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).

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, max speed and draft. The data has been collected for master’s thesis work and the data has been splitter into training and validation sets. The AIS observations do not cover all months of the collection period. The observations are sorted by vessel mmsi. Each observation contains information of timestamp, mmsi, lat, lon, speed (meters per second), course (degrees), heading (degrees), turnrate (degrees per minute), breadth (meters), vessel_type, vessel_max_speed (meters per second), draft (meters), power, dwt (tons) and iceclass.

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.

Dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information

Abstract:This layer an aggregation of annual vessel traffic from 2020. The Vessel Tracks have been derived from data sourced from the Australian Maritime Safety Authority. The Craft Tracking System (CTS) and Mariweb are AMSA’s vessel traffic databases. They collect vessel traffic data from a variety of sources, including terrestrial and satellite shipborne Automatic Identification System (AIS) data sources. This dataset has been built from AIS data extracted from CTS, and it contains vessel traffic data for the year 2020. The dataset covers the extents of Australia’s Search and Rescue Region. Each point within the dataset represents a vessel position report and is spatially and temporally defined by geographic coordinates and a Universal Time Coordinate (UTC) timestamp respectively.Source data:© Commonwealth of Australia (Australian Maritime Safety Authority) 2020Further information available from AMSA:https://www.operations.amsa.gov.au/Spatial/DataServices/DigitalData

This item is part of the collection "AIS Trajectories from Danish Waters for Abnormal Behavior Detection"

DOI: https://doi.org/10.11583/DTU.c.6287841

Using Deep Learning for detection of maritime abnormal behaviour in spatio temporal trajectories is a relatively new and promising application. Open access to the Automatic Identification System (AIS) has made large amounts of maritime trajectories publically avaliable. However, these trajectories are unannotated when it comes to the detection of abnormal behaviour. The lack of annotated datasets for abnormality detection on maritime trajectories makes it difficult to evaluate and compare suggested models quantitavely. With this dataset, we attempt to provide a way for researchers to evaluate and compare performance.
We have manually labelled trajectories which showcase abnormal behaviour following an collision accident. The annotated dataset consists of 521 data points with 25 abnormal trajectories. The abnormal trajectories cover amoung other; Colliding vessels, vessels engaged in Search-and-Rescue activities, law enforcement, and commercial maritime traffic forced to deviate from the normal course

These datasets consists of labelled trajectories for the purpose of evaluating unsupervised models for detection of abnormal maritime behavior. For unlabelled datasets for training please refer to the collection. Link in Related publications.

The dataset is an example of a SAR event and cannot not be considered representative of a large population of all SAR events.

The dataset consists of a total of 521 trajectories of which 25 is labelled as abnormal. the data is captured on a single day in a specific region. The remaining normal traffic is representative of the traffic during the winter season. The normal traffic in the ROI has a fairly high seasonality related to fishing and leisure sailing traffic.

The data is saved using the pickle format for Python. Each dataset is split into 2 files with naming convention:

datasetInfo_XXX
data_XXX

Files named "data_XXX" contains the extracted trajectories serialized sequentially one at a time and must be read as such. Please refer to provided utility functions for examples. Files named "datasetInfo" contains Metadata related to the dataset and indecies at which trajectories begin in "data_XXX" files.

The data are sequences of maritime trajectories defined by their; timestamp, latitude/longitude position, speed, course, and unique ship identifer MMSI. In addition, the dataset contains metadata related to creation parameters. The dataset has been limited to a specific time period, ship types, moving AIS navigational statuses, and filtered within an region of interest (ROI). Trajectories were split if exceeding an upper limit and short trajectories were discarded. All values are given as metadata in the dataset and used in the naming syntax.

Naming syntax: data_AIS_Custom_STARTDATE_ENDDATE_SHIPTYPES_MINLENGTH_MAXLENGTH_RESAMPLEPERIOD.pkl

See datasheet for more detailed information and we refer to provided utility functions for examples on how to read and plot the data.

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.

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.

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.

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AIS data collected by the University of Piraeus' AIS receiver

Abstract

The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9^th, 2017 to December 26^th, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes.

Dataset related to the following publication

Andreas Tritsarolis, Yannis Kontoulis, Yannis Theodoridis, The Piraeus AIS dataset for large-scale maritime data analytics, Data in Brief, Volume 40, 2022, 107782, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2021.107782.

Files Description

• ais_static: CSV flat files containing vessels' static information and their corresponding types

• geodata: ESRI Shapefiles containing several geographic-related data (e.g. harbours, islands, etc.)

• noaa_weather: ESRI Shapefiles containing weather forecast from GRIB files (as provided by NOAA)

• unipi_ais_dynamic: CSV flat files containing AIS kinematic information

• unipi_ais_dynamic_synopses: CSV flat files containing metadata (i.e. synopses) regarding vessels' AIS positions

Privacy Statement

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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...

The Report Covers Automatic Identification System (AIS) Market Forecast & Analysis. The market is segmented by Application (Fleet Management, Vessel Tracking, Maritime Security, and Other Applications), Platform (Vessel-Based and On-Shore), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

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 is a dataset cleaned from https://coast.noaa.gov/htdata/CMSP/AISDataHandler/2021/

Vessel details are recorded for statistical analysis

EMODnet Vessel Density Map were created by Cogea in 2019 in the framework of EMODnet Human Activities, an initiative funded by the EU Commission. The maps are based on AIS data purchased by CLS and show shipping density in 1km*1km cells of a grid covering all EU waters (and some neighbouring areas). Density is expressed as hours per square kilometre per month. The following ship types are available:0 Other, 1 Fishing, 2 Service, 3 Dredging or underwater ops, 4 Sailing, 5 Pleasure Craft, 6 High speed craft, 7 Tug and towing, 8 Passenger, 9 Cargo, 10T anker, 11 Military and Law Enforcement, 12 Unknown and All ship types. Data are available by month of year. Yearly averages are also available.

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 raster datasets, separated by vessel type. 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). To develop the raster dataset, we created a series of spatially explicit daily vessel tracks according to unique voyages and aggregated tracks by ship type and month. We then created a 10-km raster grid and calculated the total length, in meters, of all vessel tracks within each raster cell. 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 10-km raster data, we also produced data products in 25-km raster format as well as a 1-km coastal data subset and a hex data set which contains additional attributes (e.g., number of ships, ship speed). To find these products, search for “North Pacific and Arctic Marine Vessel Traffic Dataset” in the Arctic Data Center’s data repository.

Asset of Intergenerational Significance (AIS) is land declared under the National Parks and Wildlife Act 1975. Environmental values declared as AIS represent important habitat for entities listed as threatened under the Biodiversity Conservation Act 2016. AIS maps were generated for threatened entities with discrete populations on the national park estate, as well as socially iconic threatened entities widely dispersed and inhabiting specific locations on a temporary or transitory basis.