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.

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. A set of AIS data had to be purchased from CLS, a commercial provider. The data consists of messages sent by automatic tracking system installed on board ships and received by terrestrial and satellite receivers alike. The dataset covers the whole 2017 for an area covering all EU waters. A partial pre-processing of the data was carried out by CLS: (i) The only AIS messages delivered were the ones relevant for assessing shipping activities (AIS messages 1, 2, 3, 18 and 19). (ii) The AIS DATA were down-sampled to 3 minutes (iii) Duplicate signals were removed. (iv) Wrong MMSI signals were removed. (v) Special characters and diacritics were removed. (vi) Signals with erroneous speed over ground (SOG) were removed (negative values or more than 80 knots). (vii) Signals with erroneous course over ground (COG) were removed (negative values or more than 360 degrees). (viii) A Kalman filter was applied to remove satellite noise. The Kalman filter was based on a correlated random walk fine-tuned for ship behaviour. The consistency of a new observation with the modeled position is checked compared to key performance indicators such as innovation, likelihood and speed. (ix) A footprint filter was applied to check for satellite AIS data consistency. All positions which were not compliant with the ship-satellite co-visibility were flagged as invalid.The AIS data were converted from their original format (NMEA) to CSV, and split into 12 files, each corresponding to a month of 2017. Overall the pre-processed dataset included about 1.9 billion records. Upon trying and importing the data into a database, it emerged that some messages still contained invalid characters. By running a series of commands from a Linux shell, all invalid characters were removed. The data were then imported into a PostgreSQL relational database. By querying the database it emerged that some MMSI numbers are associated to more than a ship type during the year. To cope with this issue, we thus created an unique MMSI/shyp type register where we attributed to an MMSI the most recurring ship type. The admissible ship types reported in the AIS messages were grouped into macro categories: 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, 10 Tanker, 11 Military and Law Enforcement, 12 Unknown and All ship types. The subsequent step consisted of creating points representing ship positions from the AIS messages. This was done through a custom-made script for ArcGIS developed by Lovell Johns. Another custom-made script reconstructed ship routes (lines) from the points, by using the MMSI number as a unique identifier of a ship. The script created a line for every two consecutive positions of a ship. In addition, for each line the script calculated its length (in km) and its duration (in hours) and appended them both as attributes to the line. If the distance between two consecutive positions of a ship was longer than 30 km or if the time interval was longer than 6 hours, no line was created. Both datasets (points and lines) were projected into the ETRS89/ETRS-LAEA coordinate reference system, used for statistical mapping at all scales, where true area representation is required (EPSG: 3035).The lines obtained through the ArcGIS script were then intersected with a custom-made 1km*1km grid polygon (21 million cells) based on the EEA's grid and covering the whole area of interest (all EU sea basins). Because each line had length and duration as attributes, it was possible to calculate how much time each ship spent in a given cell over a month by intersecting line records with grid cell records in another dedicated PostgreSQL database. Using the PostGIS Intersect tool, for each cell of the grid, we then summed the time value of each 'segment' in it, thus obtaining the density value associated to that cell, stored in calculated PostGIS raster tables. Density is thus expressed in hours per square kilometre per month. The final step consisted of creating raster files (TIFF file format) with QuantumGIS from the PostgreSQL vessel density tables. Annual average rasters by ship type were also created. The dataset was clipped according to the National Marine Planning Framework (NMPF) assessment area. None

This dataset represents the density of all IMO registered ships operating in the Baltic Sea. Density is defined as the number of ships crossing a 1 x 1km grid cell.

It is based on HELCOM AIS (Automatic Identification System) data. The HELCOM AIS network hosts all the AIS signals received by the Baltic Sea States since 2005.

The AIS Explorer allows to compare density maps of different ship types per month: http://maps.helcom.fi/website/AISexplorer/

The data was processed to produce density maps and traffic statistics. All scripts are available in GitHub: https://github.com/helcomsecretariat. The production of these maps have been carried out 2016-2017 through the HELCOM project on the assessment of maritime activities in the Baltic Sea. The underlying AIS data processing work has been co-financed by EU projects Baltic Scope (2015-2017 EASME/EMFF/2014/1.2.1.5) and Baltic Lines (2016-2019, Interreg Baltic Sea Region). In addition, the Ministry of the Environment of Finland supported the work with a special contribution in view of the use of the results in the HOLAS II process.

The U.S. Vessel Traffic application is a web-based visualization and data-access utility created by Esri. Explore U.S. maritime activity, look for patterns, and download manageable subsets of this massive data set. Vessel traffic data are an invaluable resource made available to our community by the US Coast Guard, NOAA and BOEM through Marine Cadastre. This information can help marine spatial planners better understand users of ocean space and identify potential space-use conflicts. To download this data for your own analysis, explore the Download Options, navigate to a NOAA Electronic Navigation Chart area of interest, and make your selection. This data was sourced from the Automatic Identification System (AIS) provided by USCG, NOAA, and BOEM through Marine Cadastre and aggregated for visualization and sharing in ArcGIS Pro. This application was built with the ArcGIS API for JavaScript. Access this data as an ArcGIS Online collection here. Learn more about AIS tracking here. Find more ocean and maritime resources in Living Atlas. Inquiries can be sent to Keith VanGraafeiland.

These layers are used in the The U.S. Vessel Traffic application; a web-based visualization and data-access utility created by Esri. Explore U.S. maritime activity, look for patterns of vessel activity such as around ports and fishing grounds, or download manageable subsets of this massive data set. Vessel traffic data are an invaluable resource made available to our community by the US Coast Guard, NOAA and BOEM through Marine Cadastre. This information can help marine spatial planners better understand users of ocean space and identify potential space-use conflicts.To download this data for your own analysis, explore the Download Options, navigate to a NOAA Electronic Navigation Chart area of interest, and make your selection. This data was sourced from the Automatic Identification System (AIS) provided by USCG, NOAA, and BOEM through Marine Cadastre and aggregated for visualization and sharing in ArcGIS Pro. This application was built with the ArcGIS API for JavaScript.Access this data as an ArcGIS Online collection here. Learn more about AIS tracking here. Find more ocean and maritime resources in Living Atlas. Inquiries can be sent to Keith VanGraafeiland.

The API interface to the TranStat system provides access to data and metadata from the area of road and maritime transport and enables their automated downloading.

Data available through the API include experimental statistics of traffic intensity, volume of transport work and the size of pollutant emissions generated by road and maritime transport.

The TranStat system uses large data sets ("Big Data") from sensors, i.e. from the Automatic Identification System of Ships (AIS) and the Electronic Toll Collection System (e-TOLL).

The TranStat system is the result of the work of a project co-financed by the National Center for Research and Development as a part of the 1st competition of the program "Social and economic development of Poland in the conditions of globalizing markets" GOSPOSTRATEG.

The Route Density Map at 1 km resolution was created by EMSA in 2019 and made available on EMODnet Human Activities, an initiative funded by the EU Commission.

This layer is part of SDGs Today. Please see sdgstoday.orgToday, the health of our ocean is under immense pressure from both intensive human activity and climate change. One-third of global fish stocks are overfished and two-thirds of the ocean has been significantly altered by human actions. Despite the threats it faces, the ocean remains the least observed part of our planet. This lack of visibility allows illicit activity to thrive. Global Fishing Watch (GFW) is advancing ocean governance through increased transparency of human activity at sea. By creating and publicly sharing map visualizations, data and analysis tools, we enable scientific research and drive a transformation in how we manage our ocean. In 2018, GFW published the first-ever global assessment of commercial fishing activity (2012-2016) in Science. The updated 2021 version of this published dataset contains the GFW AIS-based fishing effort and vessel presence from 2012-2020, which includes over 328 million hours of fishing effort across the globe from over 117,000 unique maritime mobile service identity (MMSI) numbers. The new API Portal provides access to near real-time data on fishing vessel activity and identity.Fishing vessels are identified via a neural network classifier, vessel registry databases, and manual review by GFW and regional experts. Data are binned into grid cells 0.01 (or 0.1) degrees on a side and measured in units of hours. The time is calculated by assigning an amount of time to each AIS detection (which is the time to the previous position) and then summing all positions in each grid cell.Learn more about Global Fishing Watch technology here.