away from sperm whale habitat and there would be additional shipping safety and environmental benefits. a significant contribution to the overall conservation status of the marine natura2000 sites in the area and very important population units of threatened species such as cuvier’s beaked whales, monk seals and loggerhead turtles would be achieved, by the reduction of shipping noise and reduced risk of any oil spills reaching the coasts, which are also important touristic destinations in greece.csv file contains the following headings : mmsi maritime mobile service identity shiptype type of vessel status navigationalstatus speed speed over ground (knots * 10) course course over ground heading heading lon longitude lat latitude timestamp_utc date and time station satellite or terrestrial

AIS data prepared and provided by Statsat AS (Norway) in the framework of the T-SAR project (IKTPLUSS programme on reducing digital vulnerabilities, 10 MNOK from the Research Council of Norway, Norway).

The dataset contains AIS data (satellite + other) on a global coverage for 2020. There is one zip file and its name is the month (01 for January to 12 for December) ending with '.zip' and the zip file contains zipped Comma Separated Values (CSV) files; one per day.

The CSV zipped files are named with the following convention: ais_YYYYMMDD.zip where YYYY is the year (here 2020), MM the month (01 to 12) and DD the day (01 to 31 depending on the month). For instance, ais_20200101.zip contains one single CSV file called ais_20200101.csv that corresponds to the 1st January 2020.

The csv files have one header line:

mmsi;lon;lat;date_time_utc;sog;cog;true_heading;nav_status;rot;message_nr;source

where:

mmii

integer

MMSI number of the vessel (AIS identifier). All records belonging to the same vessel will have the same identifier.

lon

float

Geographical longitude (WGS84) between -180 to 180

lat

float

Geographical latitude (WGS84) between -90 to 90

date_time_utc

datetime

Date and Time (in UTC) when position was recorded by AIS. It is represented as: YYYY-MM-DD HH:MM:SS (for instance 2020-01-01 00:00:00).

sog

float

Speed over ground (knots)

cog

float

Course over ground (degrees)

true_heading

integer

Heading (degrees) of the vessel's hull. A value of 511 indicates there is no heading data.

nav_status

integer

Navigation status according to AIS Specification

rot

integer

rate of turn

message_nr

integer

message number

source

integer

source is the source of AIS data ('g' for ground or 's' for satellite).

One row in the CSV file corresponds to one message.