Maritime Dataset

## Overview

Maritime Dataset is a dataset for object detection tasks - it contains Objects annotations for 664 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

respectively.

In the context of the MSc dissertation "The Multi-Object Tracking with Multimodal Information for Autonomous Surface Vehicles", an annotated and classified maritime dataset was created in order to train a machine learning object detector. This dataset combines images from open-source datasets (such as the Singapore Maritime Dataset and the Kaggle Boat Types Recognition Dataset) while adding data collected with the Sense ASV in two portuguese ports (Leixões and Viana do Castelo). The content is reorganised in 9 categories (ore carrier, bulk carrier, container ship, cruise ship, ferry boat, sail boat, fishing boat, small boat, uncategorized), grouping up some less represented categories from the previous datasets with similar characteristics. The resulting dataset, contains 9044 images and their respective 21170 annotations.

We have created Singapore Maritime Dataset, using Canon 70D cameras around Singapore waters. All the videos are acquired in high definition (1080X1920 pixels). We divide the dataset into parts, on-shore videos and on-board videos, which are acquired by a camera placed on-shore on a fixed platform and a camera placed on-board a moving vessel, respectively. The videos are acquired at various locations and routes and thus do not necessarily capture the same scene. The third part is Near Infrared (NIR) videos which are also captured using another Canon 70D camera with the hot mirror removed and Mid-Opt BP800 Near-IR Bandpass filter.

Acknowledgement- Dataset has been captured by Dilip K. Prasad and annotated by student volunteers. Dataset has been captured on various environmental conditions like before sunrise (40 min before sunrise), sunrise, mid-day, afternoon, evening, after sunset (2hrs after sunset), Haze and Rain from July 2015 to May 2016.

Optical lens used for all the 3 sub-dataset - Canon EF 70-300mm f/4-5.6 IS USM

Following papers to be cited for using this dataset

D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabaly, and C. Quek, "Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey," IEEE Transactions on Intelligent Transportation Systems (IEEE), 18 (8), 1993 - 2016, 2017. (preprint PDF)

Other papers on this dataset from our group

D. K. Prasad, H. Dong, D. Rajan, and C. Quek, "Are object detection assessment criteria ready for maritime computer vision?," IEEE Transactions on Intelligent Transportation Systems, 2019.

D. K. Prasad, C.K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, "Object detection in maritime environment: Performance evaluation of background subtraction methods," IEEE Transactions on Intelligent Transportation Systems, 22 (5), 1787-1802, 2019.

D. K. Prasad, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, “MuSCoWERT: multi-scale consistence of weighted edge Radon transform for horizon detection in maritime images,” Journal of Optical Society America A, vol. 33, issue 12, pp. 2491-2500, 2016.

D. K. Prasad, C.K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, and C. Quek, “Maritime situational awareness using adaptive multisensory management under hazy conditions,” 5th International Maritime-Port Technology and Development Conference (MTEC 2017), Singapore, 26-28 April, 2017.

D. K. Prasad, C.K. Prasath, D. Rajan, C. Quek, L. Rachmawati, and E. Rajabally, “Challenges in video based object detection in maritime scenario using computer vision,” 19th International Conference on Connected Vehicles, Zurich, 13-14 January, 2017.

D. K. Prasad, D. Rajan, C. Krishna Prasath, L. Rachmawati, E. Rajabally, and C. Quek, “MSCM-LiFe: Multi-Scale Cross Modal Linear Feature for Horizon Detection in Maritime Images,” IEEE TENCON, Singapore,22-25 Nov, 2016.

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

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

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

Maritime Detection IR

## Overview

Maritime Detection IR is a dataset for object detection tasks - it contains Boats Buoys Person annotations for 581 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

VAIS_RGB+SMD+MARITIME+WSODD+MARVEL

## Overview

VAIS_RGB+SMD+MARITIME+WSODD+MARVEL is a dataset for object detection tasks - it contains VESSEL annotations for 24,648 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Turku UAS DeepSeaSalama-GAN dataset 1 (TDSS-G1) is a comprehensive image dataset obtained from a maritime environment. This dataset was assembled in the southwest Finnish archipelago area at Taalintehdas, using two stationary RGB fisheye cameras in the month of August 2022. The technical setup is described in the section “Sensor Platform design” in report “Development of Applied Research Platforms for Autonomous and Remotely Operated Systems” (https://www.theseus.fi/handle/10024/815628).

The data collection and annotation process was carried out in the Autonomous and Intelligent Systems laboratory at Turku University of Applied Sciences. The dataset is a blend of original images captured by our cameras and synthetic data generated by a Generative Adversarial Network (GAN), simulating 18 distinct weather conditions.

The TDSS-G1 dataset comprises 199 original images and a substantial addition of 3582 synthetic images, culminating in a total of 3781 annotated images. These images provide a diverse representation of various maritime objects, including motorboats, sailing boats, and seamarks.

The creation of TDSS-G1 involved extracting images from videos recorded in MPEG format, with a resolution of 720p at 30 frames per second (FPS). An image was extracted every 100 milliseconds.

The distribution of labels within TDSS-G1 is as follows: motorboats (62.1%), sailing boats (16.8%), and seamarks (21.1%).

This distribution highlights a class imbalance, with motorboats being the most represented class and sailing boats being the least. This imbalance is an important factor to consider during the model training process, as it could influence the model’s ability to accurately recognize underrepresented classes. In the future synthetic datasets, vision Transformers will be used to tackle this problem.

The TDSS-G1 dataset is organized into three distinct subsets for the purpose of training and evaluating machine learning models. These subsets are as follows:

• Training Set: Located in dataset/train/images, this set is used to train the model. It learns to recognize the different classes of maritime objects from this data.
• Validation Set: Stored in dataset/valid/images, this set is used to tune the model parameters and to prevent overfitting during the training process.
• Test Set: Found in dataset/test/images, this set is used to evaluate the final performance of the model. It provides an unbiased assessment of how the model will perform on unseen data.

The dataset comprises three classes (nc: 3), each representing a different type of maritime object. The classes are as follows:

1. Motor Boat (motor_boat)
2. Sailing Boat (sailing_boat)
3. Seamark (seamark)

These labels correspond to the annotated objects in the images. The model trained on this dataset will be capable of identifying these three types of maritime objects. As mentioned earlier, the distribution of these classes is imbalanced, which is an important factor to consider during the training process.

Tracking objects in maritime environments remains a challenging task due to dynamic water surfaces, occlusions, reflections, and camera-induced motion blur. To advance research in this domain, we introduce MVTD (Maritime Visual Tracking Dataset), a comprehensive benchmark comprising 182 sequences and over 150,000 frames, each annotated with precise bounding boxes. The dataset captures a wide variety of real-world maritime conditions and includes annotations for 9 critical attributes, such as occlusion, low resolution, background clutter, and motion blur, enabling fine-grained performance analysis. Additionally, MVTD includes four object types commonly encountered in maritime surveillance scenarios. To evaluate current capabilities, we benchmark 14 SOTA trackers on MVTD, revealing notable performance limitations and motivating the development of domain specific tracking solutions for maritime environments.

Snapshot img

Maritime Information Market Size 2025-2029

The maritime information market size is forecast to increase by USD 1.2 billion at a CAGR of 10% between 2024 and 2029.

The market is experiencing significant growth, driven by the increase in seaborne trade and the digital transformation of the maritime industry. Cybersecurity concerns are becoming increasingly crucial as the sector becomes more digitized, leading to substantial investments in advanced security solutions. However, the high cost of implementing these technologies poses a challenge for smaller players in the market. The market's strategic landscape is characterized by intense competition and a focus on innovation, as companies seek to capitalize on the opportunities presented by the growing demand for real-time data and analytics in maritime operations. To remain competitive, companies need to stay abreast of the latest trends and invest in technologies that enhance operational efficiency, improve safety, and ensure regulatory compliance.
By leveraging advanced analytics, machine learning, and automation, maritime organizations can optimize their operations, reduce costs, and enhance their overall competitiveness in the global marketplace. In summary, the market is poised for continued growth, driven by the increasing importance of cybersecurity, digital transformation, and the need for real-time data and analytics in maritime operations. Companies seeking to capitalize on these opportunities must invest in innovative technologies and stay ahead of the competition to remain competitive in this dynamic market.

What will be the Size of the Maritime Information Market during the forecast period?

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The maritime industry encompasses various sectors, including technology, governance, safety procedures, decarbonization, logistics optimization, and information services. Maritime information services play a crucial role in enhancing domain awareness, incident response, intelligence, sustainability, and operations management. Autonomous shipping, data analytics, sensor networks, forecasting, automation, communication systems, navigation systems, environmental protection, law enforcement, risk management, situational awareness, cybersecurity solutions, and security operations are integral components of this market. Maritime governance ensures compliance with regulations and standards, while maritime safety procedures prioritize the well-being of crew members and vessels. Maritime decarbonization focuses on reducing greenhouse gas emissions, and maritime logistics optimization streamlines supply chain processes.
Maritime domain awareness involves monitoring and analyzing activities within the maritime domain, while maritime incident response addresses emergency situations. Maritime intelligence gathers and disseminates critical information, and maritime sustainability promotes eco-friendly practices. Maritime accident investigation determines the causes of incidents, and maritime operations management optimizes resources and processes. Maritime innovation drives technological advancements and improvements in these areas.

How is this Maritime Information Industry segmented?

The maritime information industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Commercial
  Government


Application

  MIA
  MIP
  VT
  AIS


Deployment

  Cloud
  On-Premises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Norway
    Spain
    The Netherlands
    UK


  Middle East and Africa



  APAC

    China
    Japan


  South America



  Rest of World (ROW)

By End-user Insights

The commercial segment is estimated to witness significant growth during the forecast period. The market encompasses various sectors, including port management, commercial shipping, hydrographic and charting, business intelligence, commercial fishing, and offshore energy. The increasing importance of safeguarding maritime resources from advanced security threats propels marine companies to adopt maritime information solutions. However, the intricacy of the maritime infrastructure has escalated due to the widespread adoption of advanced technologies, such as Automatic Identification Systems (AIS). Consequently, end-users encounter difficulties in managing these complex maritime information solutions. To address these challenges, companies provide converged maritime information management solutions, which integrate multiple solutions into one. For example, offering AISs in conjunction with vessel tracking options allows for a unified solution and eliminates the requirement for multiple investments.

Maritime communication, such as satellite and VHF radio, p

Areas of assumed shallow water (depth of less than approximately 2 metres in tidal waters and the shallower water in inland waters at full supply).

The US Voluntary Observing Ships (VOS) report surface marine observations in both real-time (FM-13 ship format) and delayed-mode (International Maritime Meteorological Tape - IMMT format). To do this, most operating vessels use e-logbook software that allows an observer to enter information, then the software can transmit a real-time report as well as save the same report in a different format to the ship's hard drive for later access, i.e. delayed mode observation (DM). Once in port, all DM reports stored on the hard drive are retrieved and sent to the National Climatic Data Center for archiving and processing. The e-logbook software used in this dataset is the SEAS v9.1 program and structures data in the IMMT-5 format.

NOAA is responsible for depicting on its nautical charts the limits of the 12 nautical mile Territorial Sea, 24 nautical mile Contiguous Zone, and 200 nautical mile Exclusive Economic Zone (EEZ). The outer limit of each of these zones is measured from the U.S. normal baseline, which coincides with the low water line depicted on NOAA charts and includes closing lines across the entrances of legal bays and rivers, consistent with international law. The U.S. baseline and associated maritime limits are reviewed and approved through the interagency U.S. Baseline Committee, which is chaired by the U.S. Department of State. The Committee serves the function of gaining interagency consensus on the proper location of the baseline using the provisions of the 1958 Convention on the Territorial Sea and the Contiguous Zone, to ensure that the seaward extent of U.S. maritime zones do not exceed the breadth that is permitted by international law. In 2002 and in response to mounting requests for digital maritime zones, NOAA launched a project to re-evaluate the U.S. baseline in partnership with other federal agencies via the U.S. Baseline Committee. The focus of the baseline evaluation was NOAA's largest scale, most recent edition nautical charts as well as supplemental source materials for verification of certain charted features. This dataset is a result of the 2002-present initiative and reflects a multi-year iterative project whereby the baseline and associated maritime limits were re-evaluated on a state or regional basis. In addition to the U.S. maritime limits, the U.S. maritime boundaries with opposite or adjacent countries as well as the US/Canada International Boundary (on land and through the Great Lakes) are also included in this dataset.

The Marine Highways dataset was created on June 15, 2016 and was updated on June 16, 2025 by the U.S. Maritime Administration (MARAD) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). MARAD's Marine Highway Program is a Maritime Administration led program to expand the use of our Nation's navigable waterways to relieve landside congestion, reduce air emissions, and generate other public benefits by increasing the efficiency of the surface transportation system (https://www.maritime.dot.gov/grants/marine-highways/marine-highway). This dataset contains the locations of all 35 maritime routes that have been designated as Marine Highways by the Secretary of U.S. DOT. Routes included in this dataset are diagrammatic and may not depict all waterways and port connectors that are considered to be part of the U.S. Marine Highway System. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529036

Maritime Objects In Varied Cases

## Overview

Maritime Objects In Varied Cases is a dataset for object detection tasks - it contains Maritime Objects S2GJ annotations for 310 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Snapshot img

Maritime Security Market Size 2024-2028

The maritime security market size is forecast to increase by USD 3.63 billion, at a CAGR of 3.2% between 2023 and 2028.

The market is experiencing significant growth due to the escalating marine threats, which include piracy, smuggling, and terrorism. The increasing number of new harbors worldwide contributes to the expanding market, as there is a growing need for robust security solutions to protect these vital infrastructure assets. However, the high installation cost of maritime security systems poses a substantial challenge for market growth. Despite this obstacle, opportunities abound for companies that can offer cost-effective, innovative solutions to address the evolving security needs of the maritime industry. The ability to integrate advanced technologies such as AI, IoT, and drones into security systems will be key differentiators for market success. Companies that can effectively navigate these challenges and capitalize on market opportunities will be well-positioned to thrive in the dynamic and complex maritime security landscape.

What will be the Size of the Maritime Security Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market continues to evolve, driven by the dynamic nature of maritime risks and the need for advanced technologies to mitigate them. Entities involved in this sector are constantly integrating intelligent gathering, risk assessment, and response capabilities into their operations. Radar systems, anti-piracy measures, image processing, real-time tracking, and system reliability are essential components, seamlessly integrated to enhance situational awareness. Infrared cameras, security protocols, information sharing, object recognition, vessel tracking, and sonar technology are crucial elements in maintaining maritime domain awareness. Sensor fusion, data analytics, and machine learning algorithms enable pattern recognition and incident management, ensuring effective response planning. Collaboration platforms and communication systems facilitate information sharing among stakeholders, enhancing the overall effectiveness of maritime security efforts. Illegal fishing detection, oil spill detection, and smuggling prevention are just a few applications of these advanced technologies. Autonomous systems, such as drones and UAVs, are increasingly being deployed for maritime surveillance, providing real-time data and enhancing situational awareness. Threat detection, cybersecurity solutions, and crisis management strategies are also essential components, ensuring the network infrastructure remains secure and resilient. The integration of predictive modeling, anomaly detection, and data visualization tools further strengthens the capabilities of maritime security entities, enabling them to anticipate potential risks and respond proactively. Lidar systems, automated alerts, and counter-terrorism strategies are additional technologies that contribute to the ongoing evolution of the market.

How is this Maritime Security Industry segmented?

The maritime security industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeDeepwater securityPerimeter securityTechnologySurveillance and trackingScreening and scanningCommunicationsOther systemsEnd-UserNaval ForcesCoast GuardsPort AuthoritiesCommercial ShippingGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanRest of World (ROW)

By Type Insights

The deepwater security segment is estimated to witness significant growth during the forecast period.The market is witnessing significant growth due to the vast expanse of oceans and seas, which cover over 70% of the earth's surface and serve as strategic locations for many countries. Defense authorities worldwide are prioritizing the enhancement of their mid-sea attack and maritime border defense capabilities by investing in advanced maritime security systems. The escalating territorial disputes, particularly in the South China Sea, have heightened safety concerns for neighboring countries such as Taiwan, Japan, the Philippines, and Singapore. In response, these countries have increased their naval presence in the region, driving the demand for maritime security solutions. Autonomous systems, predictive modeling, collaboration platforms, and personnel training are integral components of these advanced security systems. Real-time data analytics, intelligence gathering, and risk assessment are essential for effective maritime domain awareness. Radar systems, sonar technology, and satellite imagery provide valuable insights for threat detection and response planning. Integra

Maritime Target Detection

## Overview

Maritime Target Detection is a dataset for object detection tasks - it contains Warship Drone Fish_boat Buoy annotations for 5,118 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Maritime Satellite Communication Market Report is Segmented by Connectivity Type (Mobile Satellite Services [MSS], Geostationary VSAT, and Non-GEO Broadband [LEO/MEO]), Frequency Band (L-Band, S-Band, C-Band, and More), Offering (Hardware and Terminals, Connectivity Services, and More), End-User Vertical (Merchant (Merchant Cargo and Tanker, Offshore Energy & Support Vessels, Passenger, and More), and Geography.

The maritime security market, while exhibiting a seemingly stagnant CAGR of 0.00 (likely reflecting data limitations or a period of market consolidation), is a dynamic sector driven by escalating geopolitical tensions, increasing maritime trade, and the growing threat of piracy and terrorism. The market's value, estimated at approximately $15 billion in 2025 (this figure is a reasonable estimation based on the scale of companies involved and the global nature of maritime activities), is expected to witness modest growth throughout the forecast period (2025-2033). Key drivers include heightened concerns over maritime domain awareness, the need for advanced surveillance technologies (like AIS and radar systems), and the rising demand for cybersecurity solutions to protect critical maritime infrastructure. Emerging trends include the integration of AI and machine learning for enhanced threat detection and response, the adoption of unmanned maritime systems (UMS) for surveillance and patrol, and the growing importance of collaborative efforts among governments and private entities to bolster maritime security. However, restraints such as high initial investment costs for advanced technologies, regulatory complexities across different jurisdictions, and the need for skilled personnel could temper market expansion. The market is segmented by production, consumption, and import/export analyses, offering insights into regional variations and supply chain dynamics. Major players like BAE Systems, Saab, and Thales are strategically positioning themselves to capitalize on these trends through technological innovation and partnerships. Regional variations are significant. North America and Europe currently hold the largest market share due to established naval capabilities and strong maritime industries. However, regions like Asia-Pacific are experiencing substantial growth driven by increasing trade volumes and rising security concerns in key shipping lanes. This growth is likely fueled by investments in advanced technology and infrastructure to protect crucial maritime trade routes and ports. While the provided CAGR is 0.00, it's likely a temporary reflection; the market is expected to demonstrate a positive, albeit moderate, growth trajectory over the next decade, driven by continuous technological advancements and the enduring need for effective maritime security measures in an increasingly complex global landscape. Key drivers for this market are: , Increased Seaborne Threats And Ambiguous Maritime Security Policies; Increasing Adoption Of Security Technologies In Bric Countries. Potential restraints include: , High Risk Rate In Ungoverned Zones; Unstructured Security Standards And Technologies. Notable trends are: Increased Seaborne Threats And Ambiguous Maritime Security Policies.

Introduction

The global maritime surveillance market is projected to grow from USD 21.9 billion in 2023 to USD 42.7 billion by 2033, reflecting a CAGR of 6.90%. This growth is driven by increasing global trade, maritime security concerns, and the adoption of advanced surveillance technologies like radars, drones, and satellite monitoring systems. In 2023, North America led the market with a 34.1% share, generating USD 7.9 billion in revenue. As maritime security becomes a top priority for governments and industries worldwide, the demand for advanced surveillance systems is expected to continue expanding across both commercial and military sectors.