Maritime Awareness Systems Performance in the Western Indian Ocean 2014-2015.
Date: 2016-01-20
Institutions: Joint Research Centre
Subjects: Africa, Indian Ocean, data collection, exchange of information, information system, maritime area, maritime safety, maritime surveillance, piracy, research report, satellite

Maritime Awareness  
Systems Performance in the  
Western Indian Ocean 2014-2015 

Results from the  
PMAR-MASE project 

Harm Greidanus, Marlene Alvarez,  
Vincenzo Gammieri, Carlos Santamaria, 
Alfredo Alessandrini, Pietro Argentieri 

2 0 1 5  

Report EUR xxxxx EN 
Report EUR 27612 EN 

1 

European Commission 
Joint Research Centre 
Institute for the Security and Protection of the Citizen 

Contact information 
Harm Greidanus 
Address: Joint Research Centre, Via Enrico Fermi 2749, TP 670, 21027 Ispra (VA), Italy 
E-mail: harm.greidanus@jrc.ec.europa.eu 
Tel.: +39 0332 78 9739 
Fax: +39 0332 78 9156 

https://ec.europa.eu/jrc/en/institutes/ipsc 
https://ec.europa.eu/jrc/en/research-topic/piracy-maritime-awareness-and-risks 

Legal Notice 
This publication is a Technical Report by the Joint Research Centre, the European Commission’s in-house science 
service.  
It aims to provide evidence-based scientific support to the European policy-making process. The scientific output 
expressed does not imply a policy position of the European Commission. Neither the European Commission nor any 
person acting on behalf of the Commission is responsible for the use which might be made of this publication. 

JRC97935 

EUR 27612 EN 

ISBN 978-92-79-54033-2 

ISSN 1831-9424 

doi:10.2788/420868 

Luxembourg: Publications Office of the European Union, 2015 

© European Union, 2015 

Reproduction is authorised provided the source is acknowledged. 

Cover illustration: Ship positions coloured by ship type and the track of one ship as seen in the PMAR Viewer. 

2 

Executive summary 

The PMAR-MASE project aimed to provide practical experience to authorities in the Eastern-Southern Africa / 
Indian Ocean region on wide-area maritime awareness. Data from automatic ship reporting systems (AIS and 
LRIT) have been received during one year over a large Area of Interest (AOI) in the Western Indian Ocean. The 
data were used to construct the Maritime Situational Picture (MSP) over the AOI, i.e. the map with the current 
positions of all reporting ships. The MSP has been served continuously in real time to two operational users in 
the region, between 15 Sep 2014 and 15 Sep 2015. The data have also been used to make monthly ship traffic 
density maps. Finally, a small number of satellite images have been collected to probe for the presence of ships 
that are not seen on the reporting systems. This report assesses the quality (completeness and timeliness) of 
the ship reporting data and the resulting MSP; it presents the derived monthly ship density maps, split between 
slow moving and fast moving traffic in order to understand ship activity; and it present results on the fraction 
of non-reporting ships based on the analysis of the satellite images.  

The PMAR-MASE AOI stretched from the East African coast nearly out to the Maldives in the east, and between 
Oman in the north to Durban in the south. The data that were used are AIS data received by up to 16 satellites 
of four different operators, AIS data from various coastal networks in the region, LRIT data from the Flags that 
use the EU LRIT Data Centre (“EU Flags”), and space-based radar images from two different satellites.  

The  performance  of  the  ship  reporting  systems  was  analysed  in  detail  for  one  representative  month,  June 
2015. During that month, on average 567k ship position messages were received every day (all from within the 
AOI). More than 80 % of these are satellite AIS, most of the remainder terrestrial AIS. LRIT only represents 0.1 
% of the total number of position reports. Less than 2% are messages from Class B AIS ships (smaller ships). The 
number of different ships seen each day in the AOI is on average just over 1,500. About 8 % are Class B ships. 
The  number  of  different  ships  seen  during  the  entire  month  is  5,350  –  much  higher  than  the  daily  number, 
because ships move in and out of the AOI every day.  

These numbers are the result of combining the data from all sources together (in June, that is 11 AIS satellites, 
several coastal networks and the EU LRIT). If only a single AIS satellite would be available, it would see only 82 
% of the number of ships seen by all sensors together on a daily basis. This is because only a fraction of all AIS 
messages  that  are  broadcast  are  received  by  any  AIS  receiver.  The  more  sensors  are  combined,  the  more 
complete the ship traffic picture becomes. The combination of two satellites sees 89 % of what all sensors see; 
the combination of three satellites, 92 %; etc.; up to 96.5 % from 11 AIS satellites, the remainder being seen 
only by the terrestrial AIS and LRIT. LRIT on its own sees daily on average 178 ships, a low number because it is 
only the ships of the “EU Flags”. If the LRIT is combined with a single AIS satellite, which by itself sees daily on 
average 1,252 ships, then still the LRIT sees on average 27 ships that the AIS does not see. To the constellation 
of 11 satellites, that reveal the presence of 1,476 ships, LRIT still adds 11 ships missed by the AIS on an average 
daily basis.  

Now looking at the number of ships in the MSP, meaning the number of ships present in the AOI at any one 
time, which is less than the daily number seen, it is found that one AIS satellite will put 1,188 ships in the MSP; 
two AIS satellites yield 1,226 ships; thee give 1,262; etc., up to 11 AIS satellites that with diminishing increases 
result in 1,345 ships in the MSP on average. LRIT alone yields 165 ships in the MSP on average; but adding LRIT 
to 11 AIS satellites still adds another 10 ships (0.7 %) to the AIS total. The MSPs are constructed from the data 
by a tracking process, which recognises and removes data errors (that would have a serious impact if left in). 
The tracking has a number of parameters that influence the results in absolute numbers, but less so in relative 
numbers (as in comparing N satellites to N+1 satellites).  

The ship positions in the MSP are interpolated between position reports that have been received prior to and 
after the MSP time, or extrapolated from one of these if the other is not available. The further the MSP is away 
in time from the prior or later position report, the less accurate the interpolation result is. The time difference 
between  the  MSP  time  and  the  nearest  position  report  is  therefore  a  measure  of  the  accuracy  of  the  MSP 
positions. If one AIS satellite is used, this time difference is 2.6 hours on average. But the distribution of these 
time differences is very broad, so many individual time differences are much smaller or much larger. If 7 AIS 
satellites are used, this mean time difference is reduced to 1.0 hours. Adding more AIS satellites continues to 
reduce  the  mean  time  difference,  but  not  by  so  much  anymore.  Using  only  LRIT,  the  time  difference  is  on 
average 1.8 hours. This compares to the time difference when using 4 AIS satellites. Of course, the LRIT only 
tracks  165  ships  in  the  MSP  whereas  4  AIS  satellites  have  1,276  ships.  The  LRIT  is  more  accurate  despite  its 

3 

much  lower  number  of  messages  because  the  messages  are  distributed  more  evenly  over  the  day,  leaving 
fewer large gaps.  

Concerning the monthly ship density maps, they were constructed from satellite AIS and LRIT data, excluding 
the terrestrial AIS – which is only concentrated in a few coastal locations – to prevent a spatial bias. The maps 
were made with a spatial resolution of 0.15 degrees which is approximately 9 nautical miles or 16.7 km.  The 
density counts the average number of ships present at any one time per unit of surface area. The maps were 
split  in  fast  moving  traffic  (>  6.5  knots)  and  slow  moving  traffic  (<  6.5  knots).  The  fast  moving  traffic  is 
dominated by the main traffic routes, and the pattern does not change much from month to month. The slow 
moving  traffic  indicates  activities  at  sea  other  than  transiting,  such  as  fishing  or  exploration,  besides 
concentrations  of  ships  at  anchor  or  moored  in  or  near  ports.  The  patterns  of  these  activities  change  much 
from month to month.  

To  sample  the  non-reporting  ship  traffic,  46  satellite  images  were  analysed  in  detail.  Of  those,  29  were 
purchased, 12 were publicly available, and 5 were used via scientific collaboration. The images were located in 
front of the Somali coast, between Kenya-Comoros-Mozambique, over La Réunion and Mauritius, in the Gulf of 
Aden and in the middle of the Indian Ocean. In total, 272 targets were detected at sea in the images. Of these, 
34 % could not be correlated with reporting ships. This percentage however varied much from area to area, the 
highest  fraction  of  non-reporting  ships  being  found off  Somalia.  In  addition  to  these  46  satellite  images that 
were analysed with human verification, more than 1,000 Sentinel-1 images that were collected in the area and 
during the project period under the EU’s Copernicus program, were analysed in fully automatic mode. Here, a 
fraction of 40 % of the targets could not be correlated with reporting ships. But this number is less  accurate 
due to the lack of verification and is probably an overestimate.  

4 

Contents 

1 
2 

2.1 
2.2 
2.3 
2.4 
2.5 
2.6 

Introduction ..................................................................................................................................................... 6 
Completeness and timeliness of AIS and LRIT ................................................................................................. 9 
Introduction ............................................................................................................................................. 9 
Types of position messages and their numbers ...................................................................................... 9 
Analysis per data category..................................................................................................................... 10 
Daily number of position reports and MMSI numbers ......................................................................... 13 
The contributions per data source ........................................................................................................ 18 
Completeness and timeliness of the MSP ............................................................................................. 23 
3  Monthly ship density maps ........................................................................................................................... 30 
Procedure .............................................................................................................................................. 30 
Results ................................................................................................................................................... 30 
Ship detections from satellite SAR ................................................................................................................ 38 
Technology and method ........................................................................................................................ 38 
Satellite SAR data used .......................................................................................................................... 38 
Results ................................................................................................................................................... 39 
Acknowledgments ................................................................................................................................................. 47 

4.1 
4.2 
4.3 

3.1 
3.2 

4 

5 

 
 
 
1 

Introduction 

This  report  analyses  and  discusses  the  data  that  have  been  collected  during  the  PMAR-MASE  project  for 
maritime surveillance of the Eastern-Southern Africa / Indian Ocean region. It is an Annex to the PMAR-MASE 
Final Report.  

PMAR  stands  for  Piracy,  Maritime  Awareness  and  Risks.  The  PMAR-MASE  project  was  executed  by  the 
European  Commission’s  Joint  Research  Centre  (JRC)  for  the  Indian  Ocean  Commission  under  the  MASE 
program. The MASE program aims to support ‘MAritime SEcurity’ in the Eastern-Southern Africa / Indian Ocean 
region  and  is  funded  by  the  10th  European  Development  Fund  (EDF).  The  purpose  of  PMAR-MASE  was  to 
provide  practical  experience  to  users  and  stakeholders  of  maritime  surveillance  in  the  region.  Under  PMAR-
MASE, one year of ship reporting data has been collected over the Western Indian Ocean, during 15 Sep 2014 – 
15 Sep 2015. The Area of Interest (AOI) was between 31 and 68 degrees East and between 30 degrees South 
and 19 degrees North (see Figure 1-1). These data have been used to provide a continuous, real-time Maritime 
Situational Picture (MSP) to two operational centres for one year – the Indian Ocean Commission’s Anti-Piracy 
Unit in the Seychelles, and the Regional Maritime Rescue Coordination Centre (RMRCC) of the Kenya Maritime 
Authority in Mombasa.  

Figure 1-1. The AOI of the PMAR-MASE project, the area within which the maritime surveillance data were collected 
and the MSP was constructed. (Screenshot from Google Earth.) 

Two main types of data have been collected. The foremost one is automatic ship reporting data from the AIS 
and LRIT systems. AIS (Automatic Identification System) is a system that has transponder equipment installed 
on ships, automatically broadcasting short VHF radio messages with the ship’s identification, position and some 
other information. These messages are received by the AIS transponders on other ships in the vicinity, so that 
each ship is aware of the local traffic situation. The short messages are broadcasted frequently, at a variable 
rate  which  is  of  the  order  of  once  a  minute.  By  using  receivers  on  the  coast  or  mounted  on  satellites,  and 
feeding  the  data  into  a  network,  the  AIS messages  can  be  centrally  collected  to  provide  the  monitoring of  a 
certain  AOI.  LRIT  (Long  Range  Identification  and  Tracking)  is  a  similar  system  with  transponders  on  ships, 
however in this case the short messages are only sent once every 6 hours and only to the Maritime Authority of 
the ship’s Flag State. The messages that contain the ship’s geographic position (latitude, longitude) are referred 
to  as  position  messages  or  position  reports.  In  the  AIS  message,  the  ship  is  identified  by  its  so-called  MMSI 
number. In the LRIT message the identification is by the ship’s IMO number, which can be linked to an MMSI 
number in order to merge the two types of messages of the same ship into one stream.  Only the larger ships 
carry  the  AIS  and/or  LRIT  equipment,  following  global  regulations  from  the  UN’s  International  Maritime 
Organisation  (IMO).  The  precise  carriage  requirements  are  rather  detailed,  but  roughly  it  concerns  the  ships 
larger than 300 tonnes. Some smaller ships may carry voluntary “Class B” AIS (the mandatory one for the larger 
ships being Class A).  

6 

 
 
  
 
The secondary type of maritime surveillance data that was used are ship detections from satellite images. This 
is a completely different type of data, where the ships are not identified, but only the presence and position of 
some object can be established, not even with the certainty that the object is a ship. These data are used to 
assess how many ships may still be present that are not seen by the reporting systems. Ship detections from 
satellite images are sometimes referred to as VDS (for Vessel Detection System). The satellite images have only 
a very limited coverage in space and time, and therefore the non-reporting ship traffic cannot be monitored as 
was done for the reporting traffic, but only very sparsely sampled.  

In this report, three types of analysis have been performed on the data:  
  Assessment  of  the  quality  of  the  ship  reporting  data,  in  terms  of  completeness  and  accuracy,  and  the 

contributions of the various individual data sources to the final result;  

  Construction of ship traffic density maps over the AOI on a monthly basis, to show and quantify the spatial 

distribution of the ship traffic;  

  Assessment of the amount of non-reporting ships, the ships that are not seen in the AIS or LRIT, through 

the use of VDS / satellite images.  

As mentioned, all the data have been used in the first place to compute the Maritime Situational Picture (MSP), 
the map of the positions of all the identified ship traffic in the AOI. The MSP was being displayed in real-time to 
the two operators in the region that were mentioned, with an update rate of once per 15 minutes. An example 
is shown in  Figure  1-2  and Figure  1-3.  The contents and  functionalities of the PMAR Viewer are  explained in 
detail in “PMAR Viewer User Manual, Issue Feb 2015”, JRC947851.  

Figure 1-2. The PMAR-MASE Maritime Situational Picture, captured at 24 April 2015 09:25 UTC. Each reporting ship 
in the AOI (red box) is indicated by a dot, colour coded by ship type. Map background from Natural Earth.  

1 http://publications.jrc.ec.europa.eu/repository/bitstream/JRC94785/lbna27117enn.pdf  

7 

 
 
 
 
                                                        
Figure 1-3. The PMAR-MASE Maritime Situational Picture zoomed in to show more detail. At this zoom level, the ship 
symbols are triangular arrows, with the short protruding line indicating the ship’s expected displacement in the next 
hour. Any ship symbol can be selected to display its information and past track, as shown here. The erratic track of 
the selected ship confirms its type as ‘Fishing’. On account of the public distribution of this report, the ship in 
question was anonymised. Map background from Natural Earth. 

The set-up of the PMAR-MASE system (to collect and process the data) was discussed in the main PMAR-MASE 
Final  Report,  and  is  not  repeated  here  in  detail.  Figure  1-4  shows  the  high-level  system  design.  The  “Data 
sources” on the left side of the figure are primarily the AIS and LRIT ship reports, but also piracy incident data, 
wind and wave predictions, and static map background layers.  

Figure 1-4. The PMAR-MASE high level system design. On the right are the two operational users, who could access 
the information as exemplified in the previous two figures via a web viewer.  

8 

 
 
 
 
 
 
2 

Completeness and timeliness of AIS and LRIT 

Introduction 

2.1 
This chapter analyses the completeness and accuracy of the information content in the AIS and LRIT data.  

AIS  and  LRIT  data  that  can  be  gathered  from  a certain  AOI  are  fundamentally  incomplete.  This  is  in  the  first 
place  because  such  information  will  only  come  from  ships  that  carry  those  systems.  But  accepting  that 
limitation, still only a fraction of the AIS and LRIT reports that are transmitted by the ships in the AOI will reach 
the user. For AIS, this is because terrestrial receivers cannot reach beyond the VHF horizon, so have only a local 
coverage; and satellite-based AIS receivers are only overhead of the AOI for a short amount of time, and even 
then miss many reports because the signal strength is extremely weak at the distance of the satellite, and too 
many messages may reach the receiver simultaneously which leads to saturation. In the case of LRIT, only a few 
reports  do  not  reach  their  destination  in  the  Flag  administration,  but  there  the  issue  is  more  to  get  the 
permission to access the data. Furthermore, LRIT under-samples the ship movements by design on account of 
its low update rate.  

This chapter aims to quantify the information content of the maritime awareness that is based on AIS and LRIT 
data  by  two  aspects.  The  first  aspect  is  completeness.  This  is  essentially  quantified  by  counting  how  many 
different  ships  are  seen  in  the  data.  The  second  aspect  is  timeliness.  This  relates  to  how  often  the  ship 
positions are updated, and it is essentially quantified by counting how many position reports are received from 
the ships per unit of time, and how well they are equally distributed in time. Different measures with various 
implementations will be used to assess these two aspects.  

Three essentially different data categories are distinguished in the ship reporting data: Satellite AIS, Terrestrial 
AIS and LRIT. The data sources that have been used for each category are:  
  Satellite AIS:  

o  Satellites operated by the Norwegian Coastal Administration 
o  Satellites operated by the company exactEarth 
o  Satellites operated by the companies Orbcomm/LuxSpace 
o  Satellites operated by the company SpaceQuest 

  Terrestrial AIS:  

o  Coastal AIS network of MSSIS 
o  Coastal AIS from the company exactEarth 
o  Coastal AIS from the companies Orbcomm/LuxSpace 

 

LRIT:  

o  LRIT from the Flags that use the EU LRIT Data Centre via EMSA 

The satellite AIS data used in the project represents all data of that category coming from (semi-) operational 
systems that were  in  existence  and known to us. No other AIS satellites  exist, except for some experimental 
systems that only produce limited amounts of data.  
Data from these sources have been received in the PMAR-MASE project for the period of one year, 15 Sep 2014 
to 15 Sep 2015. For the analysis in the present chapter, the data from the month of June 2015 have been used. 
This month is representative for the rest of the year.  

Types of position messages and their numbers  

2.2 
The AIS position message types are:  

1, 2, 3: Class A position report 
4: Base station report 
11: Response to base station request (indistinguishable Class A or B) 
18: Class B position report 
19: Extended Class B position report 

AIS message 27 (long range report) has not been used.  

9 

 
The position reports from the LRIT are only of one type and are identified in this study by the number 313.  

The distribution of the received positions reports over these types is plotted in  Figure 2-1. AIS messages 1, 2 
and 3 have the same content. From Figure 2-1 it can be seen that by far most messages are AIS Class A position 
reports  (1,  2,  3).  The  Class  B  position  reports  (18,  19)  are  much  fewer,  and  the  LRIT  (313)  even  less  again. 
Message  4  is  not  relevant  for  plotting  ship  positions,  but  it  can  be  used  to  check  the  time  stamping  of  a 
receiver.  

Figure 2-1. The number of messages received for each position message type. The numbers are for the whole month 
of June 2015 and the vertical scale is in units of one million messages.  

Analysis per data category 

2.3 
Here we look at the number of position reports received and the different number of ships contained in them, 
split over satellite AIS, terrestrial AIS and LRIT.  

Daily number of position messages and distribution over Class A/B 

The number of messages received varies per day, because one day there may be more satellite overpasses over 
the AOI than another, and because the number of ships inside the AOI varies over time. The figure below (left 
side) depicts the variation, as well as the difference in message numbers received from LRIT, terrestrial AIS, and 
satellite AIS. The total number of messages received (on average 566,522 per day) is the sum of those three. It 
can  be  seen  that  satellite  AIS  provides  the  bulk  of  the  position  reports.  On  the  right,  the  figure  shows  the 
percentage of AIS Class B messages. For the terrestrial AIS, this varies between 4 and 11 %; for the satellite AIS, 
it is much lower at around 0.5 %. There are two reasons for this. First, the Class B message is weaker, so it is 
less easily received by satellite. Second, Class B is carried by the smaller ships, which are found closer to the 
coast, within the coverage of the terrestrial AIS.  

10 

 
 
  
 
 
Figure 2-2. Left: The number of position messages received on each day of June 2015 from: LRIT (light blue, at the 
bottom); all terrestrial AIS sources combined (dark blue, around 1); all AIS satellites combined (black, around 4.5); 
all sources combined (black circles, top line). The vertical scale is in units of 100,000 messages. Right: The 
percentage of Class B position reports of the total Class A + Class B position reports received each day in June from: 
all AIS satellites combined (black, lower); all terrestrial AIS sources combined (blue, higher).  

Daily number of different MMSI numbers seen in position reports 

The number of unique MMSI numbers is close to the number of different ships in the AOI. In principle, a ship is 
uniquely defined by its MMSI number in the AIS message, and in the LRIT message by its IMO number which is 
linked  to  an  MMSI  number.  Therefore,  counting  the  different  MMSI  numbers  that  occur  should  count  the 
different  ships.  However,  when  extracting  the  tracks  of  the  ships,  it  can  be  recognized  that  some  MMSI 
numbers are used by more than one ship. This happens in particular for ships that use wrong MMSI numbers, 
such as 0, 1, 12345, 111111111, and the like. The tracking shows that several tens of ships use duplicate MMSI 
numbers. On account of this effect, the number of different MMSI numbers would underestimate the number 
of different ships by that amount. However, another effect leads to an overestimation. A small number of AIS 
messages  contain errors, that  are  introduced  either  in  the  transmission or  in  the  reception of  the  VHF  radio 
broadcast. Some of the errors occur in the MMSI value. This leads to MMSI values that are seen only one single 
time ever, or at least only once during a long period. If during one month an MMSI number is seen in only a 
single message, that message  is most  probably an error.  (Even if it is not an error and there  is a real ship of 
which only a single message is received, then that is not useable for the tracking of that ship.) Errors may also 
occur in the geographic position value; this is especially noticeable when ship positions are indicated on land. 
As the number of AIS messages per month is so high (in June, 566,522 x 30 = 17 million), a low error rate can 
still  lead  to  a  significant  number  of  erroneous  messages.  It  is  found  that  many  hundreds  of  messages  in  a 
month occur with an MMSI number that is only seen once in that month – in June, the number was 868. That 
corresponds  to  29  per  day  on  average.  That  effect  overestimates  the  number  of  ships  when  counting  the 
number of different MMSI. So both effects together, sharing of MMSI numbers and spurious MMSI numbers, in 
order of  magnitude  cancel  each  other.  Therefore,  the  number of  different  MMSI  numbers  seen  is  used  as  a 
good indicator of the actual number of different reporting ships present.  

The  total  number  of  different  MMSI  numbers  seen  each  day  in  position  reports  is  shown  in  the  left  figure 
below. It varies daily for the same reason as the number of messages discussed earlier. On average, the total 
number of MMSI numbers seen each day from all data sources together is 1,563. The figure shows that most of 
those ships are seen by satellite AIS. About half are seen by terrestrial AIS. (Note that, unlike for the number of 
messages, the number of MMSI numbers seen by the various sources does not add up to the number of MMSI 
numbers seen by all sources together.) Terrestrial AIS sees fewer ships because it has a much smaller spatial 
coverage. Only 12 % of the MMSI numbers are seen by LRIT. This is because only the ships that carry “EU Flags” 
are included in the LRIT, which is a small fraction of all ships in the AOI.  

Figure 2-3 right side shows the percentage of Class B MMSI with respect to Class A + Class B. It is around 7 %, 
and  not  much  different  as  seen  from  satellite  or  by  terrestrial  AIS.  (LRIT  does  not  cover  Class  B  ships.)  This 
should be compared with Figure 2-2 right side, where it was found that the number of Class B messages from 

11 

 
 
 
 
terrestrial AIS was more than 10 times higher than from satellite. This reflects that fact that even though the 
satellite sees a similar number of Class B AIS ships as the terrestrial AIS, the Class B ships that are seen from the 
coast are much better tracked than those seen by satellite.  

Figure 2-3. Left: The number of different ships seen each day in June 2015 by LRIT (light blue, bottom line); all 
terrestrial AIS (dark blue, second from bottom); all satellite AIS (black, second from top); and by all systems together 
(top, circles). Right: The fraction of Class B ships of the total number of ships (A+B) seen each day in June by all 
terrestrial AIS (blue, mostly the lower line); and by all satellite AIS (black).  

The  average  daily  number  of  1,563  different  MMSI  numbers  (corresponding  to  Figure  2-3)  includes  all  that 
were  seen  in  June  (from  all  position message  types,  all  data  sources).  When MMSI  numbers  that  occur  only 
once in the month of June, and position reports on land, i.e. likely erroneous ones, are excluded, the remaining 
average daily number of MMSI numbers is 1,532. This still includes MMSI numbers from AIS message type 4 
(base station message), so MMSI numbers  from base  stations which are  not  relevant  for the MSP. Excluding 
those, the remaining average daily number is 1,522.  

Over  the  entire  month  of  June  2015,  the  total  number  of  different  MMSI  numbers  seen  is  6,217  (from  all 
position  message  types,  all  data  sources).  Excluding  MMSI  numbers  that  are  seen  only  once  in  that  entire 
month,  which  are  likely  errors,  the  number  reduces  to  6,217  –  868  =  5,349.  This  is  much  higher  than  the 
average daily number, because ships move in and out of the AOI constantly, so new ships are seen every day. 
The average daily renewal rate is ( 5,349 – 1,532 ) / ( 30 – 1 ) / 1,532 = 8.5 %.  

Geographical coverage  

The AIS satellites and the LRIT cover the whole AOI uniformly. The terrestrial AIS on the other hand has only 
the coverage of the coastal ranges where the participating receiving stations are. Figure 2-4 plots the maps of 
the density of the received position messages, for all data sources together, and for satellite AIS, terrestrial AIS 
and  LRIT  separately.  The  limited  geographic  coverage  of  the  terrestrial  AIS  is  obvious,  as  is  the  much  lower 
density of the LRIT. The density map of all sources together is dominated by the satellite AIS.  

The  density  maps  have  a  very  high  dynamic  range  –  in  some  places,  e.g.  in  or  near  ports,  the  number  of 
messages per area can be very high, while in others, e.g. on the open ocean outside the main traffic routes, it 
can be very low. For a meaningful visualisation, the density colour scale is therefore logarithmic. The scale in 
the top left map ranges from 0 to 4.1, which means from 10^0 = 1 message per unit surface area to 10^4.1 = 
13,000 messages per unit surface area. The greenish parts in the middle of the scale have 10^2 = 100 messages 
per unit surface area.  

See the next chapter for all monthly density maps and further details on their construction and contents.  

12 

 
 
 
 
 
 
Figure 2-4. Density maps of all ship positions received in June 2015. Top left: from all data sources combined. Top 
right: from all AIS satellites. Bottom left: from all terrestrial AIS. Bottom right: from LRIT. The colour scaling is 
logarithmic.  

Daily number of position reports and MMSI numbers 

2.4 
This section documents the  number of position reports that have  been received, and the number of  distinct 
MMSI numbers that are present in the data. It does so per data type: satellite AIS, terrestrial AIS, LRIT, and all 
combined. It gives a number for each day between 1 Sep 2014 and 30 Sep 2015, in the form of monthly graphs. 
MMSI numbers that are seen only once ever in a certain month are not counted, because they are most likely 
due to data errors. Also messages and MMSI numbers of AIS message type 4 (base station) are not counted. 
Therefore, the numbers should accurately reflect the ship-borne MMSIs.  

The following pages show the graphs for each month, the number of messages on the left and the number of 
MMSI  numbers  on  the  right.  All  have  the  same  scale,  up  to  700,000  messages  and  1,700  MMSI  numbers 
respectively.  

13 

 
 
 
 
 
 
 
Figure 2-5. Daily number of position reports received (left) and unique MMSI numbers seen (right). Separate curves 
for LRIT (green, lowest), terrestrial AIS (light blue, second from below), satellite AIS (dark blue, second from top) 
and total over those three (black, topmost curve). The months of September, October and November 2014.  

14 

 
 
 
 
 
 
 
 
Figure 2-6. Daily number of position reports received (left) and unique MMSI numbers seen (right). The months of 
December 2014, January 2015 and February 2015.  

15 

 
 
 
 
 
 
 
 
Figure 2-7. Daily number of position reports received (left) and unique MMSI numbers seen (right). The months of 
March, April and May 2015.  

16 

 
 
 
 
 
 
 
 
Figure 2-8. Daily number of position reports received (left) and unique MMSI numbers seen (right). The months of 
June, July and August 2015.  

17 

 
 
 
 
 
 
 
 
Figure 2-9. Daily number of position reports received (left) and unique MMSI numbers seen (right). The month of 
September 2015.  

The contributions per data source 

2.5 
This section will look at the contributions of individual data sources, separately and in combinations.  

Separate contributions of individual platforms  

In the month June 2015, there were 11 distinguishable AIS satellite data sources available. (In other months the 
number  differed.)  One  of  these  11  is  Orbcomm  Second  Generation  (OG2)  which  consists  of  6  separate 
satellites. So in total there is data from 16 satellites included. However, in the data stream, the six satellites of 
OG2 are not distinguished. Therefore, in this analysis, they count as one.  

Figure 2-10 plots the daily number of MMSI numbers seen by each of the 11 satellites separately (drawn lines) 
and  by  each  terrestrial  AIS  network  and  LRIT  separately  (dotted  lines).  All  satellites  have  a  broadly  similar 
performance  in  the  range  of  1,150  to  1,350  MMSI  numbers,  but  still  systematic  differences  can  be  seen. 
Sometimes individual satellites suffer from outages, such as shown by the dips on 13 and 17 June. As long as 
there  are  several  other  satellites  available,  that  does  not  lead  to  big  problems  in  the  surveillance.  The 
differences between the coastal networks are bigger; the larger coastal networks have a similar performance of 
around 500 MMSI numbers, but also some networks are included with a much smaller geographic distribution, 
leading to the dotted lines near the bottom. The LRIT can be recognized in the purple line (around 180 MMSI 
numbers), the same line that was light blue in the previous graph of Figure 2-3 left.  

18 

 
 
 
 
 
Figure 2-10. The daily number of MMSI numbers seen for each data source separately. The drawn (higher) lines are 
the AIS satellites. The dotted (lower) lines are the terrestrial AIS networks and LRIT. The top line (circles) is all data 
sources combined.  

Figure 2-11 left shows again the number of MMSI numbers seen each day by the individual AIS satellites, but 
now as a percentage of the total number of MMSI numbers seen each day by all AIS satellites combined (so 
excluding  the  terrestrial  AIS  and LRIT).  So  it  shows  the  performance of each  AIS  satellite  with  respect  to  the 
others. The right side of that figure shows the percentage of Class B MMSI numbers seen by each satellite. This 
varies significantly from around 2 % to around 6 %. As the satellites have similar geographic coverage, and the 
Class B signals being weaker than the Class A ones, the difference is ascribed to the difference in sensitivity of 
the satellite receivers.  

19 

 
 
 
 
Figure 2-11. Left: The number of MMSI numbers seen by the individual AIS satellites on each day in June 2015, as a 
percentage of the number of MMSI numbers seen by all AIS satellites together on that day. Right: The percentage of 
Class B MMSI numbers seen each day for each satellite separately.  

Cumulative contributions of AIS satellites 

With 11 satellite platforms, it is possible to quantify the performance of one platform on average, by making 
the average of the performance of each of the 11 platforms. (Performance as before in terms of the number of 
different MMSI numbers seen per day.) It is also possible to measure the performance of a constellation of two 
satellite platforms, by calculating the average over all possible combinations of two platforms out of 11. So we 
calculate the performance of the combination of platforms 1 and 2, of the combination 1 and 3, 1 and 4, etc., 
up  to  1  and  11;  then  the  combination  2  and  3,  2  and  4;  etc.  There  is  a  total  of  11  x  10  /  2  =  55  possible 
combinations of two platforms out of 11. The performances of all these 55 combinations  are calculated each 
day, and then averaged to obtain the average performance of a constellation of two satellites on each day. This 
is also done for a constellation of 3 satellites out of the 11, 4 out of 11, 5, etc.; up to 11 out of 11 which is  of 
course only one possible combination. In general, the number of possible combinations of k out of n is n! / ( (n-
k)!    k!  ).  This  becomes  large  for  the  mid  ranges  of  k,  like  for  n=11  and  k=6  it  is  462.  The  total  number  of 
combinations to calculate, from k  = 1, 2, 3, … to 11 satellites, would be  2047.  To avoid excessive  computing 
time,  the  number  of  computed  combinations  for  each  k  is  limited  to  a  random  selection  of  maximally  55 
different combinations out of the possible total. This leads to an overall total of 463 combinations to calculate. 

The result is displayed in Figure 2-12, which shows the number of different MMSI numbers seen each day in 
June  by  the  average  combination  of  k  AIS  satellites  out  of  the  total  available  11,  for  k  =  1,  2,  3,  …,  11.  The 
number is expressed as a percentage of the total number of MMSI numbers seen that day from all data sources 
including terrestrial and LRIT. In these calculations, MMSI numbers that are only seen once ever in the whole 
month,  position  reports  on  land,  and  AIS  message  type  4  (base  stations)  have  been  excluded.  The  bottom 
(black)  curve  for  k  =  1  corresponds  to  the  average  of  the  11  individual  satellite  curves  of  Figure  2-11  left 
(although at a different scale). The top curve (brown) is the result of all 11 satellites combined, so corresponds 
to the ‘Sat’ curve in Figure 2-3 left (although again at a different scale). It can be seen that the benefit of adding 
satellites diminishes with each satellite added. It can also be seen that already a combination of 2 satellites is 
less sensitive to individual satellite outages: the curve of k = 2 is less variable than the curve of k = 1, which 
shows dips at 13 and 17 June corresponding to the individual satellite outages on those days.  

The  diminishing  return  behaviour  is  summarized  in  Figure  2-13  which  shows  how  the  performance  of  a 
constellation of satellites grows with the number of satellites. The figure plots the mean values of the 11 curves 
of Figure 2-12 averaged over the whole month. The graph grows quickly in the beginning but flattens off after 
3-4 satellites.  

20 

 
 
 
 
 
Figure 2-12. The number of different MMSI numbers seen each day in June on average by a combination of k AIS 
satellites out of the total available 11, for k = 1, 2, 3, …, 11. The number is expressed as a percentage of the total 
number of MMSI numbers seen that day from all data sources including terrestrial and LRIT.  

Figure 2-13. The average daily percentage of MMSI numbers seen over the month of June 2015 by the average 
combination of 1 AIS satellite out of 11, 2 out of 11, etc., up to all 11.  

Additional contribution of terrestrial AIS 

In order to quantify the contribution of the terrestrial AIS over that of the satellites, we count how many MMSI 
numbers are seen extra when adding terrestrial AIS to that of k satellites, for k = 1 to 11. The result is shown in 
Figure 2-14. The top line of that figure shows that on average around 160 MMSI numbers are seen each day in 
terrestrial AIS that were not also seen in satellite AIS, when using only one satellite; the points on the line are 
the average for all 11 satellites, like the black line was in Figure 2-12. That is 160 / 1,522 = 10.5 %. (As in the 

21 

 
 
 
 
 
previous  section,  the  MMSI  numbers  here  exclude  those  that  were  only  seen  once  in  the  whole  month, 
position reports on land, and AIS message 4.) The second line from the top (purple) shows that when using a 
constellation of two AIS satellites, the terrestrial AIS only  adds around 105 MMSI numbers on average; each 
point on this line is computed by averaging over all possible combinations of two satellites out of 11, like the 
purple line was in Figure 2-12. Etcetera. The lowest line shows that on average, terrestrial AIS adds about  39 
MMSI numbers to those seen by the AIS from a constellation of 11 satellites; that corresponds to 39 / 1,522 = 
2.6 %.  

The diminishing additional value of terrestrial AIS over satellite AIS with the number of satellites is visualised in 
Figure 2-16 left.  

Figure 2-14. Each line (colour) shows for each day in June 2015, the number of MMSI numbers seen by terrestrial AIS 
that were not already seen by satellite AIS on that day. The top (black) line is when using one satellite; the second 
from top (purple) is for two satellites; etc. down to the lowest line (brown) that is for using all 11 satellites.  

Additional contribution of LRIT 

Just  as for the terrestrial AIS,  Figure  2-15  shows  the additional number of MMSI numbers seen by LRIT over 
those seen by satellite AIS, for k = 1 to 11 satellites, averaged over all combinations of k satellites out of 11, for 
each day in June 2015. For LRIT, the use of one single satellite leaves on average about 27 MMSI numbers (1.8 
%) unseen; whereas that number is reduced to about 11 MMSI numbers (0.7 %) when using 11 AIS satellites. 
The diminishing additional number of MMSI numbers seen exclusively in LRIT as a function of the number of 
AIS satellites is plotted in Figure 2-16 right.  

22 

 
 
  
 
 
Figure 2-15. Each line (colour) shows for each day in June 2015, the number of MMSI numbers seen by LRIT that 
were not already seen by satellite AIS on that day. The top (black) line is when using one satellite; the second from 
top (purple) is for two satellites; etc. down to the lowest line (brown) that is for using all 11 satellites. 

Figure 2-16. The number of MMSI numbers seen exclusively in terrestrial AIS (left) / LRIT (right), on a daily basis 
averaged over the month of June 2015, over and above that seen on satellite AIS, as a function of the number of AIS 
satellites used (1 to 11).  

Completeness and timeliness of the MSP 

2.6 
Tracking 

The previous sections have dealt with numbers of position messages and numbers of ships seen in the data per 
time period, as a measure of the completeness of the data. In order to make the MSP and to make unbiased 
ship  density  maps,  and  in  general  to  use  the  data  for  maritime  awareness,  the  ships  in  the  AOI  need  to  be 
tracked. This means that the position reports of a certain MMSI number need to be collated, ordered in time, 
and the resulting irregularly sampled series of positions must be interpolated to a regular time grid (e.g. every 
15  min)  to  obtain  the  ship’s  track.  That  process,  however,  is  not  straightforward  because  the  data  contain 
errors and because sometimes, as mentioned, several ships share the same MMSI number.  

23 

 
 
 
 
 
 
The  latter  situation  can  be  recognised  by  the  tracking  algorithm  based  on  the  fact  that  ships  cannot  move 
faster than a certain maximum speed – for sure not faster than e.g. 100 knots. If two consecutive positions of 
the  same  MMSI  number  are  so  far  apart  that  a  higher  speed  would  be  needed  to  connect  them,  they must 
belong  to  two  different  ships.  With  this  principle,  two  ship  tracks  under  the  same  MMSI  number  can  be 
separated if they are sufficiently far apart in space, and sufficiently densely sampled in time. Even several (3, 4, 
5, ….) tracks can be disentangled from the same MMSI number in this way, provided they are separated well 
enough.  However,  if the  two ships are too close together or there  are long gaps in the position reports, the 
outcome of this track disentanglement becomes less reliable.  

Errors in the data can occur in any field of the message, in particular in the MMSI field, the longitude or latitude 
field, and the time stamp. These errors give rise to outlier points in the track, points that lie away from all the 
other  points.  The  tracking  algorithm  has  to  recognise  these  and  discard  them.  If  it  concerns  single,  isolated 
outliers, this is not too difficult. However, there are two sources of error that cause serious problems. The first 
is related to the time stamp; the second to faulty AIS equipment. The time stamp is, for the AIS message, not 
inside the message, but instead it is affixed to the message by the receiver upon reception. The receiver uses 
its own clock for that. It is found that some receivers have unstable or offset clocks. When the track of an MMSI 
number is compiled from several data sources, messages from a receiver with a clock offset are shifted with 
respect to the other messages, which leads to a jumping around of the track. This can further lead to a spurious 
splitting of the MMSI over two tracks. Concerning faulty AIS equipment, some MMSI numbers display positions 
that have a very high error rate, often in a systematic way, such as e.g. dropping the sign or one digit in the 
latitude. Such types of errors are more difficult to recognise automatically and also give rise to spuriously split 
tracks (more tracks under the same MMSI number).  

The  MSP  is  based  on  the  tracks,  because  the  MSP  plots  the  ship  positions  at  regular  time  intervals,  as 
interpolated from their irregular position reports. In the case of the real-time MSP, obviously only past position 
reports  are  used  to  construct  the  track,  followed  by  a  prediction  (extrapolation)  step  to  get  the  MSP  at  the 
current reference time. However, when using historic data, the MSP ship positions can be interpolated if there 
are  position  reports  on  both  sides  of  the  time  axis  available.  This  reduces  the  uncertainty  of  the  prediction. 
Nonetheless,  if  the  gap  between  two  position  reports  that  bracket  the  desired  MSP  time  is  too  large, 
interpolation  is  not  the  best  option.  For example,  to  calculate  an MSP  position  5 min  after  a  position  report 
when the next position report is 24 hours away, it is more reliable to extrapolate the previous position reports.  

For the current evaluation, the ships tracks (and consequently the MSPs) have been calculated along the lines 
above:  
  Make ordered time sequence of positions of a certain MMSI number;  
  Remove outlier points;  
  Check, by using a maximum speed criterion, if the track can be ascribed to a single vessel or should be split 

over multiple vessels;  

 

Interpolate the regularly time-sampled MSP positions from the series of irregularly time-sampled position 
reports of one track;  

  However,  instead  of  interpolate,  extrapolate  (forward  or  backward)  in  case  the  time  gap  between  two 

position reports is too large.  

This method leaves the choice of a number of parameters. In the present calculation, these parameters were 
set to:  
  Time allowance for unsynchronised clocks from different receivers: 60 seconds;  
  Maximum speed to allow in one track (else split into several tracks): 35 knots;  
  Maximum time gap to interpolate (else extrapolate): 18 hours;  
  Maximum extrapolation time (backward and forward): 9 hours;  
  MSP sampling interval: 15 minutes.  

MMSI numbers of which only  a  single position report  was received by all data sources combined, during the 
entire month, have been omitted (as these are ascribed to errors). However, when constructing the tracking 
from  a  subset  of  data  sources,  it  can  be  that  a  certain  MMSI  only  has  one  single  position  report,  or  a  few 
reports  in  one  small  time  interval.  Such  vessels  are  present  in  the  MSP  for  18  hours,  on  account  of  the 

24 

 
maximum extrapolation time of 9 hours used. The 9 hours are chosen as a compromise between on the one 
side  bridging  gaps  in  position  reports  of  ship  that move  around  in  the  AOI,  as  they  are  actually observed  to 
occur, and on the other side preventing undue extrapolation of the presence of a ship that has turned off its 
AIS transmitted e.g. because it went into a port. Ships that are seen to exit the AOI are not tracked anymore, as 
are ships of which the positions are extrapolated on land.  

Indicators for the MSP 

The evaluation has been done for the month of June 2015. The resulting MSPs can be characterised by a set of 
parameters that quantify their completeness and accuracy:  
  Total number of tracks seen in the entire month; 
  Average number of tracks (vessels) seen in the MSP at any one time; 
  Average number of position reports per track; 
  Time  from  MSP  to  closest  (earlier  or  later)  position  report,  measured  as  median,  mean  and  85  % 

percentile.  

The first two parameters quantify the completeness of the MSP: how many ships are tracked? The second two 
its  accuracy:  how  well  are  the  ship  positions  determined?  The  fourth  property,  time  from  MSP  to  nearest 
position report, is characterised by three parameters, because the distribution of its values over all MSP points 
is  very  asymmetric; most values  are  small,  but  a  few  are  very  large. The median  gives  the  best  indication of 
where most values are; the 85 % percentile is the value where 85 % of the MSP points remain below and gives 
a better indication of where the large values are.  

The  tracking  and  calculation  of  the  MSPs  has  been  done  with  the  data  for  the  month  of  June  with  various 
combinations  of  data  sources,  in  order  to  quantify  the  impact  of  the  various  data  types  and  the  number  of 
satellites  used.  The  tracking  calculation  is  relatively  fast  –  it  takes  only  7.5  minutes  on  a  simple  laptop  to 
calculate  all  the  15-min-spaced  MSPs  for  the  whole  month,  using  the  1.7  million  position  messages  of  the 
month as input. Nonetheless, a calculation of all MSPs for all possible combinations of sub-sets of satellites was 
not done, not even when limiting to maximally 55 combinations per number of satellites used, as was done for 
Figure  2-12.  That  would  still  lead  to  a  computation  time  of  463  x  7.5  minutes  =  58  hours.  Instead,  the 
performances were calculated only once for each number of satellites, by taking one arbitrary satellite to start 
with,  then  adding  a  second  (arbitrary)  one,  then  a  third,  etc.,  up  to  11.  Furthermore,  the  performance  was 
calculated  for  LRIT  only,  and  for  all  satellites  plus  LRIT.  Terrestrial  AIS  was  not  at  all  used  in  this  evaluation, 
because clock offsets in that data set introduced too many errors.  

Results for the MSP 

The results are tabulated in Table 2-1, and plotted in Figure 2-17. Inspecting the series of 1 to 11 AIS satellites, 
it can be seen that the number of vessels, seen in the whole month (column 1) as well as in the MSP at any one 
instant (column 2), levels off  for a higher  number of  satellites. This was  also seen before when counting the 
average daily number of MMSI numbers, which is in between the instantaneous number and the monthly total. 
The average number of position reports per track (column 3) on the other hand increases more or less linearly 
with  the  number  of  satellites.  The  measures  for  the  time  from  the  MSP  positions  to  their  nearest  position 
report (columns 4-6) decrease significantly with the number of satellites, but less so toward the high end. Their 
decrease  is a bit jumpy. This is because  if  a satellite is added with a similar orbit as the previous ones, even 
though  it  may  add  many  messages,  most  of  those  will  come  at  the  same  time  as  the  messages  already 
considered,  leaving  long  gaps  unfilled;  but  if  a  satellite  with  a  different  orbit  is  added,  it  will  pass  over  at 
different times, filling gaps in the track. On the whole, after 7 satellites, there is almost no more improvement. 

Regarding LRIT (first line in the table, leftmost points in the figures), it tracks much fewer ships, and has much 
fewer  reports  per  ship.  But  the  performance  in  terms  of  mean  time  to  nearest  report  (at  1.8  hours)  is 
comparable  to  that  of  3  AIS  satellites.  The  performance  in  terms  of  the  85  %  percentile  (at  2.78  hr)  is  even 
comparable  to  that  of  6  AIS  satellites,  meaning  that  even  though  3  AIS  satellites  have  a  good  performance 
averaged over all ships, they leave a part of the ships with long time gaps.  

25 

 
 
 
Column 
Data 

LRIT 
1 sat 
2 sat 
3 sat 
4 sat 
5 sat 
6 sat 
7 sat 
8 sat  
9 sat 
10 sat 
11 sat 
11 sat + LRIT 

1 
Total 
# tracks 
in month 
727 
4940 
5002 
5056 
5075 
5119 
5123 
5153 
5157 
5160 
5162 
5164 
5183 

2 
Average 
# tracks 
in MSP 

165.2 
1187.6 
1226.1 
1262.4 
1275.6 
1314.7 
1319.5 
1331.9 
1334.5 
1338.6 
1341.2 
1344.6 
1354.4 

3 
Average 
# reports 
per track 
25.3 
421.1 
607.5 
798.7 
811.9 
1165.4 
1343.6 
1542.7 
1705.6 
2008.6 
2193.8 
2441.6 
2436.1 

4 
Median time 
to nearest 
report (hr) 

1.633 
2.417 
2.177 
1.353 
1.304 
0.746 
0.724 
0.355 
0.328 
0.300 
0.279 
0.257 
0.255 

5 
Mean time 
to nearest 
report (hr) 
1.800 
2.624 
2.407 
1.843 
1.791 
1.396 
1.375 
1.034 
1.005 
0.972 
0.943 
0.912 
0.891 

6 
<85 % time 
to nearest 
report (hr) 
2.783 
4.687 
4.418 
3.613 
3.548 
2.871 
2.859 
2.348 
2.290 
2.226 
2.161 
2.091 
2.027 

Table 2-1. Properties of the MSPs based on tracking using (from top to bottom) LRIT data only, one AIS satellite, two 
satellites, three, etc., to 11 satellites, and at the bottom 11 AIS satellites + LRIT. The columns are explained in the text.  

Figure 2-17. Performance indicators of the MSP after tracking, as a function of using: only LRIT data (at 0 on the 
horizontal axis, o symbol); using the data from 1 to 11 satellites; and using 11 satellites plus LRIT (at 12 on the 
horizontal axis, + symbol). Top left: The total number of tracks seen during the whole month of June 2015. Top right: 
the average number of tracks seen in the MSP at any one time. Bottom left: The average number of position reports 

26 

 
 
 
 
 
 
per track. Bottom right: The time between the MSP and the nearest position report in hours, as median (lowest 
values, blue); mean (intermediate values, green); and 85 % percentile (highest values, red).  

Figure 2-18 shows how the number of tracks in the MSP, of which Table 2-1 column 2 gives the average, varies 
with  time.  The  graph  for  the  one-satellite  case  (top  right)  shows  an  oscillation  that  is  related  to  the  revisit 
periodicity  of  the  satellite  as  dictated  by  its  orbit.  If  the  maximum  extrapolation  time  of  9  hours  would  be 
extended, this oscillation would be further dampened, but that would also give rise to spurious vessel presence 
in the MSP. In the graph for 6 satellites (bottom left) the oscillation is much reduced as the various satellites 
are not fully synchronised. The LRIT (top left) does not show this regular oscillation, only some random noise 
(LRIT also does not contain Class B vessels).  

Figure 2-18. The number of tracks in each 15-minute MSP for the month of June 2015, split in Class A and Class B 
vessels and their sum. Top left: LRIT only. Top right: one AIS satellite. Bottom left: 6 satellites. Bottom right: 11 AIS 
satellites.  

Figure  2-19  visualises  the  asymmetry  of  the  distribution  of  the  times  from  the  MSP  points  to  their  nearest 
position report. It is from these distributions that the median, mean and 85 % percentile used above have been 
calculated.  The  histogram  for  LRIT  (top  left)  directly  reflects  the  6  hourly  reporting:  most  MSP  points  are 
between 0 and 3 hours away from their nearest position report. In some cases where an LRIT report is missed, 
it is between 0 and 6 hours. There is a tail up to 9 hours, the maximum time for extrapolation, when no more 
LRIT reports are received – but not due to leaving the AOI because then the tracking is stopped.  

The histogram for one AIS satellite (top right) also reflects a periodicity due to the satellite orbit.  

27 

 
 
 
 
 
 
 
 
The histogram for 6 AIS satellites (bottom left) shows a peak left of 1 hr, a plateau left of 3.5 hr and a tail up to 
6-9 hr. The distribution for all 11 AIS satellites (bottom right) shows that most MSP points are very close to a 
position report, with the peak to the left of around 0.5 hr; but a few are up to 3-4 hr away, and very few up to 9 
hr. Adding the LRIT to 11 AIS satellites (not shown) does not change the shape of the distribution much.  

Figure 2-19. The distribution of the times between an MSP point and its nearest position report. The horizontal scale 
is in hours and runs from 0 to 9. The vertical scale counts MSP points, from all tracks taken together. Top left, LRIT 
only. Top right, one AIS satellite. Bottom left, 6 AIS satellites. Bottom right, all 11 AIS satellites.  

Instead of making a histogram of the individual values of the time from MSP to nearest position report from all 
MSP points together, it is also possible to first average over all MSP points of one track, and thus get one mean 
value per track. These mean values can then be plotted in a histogram. The result is displayed in Figure 2-20. 
These  distributions  are  narrower  than  the  previous  ones,  because  they  are  distributions  of  values  that  have 
already  been  averaged.  It  shows  that  for  LRIT  (top  left),  most  tracks  have  a  mean  time  of  MSP  to  nearest 
position report between 1.5 and 2.1 hr. For one AIS satellite (top right) it is between 2 and 3 hr. For 6 satellites 
it is between 0.7 and 1.4 hr; and for 11 satellites between 0.2 and 1 hr.  

28 

 
 
 
 
 
 
 
 
Figure 2-20. The distribution of the mean values for each track of the time from MSP to nearest position report. The 
horizontal scale is in hours. The vertical scale counts tracks. Top left: LRIT only. Top right: One AIS satellite. Bottom 
left: Six satellites. Bottom right: All 11 satellites.  

29 

 
 
 
 
 
 
3  Monthly ship density maps 

Procedure 

3.1 
Ship density maps have been compiled on a monthly basis. The data that have been used are the satellite AIS 
data and the LRIT. Terrestrial AIS has not been used, to avoid a bias in the areas that are covered by that, which 
would  prejudice  the  density  maps  because  those  areas  are  so  unequally  distributed  (see  Figure  2-4  bottom 
left). As a first step, the MSPs are constructed by the tracking  process described in the previous chapter. The 
same parameters as mentioned there have been used in the tracking. Another reason why terrestrial AIS was 
not used is because the clock errors in there (discussed in the previous chapter) lead to erroneous tracks. Then, 
each of the MSP ship positions is counted in a grid with a cell size of 0.025 degrees, which is approximately 1.5 
nautical miles or 2.8 km. The resulting 2-D density map is convolved with a boxcar of 6 x 6 grid cells, leading to 
a  resolution  of  0.15  degrees  which  is  approximately  9  nautical  miles  or  16.7  km.  The  resulting  numbers  are 
normalised to obtain ship density in units of the average number of ships present at any one time per square 
degree. The density maps cover the entire AOI.  

The tracking also allows to compute the  speed of the  ship, derived from  a ship’s  consecutive MSP positions. 
This enables the separation of MSP positions by speed. This provides additional insight in the ship behaviour: 
fast  moving  ships  are mostly  in  transit,  while  slow  moving  ships out  on the open  sea  are  mostly  engaged  in 
some activity, like fishing or exploration. Ships are also slow moving in and near ports. Some trials have shown 
that a speed limit of 6.5 knots gives a useful separation between transit and other activity. Therefore, the ship 
density maps have been split into these two domains: fast moving ships (> 6.5 knots) and slow moving ships (< 
6.5 knots). The latter also include stationary ships such as found moored in ports or at anchor in port vicinity.  

Results 

3.2 
The  figures  in  this  section  show  the  ship  density  maps  for  each  month  between  September  2014  and 
September 2015, split into fast moving and slow moving (including stationary) ships. The scale is logarithmic to 
take into account the large dynamic range of the densities, as previously discussed with Figure 2-4, and signifies 
the 10-logarithm of the average number of ships present at any one time per square degree unit of surface. 
The  very  high  numbers  are  particularly  reached  in  ports,  in  areas  that  are  so  small  that  they  are  difficult  to 
recognise at the scale of the figures (small red dots on the coast). The size of the resolution cell of 0.15 degrees 
is printed here at a size of 0.28 mm.  

The ship density maps of the fast moving ships  are always on the left. They are dominated by the main ship 
traffic routes. They do not vary much from month to month. The density maps of the slow moving ships are 
always on the right. Most of the structure in those maps can be attributed to fishing activity. Linear features 
are due to a few ships transiting at a slow speed. These maps show much monthly variation, mostly reflecting 
the shifting of the fishing areas with the season. In many places, the ship density displays a strong correlation 
with EEZ boundaries (the pink lines are non-authoritative EEZ boundary indications, for orientation only).  

The  PMAR-MASE  data  stream  worked  at  full  capacity  between  15  Sep  2014  and  15  Sep  2015.  Nevertheless, 
some data were already coming in during the first half of Sep 2014 (for setting up and testing the system), and 
were still coming in during the second half of Sep 2015 (as some providers did not immediately switch off their 
data flow). Therefore, density maps of these entire months could be produced.  

30 

 
 
Figure 3-1. Ship density map for September 2014. Fast moving ships on the left, slow moving ships on the right. 

Figure 3-2. Ship density map for October 2014. Fast moving ships on the left, slow moving ships on the right. 

31 

 
 
 
 
 
 
 
Figure 3-3. Ship density map for November 2014. Fast moving ships on the left, slow moving ships on the right. 

Figure 3-4. Ship density map for December 2014. Fast moving ships on the left, slow moving ships on the right. 

32 

 
 
 
 
 
 
 
Figure 3-5. Ship density map for January 2015. Fast moving ships on the left, slow moving ships on the right. 

Figure 3-6. Ship density map for February 2015. Fast moving ships on the left, slow moving ships on the right. 

33 

 
 
 
 
 
 
 
Figure 3-7. Ship density map for March 2015. Fast moving ships on the left, slow moving ships on the right. 

Figure 3-8. Ship density map for April 2015. Fast moving ships on the left, slow moving ships on the right. 

34 

 
 
 
 
 
 
 
Figure 3-9. Ship density map for May 2015. Fast moving ships on the left, slow moving ships on the right. 

Figure 3-10. Ship density map for June 2015. Fast moving ships on the left, slow moving ships on the right.  

35 

 
 
 
 
 
 
 
Figure 3-11. Ship density map for July 2015. Fast moving ships on the left, slow moving ships on the right.  

Figure 3-12. Ship density map for August 2015. Fast moving ships on the left, slow moving ships on the right.  

36 

 
 
 
 
 
 
 
Figure 3-13. Ship density map for September 2015. Fast moving ships on the left, slow moving ships on the right.  

37 

 
 
 
 
4 

Ship detections from satellite SAR 

Technology and method 

4.1 
The ship reporting systems (AIS and LRIT) provide very rich information on ship presence and behaviour, but 
only for the reporting ships. Ships that are not transmitting on these systems are not seen. In order to find out 
more about the presence of such ships on the open sea away from coastal monitoring sites, Earth observation 
satellites can be used. Such satellites make images of the Earth surface, also over sea. Satellites carry optical 
cameras for images in the optical (visual) domain, but also radars for images in radar wavelengths. This study 
uses radar images to prevent the obscuration by clouds that hinders optical imaging from space.  The type of 
radar used on satellites is the  Synthetic Aperture Radar (SAR), which is a method to still get good resolution 
even from a long distance. The character of the SAR image data is very different from that of the ship reporting 
data. The satellite  SAR provides  an image of the  sea surface of a limited size, in which the ships show  up as 
bright  points  against  a  darker  background.  There  is  no  possibility  to  identify  the  ships  that  are  seen;  just  to 
notice their presence and obtain their geographic position. Small ships are not detected in the image, the exact 
size limit being dependent on many factors including image type and sea conditions. Other effects on the sea 
surface  besides  ships  may  cause  false  alarms,  such  as  breaking  waves  or  interference  from  nearby  land.  A 
satellite  SAR  image  does  not  show  any  movement,  and  can  only  be  taken  once  every  so  often;  there  is  no 
possibility for continuous or persistent monitoring.  

Satellite  SARs  can  make  images  in  different  modes:  wide-area  images  at  low  resolution,  detecting  only  big 
targets; or small-area images at high resolution, where targets down to just a few meters can be detected if the 
circumstances are right. The latter are less useful for maritime surveillance, because their coverage is too small 
(of the order of 5-10 km wide). The wide-area images on the other hand can have coverages of 150 km to 400 
km wide.  

The targets detected in a satellite image, taken at a certain time, are compared to the ships that are known to 
be present at that time from the reporting data. In this way it is established for each ship detected in the SAR 
image whether it was already known from the reporting data, or whether it is a non-reporting ship. In addition, 
there  could  be  reporting  ships  that  are  not  detected  in  the  SAR  image.  This  process  is  called  correlation 
(between the SAR ship detections and the reported ship positions). The correlation was done using the JRC’s 
Blue Hub software.  

The  ship  detections  from  the  satellite  SAR  images  are  referred  to  as  VDS  (for Vessel  Detection  System). The 
ship positions of the reporting ships at the time of the satellite image are referred to as Interpolations, because 
these positions are interpolated from the bracketing ship position reports (i.e., the reports available from just 
before and just  after the  time of the satellite image). The outcome of the  correlation process leads to three 
kinds of target:  

  Correlated target, where a VDS position and an Interpolation match; 
  Uncorrelated  VDS,  where  a  VDS  position  (satellite  SAR  detection)  does  not  have  a  matching 

Interpolated reported position – so a non-reporting ship (or a false alarm);  

  Uncorrelated Interpolation, where an interpolated position from a reporting ship is not matched with a 

VDS target, so a ship that is not detected in the satellite SAR image.  

Satellite SAR data used 

4.2 
Two different satellite SARs were used, Radarsat-2 and Sentinel-1.  

Radarsat-2  is  commercially  operated  by  the  Canadian  company  MDA.  Its  images  are  commercially  offered 
through  European  resellers.  The  PMAR-MASE  project  has  bought  29  Radarsat-2  images.  Of  those,  21  were 
“ScanSAR Narrow B” (SNB) mode that has 300 km x 300 km image size at 50 m resolution; and 8 were “Wide 
Fine” mode that has 150 km x 150 km size at 8 m resolution. All images were taken in the period 15 Aug – 9 Sep 
2015.  Those  images  were analysed  with  the  JRC’s “SUMO”  software  for  ship  detection.  The  analysis  is  semi-
automatic; first, the images are subjected to an automatic target detection and correlation process, and then 
the results are verified by an operator.  

In  addition,  ship  detection  results  were  received  through  a  scientific  exchange  from  Defence  Research  and 
Development Canada (DRDC) from 5 Radarsat-2 images in MSSR-DVWF mode, which is an extra wide mode for 

38 

 
ocean surveillance that was not commercially available (at the time – it is now). It has a 450 km swath at 40 m 
resolution. These images were acquired in the period 24 Feb – 10 Mar 2015.  

Sentinel-1 is a relatively new SAR satellite under the EU’s Copernicus program, operated by the European Space 
Agency (ESA) on behalf of the European Commission. Its images are available for free. However, unlike for the 
commercial satellite SARs, it is not possible to request the collection of Sentinel-1 images in specific locations 
and  times.  So  one  can  only  use  what  has  been  collected,  if  indeed  anything  of  interest  has  been  collected. 
Consulting  ESA’s  Sentinel-1  archive  for  images  within  the  PMAR-MASE  AOI  and  time  frame,  many  hundred 
image were found. Most are on the coast. All these images have been downloaded, plus some more that were 
acquired  further  south,  just  outside  the  AOI;  in  total  1,500  Sentinel-1  images.  Also  these  images  have  been 
analysed with the SUMO ship detector, but it is not possible to do the manual verification for so many images. 
Therefore,  most  of  these  1,500  images  were  analysed  in  the  so-called  ‘batch’  mode,  fully  automatic.  Their 
results are therefore less reliable than the manually verified ones. Only 12 images from ESA’s Sentinel-1 archive 
were  selected  for  manual  verification;  these  were  acquired  over  the  Somali  coast.  They  are  all  of  the 
“Interferometric  Wide”  (IW)  mode  that  has  250 km  swath  width  at  20  m  resolution.  Most  of these 12  were 
acquired between 24 Dec 2014 and 3 Jan 2015, and two on 12 Sep 2015.  

The  correlation  of  the  ships  detected  in  the  satellite  SAR  images  (the  VDS)  was  done  with  the  AIS  and  LRIT 
collected during 15 Sep 2014 – 15 Sep 2015 as described in the first chapters of this report.  

4.3 
Results 
Radarsat-2 SNB and Wide Fine 

Figure 4-1 shows the locations and dates of the commercially obtained Radarsat-2 images; the ScanSAR Narrow 
B mode images on the left and the Wide Fine  mode on the right. The acquisitions have been in strips of 2-6 
images long.  

Figure 4-1. Locations and dates of the 29 commercially procured Radarsat-2 images. Left: ScanSAR Narrow B mode 
(300 km wide). Right: Wide Fine mode (150 km wide). (Google Earth map backgrounds.) 

39 

 
 
 
 
 
Figure 4-2 shows the results of the ship detection and the correlation. Only 38 ships have been detected in the 
29 images, which is not a high number (average 1.3 ship per image). These were compared to 39 Interpolated 
reporting  ship  positions.  This  led  to  34  correlated  targets,  4  uncorrelated  VDS  (non-reporting  ships),  and  5 
uncorrelated Interpolations (undetected ships).  

Figure 4-2. Left: The 29 commercial Radarsat-2 images (blue squares) with the correlated ships (34 green stars) and 
the non-reporting ships (4 red stars). Right: Tabular overview per image, the left column specifying the image date 
and start time (UTC).  

Radarsat-2 MSSR-DVWF 

Figure 4-3 shows the results for the Radarsat-2 MSSR-DVWF mode images of which the ship detections were 
provided by DRDC. The number of detected ships is much higher, at 216 in total (average 43.2 per image). One 
reason for this is that the MSSR images are bigger than the ScanSAR Narrow B and Wide Fine images. But even 
more  than  that,  the  MSSR  images  have  been  taken  in  areas  of  higher  ship  density,  around  the  islands  of 
Mauritius and La Réunion and between Aden and Djibouti.  

Another reason why these images originally had more detections was that, upon closer inspection, they were 
found  to  contain  many  false  alarms  caused  by  coastal  features  (see  Figure  4-4).  These  have  been  removed, 
however, and are not anymore included in the detection counts and plots.  

Figure 4-5 shows some details of two of the images with a high number of detections.  

40 

 
 
 
 
Figure 4-3. The correlation results of the five Radarsat-2 images in MSSR-DVWF mode of which the ship detections 
were provided by DRDC. A total of 206 targets were detected, and compared with 160 Interpolations, which led to 
137 correlated detections (green stars) and 69 uncorrelated ones (red stars, non-reporting ships).  

41 

 
 
 
 
Figure 4-4. Detections (stars) in the Radarsat-2 MSSR-DVWF image of the next figure (left side), overlaid over an 
optical satellite image background. The detections that touch the coast are attributed to false alarms due to features 
on the coast. These have been removed from the count of correlated / non-correlated ships. Optical background 
image from ESRI World Imagery.  

Figure 4-5. Correlations for two of the Radarsat-2 MSSR-DVWF images that have many targets. Left, Aden and 
Djibouti. Right, La Réunion and Mauritius. Correlated VDS are green stars, correlated Interpolations are black 
squares, uncorrelated VDS are red stars, uncorrelated Interpolations are yellow squares. Map background from 
Natural Earth.  

Sentinel-1 IW manually verified 

The results of the ship detection and correlation of the manually verified Sentinel-1 images on the Somali coast 
are shown in Figure 4-6. In the 12 images, a total of 28 VDS targets are detected (average 2.3 target per image). 
These  are compared with 10  Interpolated  reporting  ship  positions.  This  leads to  9  correlated  targets  and  19 
uncorrelated ones.  

Many detected targets are very close to the coast. A closer inspection has shown that several of them are in 
fact ship wrecks. Two examples are shown in Figure 4-7. These are not counted under the 28 VDS targets.  

42 

 
 
 
 
 
 
 
Figure 4-6. Twelve Sentinel-1 images over the Somali coast (the lowest ones extending over the Kenya coast). 
Correlated targets are green stars (9) and uncorrelated targets are red stars (19).  

43 

 
 
 
Figure 4-7. Two examples of VDS detections close to the Somali coast that are found to be ship wrecks. The map on 
the left is a part of the previous figure, except that here the detections near the coast are still included as red stars. 
The two b/w image chips in the middle are extracts from the Sentinel-1 image around the two detections in question. 
The two optical images on those locations are taken from Google Earth.  

Overall numbers of correlation and non-reporting ships for the verified images 

The results of all verified SAR images are put together inTable 4-1. This compilation brings out the variation in 
the  number  of  detections  per  unit  area  (5th  column),  and  in  the  fraction  of  uncorrelated  detections  (9th 
column).  The  Radarsat-2  SNB  and  Wide  Fine  images  were  taken  in  relatively  empty  areas,  and  the  fraction 
uncorrelated detections (non-reporting ships) is low, 0-12 %. The Sentinel-1 images over the coast of Somalia 
have  a  high  number  of  uncorrelated  detections,  even  after  the  detections  very  close  to  the  coast  (some  of 
which are confirmed wrecks) have been removed. Finally, the Radarsat-2 MSSR images were taken over areas 
of  much  higher  ship  density,  where  also  an  increased  presence  of  smaller,  non-reporting  ships  may  be 
expected.  The  fraction  of  non-reporting  ships  of  33  %  seems  reasonable  there.  The  overall  fraction  of  non-
reporting ships, taking all data together, is 34 %.  

Sensor 

Mode 
Swath Resolution 

# Imag 

# VDS 

Radarsat-2  SNB  

300 km 40 m 

Radarsat-2  Wide Fine  
150 km 8 m 
Radarsat-2  MSSR-DVWF 
450 km 40 m 
IW  
250 km 20 m 

Sentinel-1 

Overall 

21 

8 

5 

12 

46 

# VDS /  
104 km2 
0.17 

0.28 

# Interpol 

# Corr 

34 

5 

29 

5 

33 

5 

206 

2.03 

160 

137 

28 

0.37 

10 

9 

272 

0.71 

209 

180 

# VDS 
 uncorr 
4 

% VDS 
 uncorr 
12 % 

# Interpol  
uncorr 
5 

0 

69 

19 

92 

0 % 

33 % 

68 % 

34 % 

0 

23 

1 

29 

Table 4-1. Ship detection and correlation results from all verified satellite SAR images.  

44 

 
 
 
 
 
Sentinel-1 unverified 

The  results  of  the  ship  detection  and  correlation  of  all  Sentinel-1  images,  analysed  in  batch  mode  without 
manual verification, are shown in Figure 4-8. A total of 18,000 targets were detected; 14,000 of which inside 
the AOI. Of those targets, about 40 % could not be correlated with known / reporting ships. This number likely 
overestimates the percentage of non-reporting ships, because the fully automatic, unverified results are bound 
to contain many false alarms. But in order of magnitude it compares with the manually verified overall 34 %.  

Figure 4-8. The outlines (squares) of all Sentinel-1 images downloaded from the ESA archive up to 15 Sep 2015, and 
the targets (dots) detected in them. A green outline means that the image has been acquired only once; a red outline 
means that several images over that location have been collected. The large blue box is the PMAR-MASE AOI.  

45 

 
 
 
Oil pollution 

It is well known that SAR images of the sea surface are suitable to reveal oil spills. In one Sentinel-1 image, a 
dark trail is seen behind a ship that has the characteristics of an oil slick (see Figure 4-9). At the tip of the dark 
oil trail, a bright spot is seen that signifies a ship. Comparing with the ship reporting data, there is a reporting 
ship whose position matches that of the bright spot and whose track closely matches the oil trail. The oil trail 
has  drifted  slightly  off  the  ship  track  due  to  the  effect  of  ocean  currents  and  wind.  The  Sentinel-1  image  in 
question is of the Extra Wide (EW) mode that has wider swath but lower resolution than the IW mode images 
that were used for the ship detection.  

Figure 4-9. Part of a Sentinel-1 EW-mode image off Mozambique on 28 Aug 2015, with in green the AIS positions of a 
ship overlaid. The dark trail is likely an oil spill. The ship is seen as a bright point at the tip of the trail. Sentinel-1 
image © Copernicus 2015.  

46 

 
 
 
 
Acknowledgments 

LRIT data were obtained courtesy of the National Competent Authorities of the Flags that participate in the EU 
LRIT Data Centre, with the help of EMSA (European Maritime Safety Agency).  

Satellite  AIS  data  from  NORAIS,  AISSat-1  and  AISSat-2  were  obtained  courtesy  of  the  Norwegian  Coastal 
Administration and the Norwegian Defence Research Establishment (FFI).  

MSSIS  data  were  obtained  courtesy  of  the  Volpe  Center  of  the  U.S.  Department  of  Transportation,  the  U.S. 
Navy and SPAWAR.  

Ship detections from RADARSAT-2 MSSR mode images were provided by Paris W. Vachon of Defence Research 
and Development Canada.  

A coastline data base of OpenStreetMap, © OpenStreetMap contributors, was used.  

Map  backgrounds  were  used  of  Google  Earth;  of  Environmental  Systems  Research  Institute  (ESRI  –  ‘World 
Ocean’, ‘World Imagery’ and ‘World Topographical’); of TRANSAS Web Map Services (Nautical Charts); and of 
Natural Earth (free vector and raster map data @ naturalearthdata.com).  

Sentinel-1 images are © Copernicus 2014-2015.  

Radarsat-2 images are © MDA 2015 and were purchased from MDA / e-Geos.  

Satellite AIS data were purchased from exactEarth, Orbcomm / LuxSpace and SpaceQuest.  

47 

 
 
 
Europe Direct is a service to help you find answers to your questions about the European Union 
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You can obtain their contact details by sending a fax to (352) 29 29-42758. 

European Commission 
EUR 27612 EN – Joint Research Centre – Institute for the Protection and Security of the Citizen 

Title: Maritime Awareness Systems Performance in the Western Indian Ocean 2014-2015 

Author(s): Harm Greidanus, Marlene Alvarez, Vincenzo Gammieri, Carlos Santamaria, 

 Alfredo Alessandrini, Pietro Argentieri 

Luxembourg: Publications Office of the European Union 

2015 – 49 pp. – 21.0 x 29.7 cm 

EUR – Scientific and Technical Research series – ISSN 1831-9424 

ISBN 978-92-79-54033-2 

doi:10.2788/420868 

Abstract 

The PMAR-MASE project aimed to provide practical experience to authorities in the Eastern-Southern Africa / Indian 
Ocean region on wide-area maritime awareness. Data from automatic ship reporting systems (AIS and LRIT) have been 
received during one year over a large Area of Interest (AOI) in the Western Indian Ocean. The data were used to 
construct the Maritime Situational Picture (MSP) over the AOI, i.e. the map with the current positions of all reporting 
ships. The MSP has been served continuously in real time to two operational users in the region, between 15 Sep 2014 
and 15 Sep 2015. The data have also been used to make monthly ship traffic density maps. Finally, a number of satellite 
images have been collected to probe for the presence of ships that are not seen on the reporting systems.  
   This report assesses the quality (completeness and timeliness) of the ship reporting data and the resulting MSP; it 
presents the derived monthly ship density maps, split between slow moving and fast moving traffic in order to 
understand ship activity; and it present results on the fraction of non-reporting ships based on the analysis of the 
satellite images.  
   The ship reporting data that were used are AIS data received by up to 16 satellites of four different operators, AIS data 
from various coastal networks in the region, and LRIT data from the Flags that use the EU LRIT Data Centre (“EU Flags”). 
Fusing the data from these systems – that amounts to more than 566,000 incoming ship position messages per day 
(figures are given here for the month of June 2015) – it is found that on average, a total of 1,522 ships are seen in the 
AOI on a daily basis. Of those, 11 (0.7 %) are only seen by LRIT, in spite of the fact that LRIT contributes only a tiny 614 
position reports per day (0.1 %) on average.  
   A sampling of the non-reporting ship traffic by satellite imaging has shown that 34 % of the ships detected in satellite 
radar images are not seen in AIS or LRIT. However, this fraction varies much from one location to another; among the 
limited locations sampled, it is highest  off the Somali coast.  

L
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49 

doi:10.2788/420868 

ISBN 978-92-79-54033-2