The aforementioned data were compiled from the ships transiting the Kattegat Strait between January 1st and March 10th, 2022. AIS data is published as open source in some countries. Two types of data, static and dynamic, are kept in the AIS device:

Static Information : 1. The ship's IMO number 2. The ship's MMSI number 3. The ship's Call Sign 4. The ship's name 5. The ship's type 6. What type of destination this message was received from (like Class A / Class B) 7. Width of ship 8. Length of ship 9. Draft of ship 10. Type of GPS device 11. Length from GPS to bow (Length A) 12. Length from GPS to stern (Size B) 13. Length from GPS to starboard (Size C) 14. Length from GPS to port side (Dimension D)

Dynamic Data: 1. Time information (31/12/2015 in 23:59:59 format) 2. Latitude 3. Longitude 4. Navigational status (For example: 'Fishing', Anchored, etc.) 5. Rate of Turn (ROT) 6. Speed Over Ground (SOG) 7. Course Over Ground (COG) 8. Heading 9. Type of cargo 10. Port of Destination 11. Estimated Time of Arrival (ETA) 12. Data source type, eg. AIS

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.

About Dataset

Context

This dataset contains processed Automatic Identification System (AIS) data for vessel traffic analysis. Unlike raw AIS logs, this version has been heavily pre-processed and feature-engineered for machine learning tasks like ETA Prediction (Regression) and Vessel Movement Classification.

Content

The dataset contains vessel dynamics, physical characteristics, and engineered features calculated relative to destination clusters.

1. Identifiers & Time: * MMSI, IMO, CallSign, VesselName: Unique identifiers for the ships. * BaseDateTime: Timestamp of the recorded position.

2. Kinematics (Movement): * LAT, LON: Current geographical coordinates. * SOG (Speed Over Ground) & SOG_kmh: Speed in knots and converted to km/h. * COG (Course Over Ground) & Heading: Direction of movement and bow orientation. * Speed_Category: Binned speed status (e.g., "Stopped", "Slow").

3. Vessel Physicals: * Length, Width, Draft: Physical dimensions in meters. * VesselType, Status, Cargo: Raw numeric codes defining ship category and state. * Transceiver: Class A or B transceiver type.

4. Engineered / Predictive Features: * dest_cluster: Clustered destination ID (derived from frequent stopping points). * dest_lat, dest_lon: Coordinates of the predicted destination. * dist_km: Calculated distance from current position to destination. * ETA_min, ETA_hours: Estimated Time of Arrival (calculated target variables).

Processing Steps

• Imputation: Missing heading/draft values filled with mean/mode.
• Feature Engineering: Converted SOG to km/h; calculated haversine distance (dist_km) to destination clusters.
• Encoding: Created numerical encodings for categorical fields (Status_enc, VesselType_enc).

The dataset consists of vessel tracking data in the form of AIS observations in the Baltic Sea during years 2017-19. The AIS observations have been enriched with vessel metadata such as power

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.

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...

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

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.

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. 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...

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 m...

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.

📖 Overview

This dataset combines Automatic Identification System (AIS) vessel tracking data with historical weather information to enable advanced maritime analytics, including ETA (Estimated Time of Arrival) prediction, delay classification, and route optimization.

this dataset addresses a critical gap in maritime supply chain intelligence by providing weather-contextualized vessel movement data for machine learning applications.

📊 Feature Dictionary | AIS + Weather Dataset

🔑 Quick Reference

Category	Features	Count
🚢 Vessel ID	MMSI, IMO, CallSign, VesselName	4
📍 Location	LAT, LON, dest_lat, dest_lon, lat_r, lon_r	6
⏱️ Time	BaseDateTime, timestamp_str	2
🧭 Navigation	SOG, COG, Heading, SOG_kmh, dist_km	5
🎯 Target	ETA_min, ETA_hours, Speed_Category	3
🚢 Vessel Info	VesselType, Status, Length, Width, Draft, Cargo, TransceiverClass	7
🔢 Encoded	VesselType_enc, Status_enc, Cargo_enc	3
🌤️ Weather	9 weather features + weather_available	10
🔗 Grouping	dest_cluster, group_id	2
Total		42

🚢 Vessel Identification

Feature	Type	Description	Example	Notes
MMSI	int	Maritime Mobile Service Identity – unique 9-digit vessel identifier	367702220	Primary key for vessel tracking
IMO	str/int	International Maritime Organization number – permanent vessel ID	IMO9221322	May be missing for small vessels
CallSign	str	Radio call sign assigned to vessel	WDI4808	May be missing
VesselName	str	Registered name of the vessel	JOE B WARD	May contain missing values

📍 Geographic Features

Feature	Type	Description	Range	Notes
LAT	float	Vessel latitude (decimal degrees, WGS84)	0.46 to 50.11	Original AIS precision
LON	float	Vessel longitude (decimal degrees, WGS84)	-160.60 to -9.00	Original AIS precision
dest_lat	float	Destination latitude (inferred from AIS destination field)	–	May be approximate
dest_lon	float	Destination longitude (inferred from AIS destination field)	–	May be approximate
lat_r	float	Latitude rounded to 2 decimals (~1km precision)	–	Used for weather grouping
lon_r	float	Longitude rounded to 2 decimals (~1km precision)	–	Used for weather grouping

⏱️ Temporal Features

Feature	Type	Description	Format	Notes
BaseDateTime	datetime	Timestamp of AIS report	YYYY-MM-DD HH:MM:SS	UTC timezone
timestamp_str	str	String representation for API queries	YYYY-MM-DD HH:00	Used for weather fetch

🧭 Navigation & Movement

Feature	Type	Description	Unit	Range	Notes
SOG	float	Speed Over Ground – vessel speed relative to earth	knots	0.0 – 102.3	Raw AIS value
COG	float	Course Over Ground – direction of movement	degrees	0 – 388.8	Raw AIS value
Heading	float	Vessel heading direction (compass)	degrees	0 – 511	511 = not available
SOG_kmh	float	Speed Over Ground converted to km/h	km/h	0.0 – 189.5	Engineered feature
dist_km	float	Great-circle distance to destination	kilometers	0 – 5000+	Haversine formula

🚢 Vessel Characteristics

Feature	Type	Description	Example	Notes
VesselType	int	AIS vessel type code (ITU-R M.1371 standard)	31=Tug, 70=Cargo	See AIS Vessel Types
Status	int	Navigation status code (0-15)	0=Under way, 5=Moored	See AIS Status Codes
Length	float	Vessel length overall	meters	May be 0 if unknown
Width	float	Vessel beam (width)	meters	May be 0 if unknown
Draft	float	Current vessel draft (depth below waterline)	meters	May be 0 if unknown
Cargo	int	Cargo type code (for cargo vessels)	57=Hazardous A	May be missing
TransceiverClass	str	AIS transceiver class: A=Class A, B=Class B	A	Class A = commercial vessels

🌤️ Weather Features (Historical)

Feature	Type	Description	Unit	Source
weather_available	int	Flag: 1=weather data present, 0=missing	binary	Derived
weather_temp_c	float	Air temperature at 2m height	°C	WeatherAPI
weather_humidity	float	Relative humidity	%	Weather...

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 Guards 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 AIS Ship Tracking System market was valued at $3.8 billion in 2025 and is projected to reach $6.9 billion by 2033, growing at 7.8% CAGR.

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.

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.

This dataset fuses Sentinel-2 optical remote sensing images with data from the Automatic Identification System (AIS) for vessels. It contains spatiotemporal information including multi-temporal satellite imagery, vessel positions, speed, course, ship type and other attributes, enabling accurate matching between spatial observations and dynamic trajectories. As a high-quality sample set, it can support inshore vessel monitoring and maritime target recognition. It addresses the limitations of single-source remote sensing data—such as susceptibility to cloud and fog interference and lack of vessel attribute information—and the absence of spatial context in AIS data. The dataset can underpin research in remote sensing-based vessel detection, spatiotemporal registration of multi-source data, abnormal navigation behavior identification, and maritime traffic supervision. It holds significant application value for improving the intelligent level of maritime supervision, ensuring inshore shipping safety, and conducting maritime rights and interests monitoring.

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.