Snapshot img { margin: 10px !important; } AI Data Labeling Market Size 2025-2029

The ai data labeling market size is forecast to increase by USD 1.4 billion, at a CAGR of 21.1% between 2024 and 2029.

The escalating adoption of artificial intelligence and machine learning technologies is a primary driver for the global ai data labeling market. As organizations integrate ai into operations, the need for high-quality, accurately labeled training data for supervised learning algorithms and deep neural networks expands. This creates a growing demand for data annotation services across various data types. The emergence of automated and semi-automated labeling tools, including ai content creation tool and data labeling and annotation tools, represents a significant trend, enhancing efficiency and scalability for ai data management. The use of an ai speech to text tool further refines audio data processing, making annotation more precise for complex applications.Maintaining data quality and consistency remains a paramount challenge. Inconsistent or erroneous labels can lead to flawed model performance, biased outcomes, and operational failures, undermining AI development efforts that rely on ai training dataset resources. This issue is magnified by the subjective nature of some annotation tasks and the varying skill levels of annotators. For generative artificial intelligence (AI) applications, ensuring the integrity of the initial data is crucial. This landscape necessitates robust quality assurance protocols to support systems like autonomous ai and advanced computer vision systems, which depend on flawless ground truth data for safe and effective operation.

What will be the Size of the AI Data Labeling Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe global ai data labeling market's evolution is shaped by the need for high-quality data for ai training. This involves processes like data curation process and bias detection to ensure reliable supervised learning algorithms. The demand for scalable data annotation solutions is met through a combination of automated labeling tools and human-in-the-loop validation, which is critical for complex tasks involving multimodal data processing.Technological advancements are central to market dynamics, with a strong focus on improving ai model performance through better training data. The use of data labeling and annotation tools, including those for 3d computer vision and point-cloud data annotation, is becoming standard. Data-centric ai approaches are gaining traction, emphasizing the importance of expert-level annotations and domain-specific expertise, particularly in fields requiring specialized knowledge such as medical image annotation.Applications in sectors like autonomous vehicles drive the need for precise annotation for natural language processing and computer vision systems. This includes intricate tasks like object tracking and semantic segmentation of lidar point clouds. Consequently, ensuring data quality control and annotation consistency is crucial. Secure data labeling workflows that adhere to gdpr compliance and hipaa compliance are also essential for handling sensitive information.

How is this AI Data Labeling Industry segmented?

The ai data labeling 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. TypeTextVideoImageAudio or speechMethodManualSemi-supervisedAutomaticEnd-userIT and technologyAutomotiveHealthcareOthersGeographyNorth AmericaUSCanadaMexicoAPACChinaIndiaJapanSouth KoreaAustraliaIndonesiaEuropeGermanyUKFranceItalySpainThe NetherlandsSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaUAESouth AfricaTurkeyRest of World (ROW)

By Type Insights

The text segment is estimated to witness significant growth during the forecast period.The text segment is a foundational component of the global ai data labeling market, crucial for training natural language processing models. This process involves annotating text with attributes such as sentiment, entities, and categories, which enables AI to interpret and generate human language. The growing adoption of NLP in applications like chatbots, virtual assistants, and large language models is a key driver. The complexity of text data labeling requires human expertise to capture linguistic nuances, necessitating robust quality control to ensure data accuracy. The market for services catering to the South America region is expected to constitute 7.56% of the total opportunity.The demand for high-quality text annotation is fueled by the need for ai models to understand user intent in customer service automation and identify critical

Human Feedback Labeling Tools Market Outlook

According to our latest research, the global Human Feedback Labeling Tools market size reached USD 1.42 billion in 2024, reflecting the rapidly increasing adoption of AI and machine learning technologies requiring high-quality labeled datasets. The market is expected to grow at a robust CAGR of 21.8% from 2025 to 2033, reaching a forecasted value of USD 10.41 billion by 2033. This remarkable growth is primarily driven by the escalating demand for accurate data annotation across various industries, including healthcare, automotive, and BFSI, as well as the increasing sophistication of AI models that rely on human-in-the-loop feedback for optimization and bias mitigation.

One of the most significant growth factors for the Human Feedback Labeling Tools market is the surging reliance on artificial intelligence and machine learning models across diverse sectors. As organizations strive to develop and deploy more sophisticated AI systems, the need for high-quality, accurately labeled data has become paramount. Human feedback labeling tools bridge the gap between raw data and actionable AI models by enabling precise annotation, validation, and correction of datasets. This is particularly crucial for supervised learning applications, where the quality of labeled data directly influences model performance. Additionally, increasing awareness about the risks of algorithmic bias and the need for ethical AI development has further amplified the demand for human-in-the-loop solutions that can provide nuanced, context-aware labeling, ensuring fairness and transparency in AI outcomes.

Another key driver propelling the growth of the Human Feedback Labeling Tools market is the rapid digital transformation initiatives undertaken by enterprises globally. As businesses in sectors such as healthcare, retail, automotive, and finance digitize their operations, they generate vast amounts of unstructured data that require labeling for AI-driven analytics and automation. The proliferation of new data types, including images, videos, speech, and text, has necessitated the development of advanced labeling tools capable of handling multimodal data. Moreover, the rise of edge computing and IoT has created new use cases for real-time data annotation, further expanding the market’s scope. The integration of active learning, reinforcement learning, and continuous feedback loops into labeling workflows is also enhancing the value proposition of these tools, enabling organizations to iteratively improve model accuracy and adapt to evolving data patterns.

The evolution of regulatory frameworks and industry standards related to data privacy and AI ethics is also shaping the Human Feedback Labeling Tools market. Governments and regulatory bodies worldwide are enacting stricter guidelines around data usage, consent, and transparency in AI systems. This regulatory push is compelling organizations to adopt labeling tools that not only ensure data quality but also maintain robust audit trails, compliance reporting, and secure handling of sensitive information. Furthermore, the increasing emphasis on explainable AI and model interpretability is driving demand for labeling solutions that facilitate granular feedback and traceability, empowering stakeholders to understand and trust AI-driven decisions. As a result, vendors are investing in the development of user-friendly, customizable, and scalable labeling platforms that cater to the diverse compliance needs of different industries.

Regionally, North America continues to dominate the Human Feedback Labeling Tools market, accounting for over 38% of global revenue in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology companies, robust R&D investments, and early adoption of AI-driven solutions have cemented North America’s leadership position. Europe is experiencing significant growth due to stringent data privacy regulations such as GDPR and a strong focus on ethical AI. Meanwhile, Asia Pacific is emerging as the fastest-growing market, with a CAGR of 25.2% during the forecast period, fueled by rapid digitization, expanding AI research, and increasing investments in smart infrastructure across countries like China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady adoption, driven by government initiatives and the growing need for automation in public and private sectors.

Component Analysis</h2

Snapshot img { margin: 10px !important; } Generative AI In Data Labeling Solution And Services Market Size 2025-2029

The generative ai in data labeling solution and services market size is forecast to increase by USD 31.7 billion, at a CAGR of 24.2% between 2024 and 2029.

The global generative AI in data labeling solution and services market is shaped by the escalating demand for high-quality, large-scale datasets. Traditional manual data labeling methods create a significant bottleneck in the ai development lifecycle, which is addressed by the proliferation of synthetic data generation for robust model training. This strategic shift allows organizations to create limitless volumes of perfectly labeled data on demand, covering a comprehensive spectrum of scenarios. This capability is particularly transformative for generative ai in automotive applications and in the development of data labeling and annotation tools, enabling more resilient and accurate systems.However, a paramount challenge confronting the market is ensuring accuracy, quality control, and mitigation of inherent model bias. Generative models can produce plausible but incorrect labels, a phenomenon known as hallucination, which can introduce systemic errors into training datasets. This makes ai in data quality a critical concern, necessitating robust human-in-the-loop verification processes to maintain the integrity of generative ai in healthcare data. The market's long-term viability depends on developing sophisticated frameworks for bias detection and creating reliable generative artificial intelligence (AI) that can be trusted for foundational tasks.

What will be the Size of the Generative AI In Data Labeling Solution And Services Market during the forecast period?

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The global generative AI in data labeling solution and services market is witnessing a transformation driven by advancements in generative adversarial networks and diffusion models. These techniques are central to synthetic data generation, augmenting AI model training data and redefining the machine learning pipeline. This evolution supports a move toward more sophisticated data-centric AI workflows, which integrate automated data labeling with human-in-the-loop annotation for enhanced accuracy. The scope of application is broadening from simple text-based data annotation to complex image-based data annotation and audio-based data annotation, creating a demand for robust multimodal data labeling capabilities. This shift across the AI development lifecycle is significant, with projections indicating a 35% rise in the use of AI-assisted labeling for specialized computer vision systems.Building upon this foundation, the focus intensifies on annotation quality control and AI-powered quality assurance within modern data annotation platforms. Methods like zero-shot learning and few-shot learning are becoming more viable, reducing dependency on massive datasets. The process of foundation model fine-tuning is increasingly guided by reinforcement learning from human feedback, ensuring outputs align with specific operational needs. Key considerations such as model bias mitigation and data privacy compliance are being addressed through AI-assisted labeling and semi-supervised learning. This impacts diverse sectors, from medical imaging analysis and predictive maintenance models to securing network traffic patterns against cybersecurity threat signatures and improving autonomous vehicle sensors for robotics training simulation and smart city solutions.

How is this Generative AI In Data Labeling Solution And Services Market segmented?

The generative ai in data labeling solution and services market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029,for the following segments. End-userIT dataHealthcareRetailFinancial servicesOthersTypeSemi-supervisedAutomaticManualProductImage or video basedText basedAudio basedGeographyNorth AmericaUSCanadaMexicoAPACChinaIndiaSouth KoreaJapanAustraliaIndonesiaEuropeGermanyUKFranceItalyThe NetherlandsSpainSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaSouth AfricaUAETurkeyRest of World (ROW)

By End-user Insights

The it data segment is estimated to witness significant growth during the forecast period.

In the IT data segment, generative AI is transforming the creation of training data for software development, cybersecurity, and network management. It addresses the need for realistic, non-sensitive data at scale by producing synthetic code, structured log files, and diverse threat signatures. This is crucial for training AI-powered developer tools and intrusion detection systems. With South America representing an 8.1% market opportunity, the demand for localized and specia

Data Labeling Operations Platform Market Outlook

According to our latest research, the global data labeling operations platform market size stood at USD 2.1 billion in 2024, reflecting robust demand across industries leveraging artificial intelligence and machine learning. The market is expected to grow at an impressive CAGR of 22.7% during the forecast period, reaching approximately USD 15.2 billion by 2033. This remarkable expansion is primarily driven by the urgent need for high-quality labeled datasets, which are foundational to the development and deployment of AI-driven solutions across diverse sectors such as healthcare, automotive, retail, and BFSI. As per our comprehensive industry analysis, the surge in automation, proliferation of big data, and increasing sophistication of AI algorithms are catalyzing the adoption of advanced data labeling operations platforms worldwide.

One of the primary growth factors for the data labeling operations platform market is the explosive increase in data generation, spurred by the widespread adoption of IoT devices, connected infrastructure, and digital transformation initiatives. Organizations are grappling with vast volumes of raw data that require accurate annotation to train machine learning models effectively. The demand for automated and semi-automated data labeling solutions is escalating as enterprises seek to accelerate AI project timelines while maintaining data quality and compliance. Furthermore, the rise of edge computing and real-time analytics is intensifying the need for rapid, scalable data labeling operations that can support continuous learning and adaptive systems. These trends are fostering a fertile environment for the growth of data labeling platforms that offer robust workflow management, quality assurance, and integration capabilities.

Another significant driver is the increasing complexity and variety of data types that organizations must process. With the expansion of AI applications into areas such as autonomous vehicles, medical diagnostics, and natural language processing, the need for precise labeling of images, videos, audio, and text data has become paramount. Data labeling operations platforms are evolving to support multi-modal annotation, advanced collaboration tools, and seamless integration with data pipelines and machine learning frameworks. The competitive landscape is further shaped by the entry of specialized vendors offering domain-specific labeling expertise, as well as the adoption of crowdsourcing and hybrid labeling models. These advancements are enabling organizations to handle large-scale, complex annotation tasks efficiently, thus accelerating AI innovation and deployment.

The growing emphasis on data privacy, security, and regulatory compliance is also influencing the evolution of the data labeling operations platform market. As organizations handle sensitive data, particularly in sectors like healthcare and finance, there is a heightened focus on ensuring that labeling processes adhere to stringent data protection standards. This has led to the development of platforms with built-in privacy controls, audit trails, and secure deployment options, including on-premises and private cloud solutions. Additionally, the integration of AI-assisted labeling and quality control features is helping organizations mitigate risks associated with human error and bias, further enhancing the reliability and trustworthiness of labeled datasets. These factors collectively contribute to the sustained growth and maturation of the data labeling operations platform ecosystem.

From a regional perspective, North America continues to dominate the global data labeling operations platform market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high concentration of technology giants, early AI adopters, and a mature digital infrastructure in North America have fueled significant investments in data labeling solutions. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding AI research, and increasing government initiatives to foster innovation. Europe maintains a strong position due to its focus on data privacy and regulatory compliance, particularly with the implementation of the General Data Protection Regulation (GDPR). Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the value of robust data labeling operations in supporting their AI ambitions

Annotation Tools for Robotics Perception Market Outlook

As per our latest research, the global Annotation Tools for Robotics Perception market size reached USD 1.47 billion in 2024, with a robust growth trajectory driven by the rapid adoption of robotics in various sectors. The market is expected to expand at a CAGR of 18.2% during the forecast period, reaching USD 6.13 billion by 2033. This significant growth is attributed primarily to the increasing demand for sophisticated perception systems in robotics, which rely heavily on high-quality annotated data to enable advanced machine learning and artificial intelligence functionalities.

A key growth factor for the Annotation Tools for Robotics Perception market is the surging deployment of autonomous systems across industries such as automotive, manufacturing, and healthcare. The proliferation of autonomous vehicles and industrial robots has created an unprecedented need for comprehensive datasets that accurately represent real-world environments. These datasets require meticulous annotation, including labeling of images, videos, and sensor data, to train perception algorithms for tasks such as object detection, tracking, and scene understanding. The complexity and diversity of environments in which these robots operate necessitate advanced annotation tools capable of handling multi-modal data, thus fueling the demand for innovative solutions in this market.

Another significant driver is the continuous evolution of machine learning and deep learning algorithms, which require vast quantities of annotated data to achieve high accuracy and reliability. As robotics applications become increasingly sophisticated, the need for precise and context-rich annotations grows. This has led to the emergence of specialized annotation tools that support a variety of data types, including 3D point clouds and multi-sensor fusion data. Moreover, the integration of artificial intelligence within annotation tools themselves is enhancing the efficiency and scalability of the annotation process, enabling organizations to manage large-scale projects with reduced manual intervention and improved quality control.

The growing emphasis on safety, compliance, and operational efficiency in sectors such as healthcare and aerospace & defense further accelerates the adoption of annotation tools for robotics perception. Regulatory requirements and industry standards mandate rigorous validation of robotic perception systems, which can only be achieved through extensive and accurate data annotation. Additionally, the rise of collaborative robotics (cobots) in manufacturing and agriculture is driving the need for annotation tools that can handle diverse and dynamic environments. These factors, combined with the increasing accessibility of cloud-based annotation platforms, are expanding the reach of these tools to organizations of all sizes and across geographies.

In this context, Automated Ultrastructure Annotation Software is gaining traction as a pivotal tool in enhancing the efficiency and precision of data labeling processes. This software leverages advanced algorithms and machine learning techniques to automate the annotation of complex ultrastructural data, which is particularly beneficial in fields requiring high-resolution imaging and detailed analysis, such as biomedical research and materials science. By automating the annotation process, this software not only reduces the time and labor involved but also minimizes human error, leading to more consistent and reliable datasets. As the demand for high-quality annotated data continues to rise across various industries, the integration of such automated solutions is becoming increasingly essential for organizations aiming to maintain competitive advantage and operational efficiency.

From a regional perspective, North America currently holds the largest share of the Annotation Tools for Robotics Perception market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the regionÂ’s strong presence of robotics technology developers, advanced research institutions, and early adoption across automotive and manufacturing sectors. Asia Pacific follows closely, fueled by rapid industrialization, government initiatives supporting automation, and the presence of major automotiv

As single-cell chromatin accessibility profiling methods advance, scATAC-seq has become ever more important in the study of candidate regulatory genomic regions and their roles underlying developmental, evolutionary, and disease processes. At the same time, cell type annotation is critical in understanding the cellular composition of complex tissues and identifying potential novel cell types. However, most existing methods that can perform automated cell type annotation are designed to transfer labels from an annotated scRNA-seq data set to another scRNA-seq data set, and it is not clear whether these methods are adaptable to annotate scATAC-seq data. Several methods have been recently proposed for label transfer from scRNA-seq data to scATAC-seq data, but there is a lack of benchmarking study on the performance of these methods. Here, we evaluated the performance of five scATAC-seq annotation methods on both their classification accuracy and scalability using publicly available single-cell datasets from mouse and human tissues including brain, lung, kidney, PBMC, and BMMC. Using the BMMC data as basis, we further investigated the performance of these methods across different data sizes, mislabeling rates, sequencing depths and the number of cell types unique to scATAC-seq. Bridge integration, which is the only method that requires additional multimodal data and does not need gene activity calculation, was overall the best method and robust to changes in data size, mislabeling rate and sequencing depth. Conos was the most time and memory efficient method but performed the worst in terms of prediction accuracy. scJoint tended to assign cells to similar cell types and performed relatively poorly for complex datasets with deep annotations but performed better for datasets only with major label annotations. The performance of scGCN and Seurat v3 was moderate, but scGCN was the most time-consuming method and had the most similar performance to random classifiers for cell types unique to scATAC-seq.

Mobile Robot Data Annotation Tools Market Outlook

According to our latest research, the global mobile robot data annotation tools market size reached USD 1.46 billion in 2024, demonstrating robust expansion with a compound annual growth rate (CAGR) of 22.8% from 2025 to 2033. The market is forecasted to attain USD 11.36 billion by 2033, driven by the surging adoption of artificial intelligence (AI) and machine learning (ML) in robotics, the escalating demand for autonomous mobile robots across industries, and the increasing sophistication of annotation tools tailored for complex, multimodal datasets.

The primary growth driver for the mobile robot data annotation tools market is the exponential rise in the deployment of autonomous mobile robots (AMRs) across various sectors, including manufacturing, logistics, healthcare, and agriculture. As organizations strive to automate repetitive and hazardous tasks, the need for precise and high-quality annotated datasets has become paramount. Mobile robots rely on annotated data for training algorithms that enable them to perceive their environment, make real-time decisions, and interact safely with humans and objects. The proliferation of sensors, cameras, and advanced robotics hardware has further increased the volume and complexity of raw data, necessitating sophisticated annotation tools capable of handling image, video, sensor, and text data streams efficiently. This trend is driving vendors to innovate and integrate AI-powered features such as auto-labeling, quality assurance, and workflow automation, thereby boosting the overall market growth.

Another significant growth factor is the integration of cloud-based data annotation platforms, which offer scalability, collaboration, and accessibility advantages over traditional on-premises solutions. Cloud deployment enables distributed teams to annotate large datasets in real time, leverage shared resources, and accelerate project timelines. This is particularly crucial for global enterprises and research institutions working on cutting-edge robotics applications that require rapid iteration and continuous learning. Moreover, the rise of edge computing and the Internet of Things (IoT) has created new opportunities for real-time data annotation and validation at the source, further enhancing the value proposition of advanced annotation tools. As organizations increasingly recognize the strategic importance of high-quality annotated data for achieving competitive differentiation, investment in robust annotation platforms is expected to surge.

The mobile robot data annotation tools market is also benefiting from the growing emphasis on safety, compliance, and ethical AI. Regulatory bodies and industry standards are mandating rigorous validation and documentation of AI models used in safety-critical applications such as autonomous vehicles, medical robots, and defense systems. This has led to a heightened demand for annotation tools that offer audit trails, version control, and compliance features, ensuring transparency and traceability throughout the model development lifecycle. Furthermore, the emergence of synthetic data generation, active learning, and human-in-the-loop annotation workflows is enabling organizations to overcome data scarcity challenges and improve annotation efficiency. These advancements are expected to propel the market forward, as stakeholders seek to balance speed, accuracy, and regulatory requirements in their AI-driven robotics initiatives.

From a regional perspective, Asia Pacific is emerging as a dominant force in the mobile robot data annotation tools market, fueled by rapid industrialization, significant investments in robotics research, and the presence of leading technology hubs in countries such as China, Japan, and South Korea. North America continues to maintain a strong foothold, driven by early adoption of AI and robotics technologies, a robust ecosystem of annotation tool providers, and supportive government initiatives. Europe is also witnessing steady growth, particularly in the manufacturing and automotive sectors, while Latin America and the Middle East & Africa are gradually catching up as awareness and adoption rates increase. The interplay of regional dynamics, regulatory environments, and industry verticals will continue to shape the competitive landscape and growth trajectory of the global market over the forecast period.

Video Dataset Labeling for Security Market Outlook

According to our latest research, the global Video Dataset Labeling for Security market size reached USD 1.84 billion in 2024, with a robust year-over-year growth rate. The market is expected to expand at a CAGR of 18.7% from 2025 to 2033, ultimately achieving a projected value of USD 9.59 billion by 2033. This impressive growth is driven by the increasing integration of artificial intelligence and machine learning technologies in security systems, as well as the rising demand for accurate, real-time video analytics across diverse sectors.

One of the primary growth factors for the Video Dataset Labeling for Security market is the escalating need for advanced surveillance solutions in both public and private sectors. As urban environments become more complex and security threats more sophisticated, organizations are increasingly investing in intelligent video analytics that rely on meticulously labeled datasets. These annotated datasets enable AI models to accurately detect, classify, and respond to potential threats in real-time, significantly enhancing the effectiveness of surveillance systems. The proliferation of smart cities and the adoption of IoT-enabled devices have further amplified the volume of video data generated, necessitating efficient and scalable labeling solutions to ensure actionable insights and rapid incident response.

Another significant driver is the evolution of regulatory frameworks mandating higher standards of security and data privacy. Governments and industry bodies across the globe are implementing stringent guidelines for surveillance, especially in critical infrastructure sectors such as transportation, BFSI, and energy. These regulations not only require comprehensive monitoring but also demand that video analytics systems minimize false positives and ensure accurate identification of individuals and behaviors. Video dataset labeling plays a pivotal role in training AI models to comply with these regulations, reducing the risk of compliance breaches and supporting forensic investigations. The need for transparency and accountability in automated security solutions is further pushing organizations to invest in high-quality labeling services and software.

Technological advancements in deep learning and computer vision have also catalyzed market growth. The development of sophisticated annotation tools, automation platforms, and cloud-based labeling services has significantly reduced the time and cost associated with preparing training datasets. Innovations such as active learning, semi-supervised labeling, and synthetic data generation are making it possible to annotate vast volumes of video footage with minimal manual intervention, thereby accelerating AI model deployment. Furthermore, the integration of multimodal data—combining video with audio, thermal, and biometric inputs—has expanded the scope of security applications, driving demand for more comprehensive and nuanced labeling solutions.

From a regional perspective, North America currently leads the global Video Dataset Labeling for Security market, accounting for approximately 37% of the total market share in 2024. This dominance is attributed to the region's early adoption of AI-driven security solutions, substantial investments in smart infrastructure, and the presence of leading technology providers. Europe and Asia Pacific are also witnessing rapid growth, fueled by government initiatives to modernize public safety systems and the increasing incidence of security threats in urban and industrial environments. The Asia Pacific region, in particular, is expected to register the highest CAGR over the forecast period, driven by large-scale deployments in countries such as China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing urbanization and heightened security concerns.

Component Analysis

The Video Dataset Labeling for Secu

Data Annotation for Autonomous Driving Market Outlook

According to our latest research, the global Data Annotation for Autonomous Driving market size has reached USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 23.1% projected through the forecast period. By 2033, the market is expected to attain a value of USD 10.82 billion, reflecting the surging demand for high-quality labeled data to fuel advanced driver-assistance systems (ADAS) and fully autonomous vehicles. The primary growth factor propelling this market is the rapid evolution of machine learning and computer vision technologies, which require vast, accurately annotated datasets to ensure the reliability and safety of autonomous driving systems.

The exponential growth of the data annotation for autonomous driving market is largely attributed to the intensifying race among automakers and technology companies to deploy Level 3 and above autonomous vehicles. As these vehicles rely heavily on AI-driven perception systems, the need for meticulously annotated datasets for training, validation, and testing has never been more critical. The proliferation of sensors such as LiDAR, radar, and high-resolution cameras in modern vehicles generates massive volumes of multimodal data, all of which must be accurately labeled to enable object detection, lane keeping, semantic understanding, and navigation. The increasing complexity of driving scenarios, including urban environments and adverse weather conditions, further amplifies the necessity for comprehensive data annotation services.

Another significant growth driver is the expanding adoption of semi-automated and fully autonomous commercial fleets, particularly in logistics, ride-hailing, and public transportation. These deployments demand continuous data annotation for real-world scenario adaptation, edge case identification, and system refinement. The rise of regulatory frameworks mandating safety validation and explainability in AI models has also contributed to the surge in demand for precise annotation, as regulatory compliance hinges on transparent and traceable data preparation processes. Furthermore, the integration of AI-powered annotation tools, which leverage machine learning to accelerate and enhance the annotation process, is streamlining workflows and reducing time-to-market for autonomous vehicle solutions.

Strategic investments and collaborations among automotive OEMs, Tier 1 suppliers, and specialized technology providers are accelerating the development of scalable, high-quality annotation pipelines. As global automakers expand their autonomous driving programs, partnerships with data annotation service vendors are becoming increasingly prevalent, driving innovation in annotation methodologies and quality assurance protocols. The entry of new players and the expansion of established firms into emerging markets, particularly in the Asia Pacific region, are fostering a competitive landscape that emphasizes cost efficiency, scalability, and domain expertise. This dynamic ecosystem is expected to further catalyze the growth of the data annotation for autonomous driving market over the coming decade.

From a regional perspective, Asia Pacific leads the global market, accounting for over 36% of total revenue in 2024, followed closely by North America and Europe. The regionÂ’s dominance is underpinned by the rapid digitization of the automotive sector in countries such as China, Japan, and South Korea, where government incentives and aggressive investment in smart mobility initiatives are stimulating demand for autonomous driving technologies. North America, with its concentration of leading technology companies and research institutions, continues to be a hub for AI innovation and autonomous vehicle testing. EuropeÂ’s robust regulatory framework and focus on vehicle safety standards are also contributing to a steady increase in data annotation activities, particularly among premium automakers and mobility service providers.

Annotation Tools for Robotics Perception are becoming increasingly vital in the realm of autonomous driving. These tools facilitate the precise labeling of complex datasets, which is crucial for training the perception systems of autonomous vehicles. By employing advanced annotation techniques, these tools enable the identification and clas

The LapEx dataset contains 30 videos of sleeve gastrectomy surgeries performed by two surgeons. The videos were captured at 25 fps. Three annotation tasks were performed on the videos of the "dissection of fundus" surgical step : 1. Surgical activites were annotated with the quadruplet < actor , instrument , verb , target > 2. The quality of exposure was annotated with a value in [good, satisfying, unsatisfying] 3. Images were completely segmented with a labeling of visible tools and organs

Perception Dataset Management Platforms Market Outlook

According to our latest research, the global Perception Dataset Management Platforms market size reached USD 1.27 billion in 2024, and is expected to grow at a robust CAGR of 23.8% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of approximately USD 10.98 billion. This remarkable growth is driven by the rapid adoption of artificial intelligence (AI) and machine learning (ML) technologies across industries, which necessitate high-quality, well-annotated perception datasets for training and validating advanced models.

The primary growth factor fueling the Perception Dataset Management Platforms market is the surging demand for AI-powered solutions in sectors such as autonomous vehicles, robotics, and surveillance. As organizations increasingly rely on AI systems that require complex perception capabilities—such as object detection, scene understanding, and environmental awareness—the need for sophisticated dataset management platforms has intensified. These platforms streamline the collection, curation, annotation, and governance of large-scale perception datasets, ensuring high data quality and compliance with regulatory standards. The proliferation of edge devices and IoT sensors further amplifies the volume and diversity of data generated, necessitating scalable and efficient management solutions.

Another significant driver is the escalating complexity of AI applications in healthcare, retail, and security sectors. In healthcare, for example, perception datasets are crucial for developing diagnostic imaging solutions, patient monitoring systems, and robotic surgery tools. The retail industry leverages these platforms for in-store analytics, customer behavior tracking, and inventory management, while security and defense sectors utilize them for surveillance, threat detection, and situational awareness. The ability of perception dataset management platforms to handle multi-modal data—including images, videos, LiDAR, and radar—positions them as indispensable tools for organizations aiming to accelerate AI innovation while maintaining data integrity and privacy.

Furthermore, the market is benefiting from increased investments in research and academia, where the demand for high-quality, annotated datasets is paramount for advancing AI research. Collaborative initiatives between universities, research institutions, and industry players are fostering the development of standardized dataset management practices and open-source platforms, thereby accelerating innovation and knowledge sharing. Additionally, the growing emphasis on ethical AI and data transparency is prompting organizations to adopt platforms that offer robust data lineage, audit trails, and compliance features, further driving market growth.

Regionally, North America remains the dominant market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology companies, advanced research institutions, and a strong focus on AI-driven innovation underpin North America’s leadership. Europe is witnessing substantial growth due to stringent data privacy regulations and increased investments in AI research, while Asia Pacific is emerging as a high-growth region, propelled by government initiatives, expanding digital infrastructure, and the rapid adoption of AI technologies across industries. Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness and investments in digital transformation.

Component Analysis

The Perception Dataset Management Platforms market is primarily segmented by component into software and services. The software segment holds the lion’s share of the market, driven by the proliferation of advanced AI and ML tools that require sophisticated data management capabilities. These software solutions offer functionalities such as automated data labeling, annotation, quality control, and versioning—enabling organizations to efficiently manage large volumes of perception data. The integration of AI-powered analytics and visualization tools within these platforms further enhances their value proposition, allowing users to gain actionable insights from complex multi-modal datasets. As AI applications become more mainstream, the demand for robust, scalable, and user-friendly software platforms is expected to surg

Pituitary Adenoma Multimodal MRI Dataset

With Ground Truth and Benchmark Segmentations

This dataset provides a comprehensive, BIDS-organized collection of multiparametric MRI scans and expert-validated segmentations from 50 patients with pituitary adenomas. It is designed to support development and evaluation of segmentation models targeting challenging, pathologically altered sellar anatomy.

The dataset includes: - Up to 10 MRI sequences per subject - Ground truth annotations for 5 anatomical structures - Outputs from 5 segmentation models - Full benchmarking metrics

For the accompanying data descriptior paper, please refer to

Černý M, Májovský M, Valošek J, et al. Open-access multiparametric MRI dataset of pituitary adenoma and benchmark analysis of five segmentation models. Scientific Data. [Under review].

Context

Accurate segmentation of pituitary adenomas and surrounding structures is critical for: - Surgical planning and navigation
- Radiosurgery targeting
- Volumetric progression monitoring

Until now, no large, openly available dataset with detailed annotations and benchmarked segmentation outputs existed in this clinical domain.

Imaging Data

MRI scans were acquired using a 3T GE 750w system. Data is stored in .nii.gz format and organized in compliance with the BIDS standard.

Ground Truth Segmentations

Each subject includes a multi-class segmentation mask with the following labels:

• 1 – Tumor
• 2 – Normal pituitary gland
• 3 – Left internal carotid artery (ICA)
• 4 – Right ICA
• 5 – Optic pathway

Manual multi-class segmentation was performed on coronal CE-T1w scans using Brainlab Elements and refined in multiple review rounds, including an external reviewer. Each segmentation was saved as a single-label .nii.gz file with integer values from 0–5. Additional annotations—seed points for semi-automated segmentation and keyframe slice indices—were also created for each subject and are included in the dataset. For further details, please refer to the accompanying paper.

https://raw.githubusercontent.com/DrMartinCerny/pituitary_adenoma_multimodal_MRI_dataset/main/img/figure_2.png" alt="A schematic overview of the process of dataset creation and model benchmarking">

A schematic overview of the process of dataset creation and model benchmarking. The left and bottom part of the image (green arrows) depicts the ground truth creation. First, segmentation masks were drawn manually by the main author. They were then reviewed and refined if necessary by the second author and by a reviewer from an external institution, establishing ground truth segmentation masks. Seed points for semi-automated segmentation were placed manually. The upper right part of the image (blue arrows) depicts the benchmark analysis. Predicted segmentations were obtained from one semi-automated method and three models and compared with the ground truth. Segmentation accuracy was assessed quantitatively using the Dice similarity coefficient, intersection over union, and Hausdorff distance.

Benchmark Study

We include predictions from five models:

• Černý et al. 2025 – nnU-Net, full volume
• Da Mutten et al. 2025 – nnU-Net, full volume
• Da Mutten et al. 2024 – CNN ensemble
• Černý et al. 2023 – Keyframe-only CNN
• Egger et al. 2012 – GrowCut (semi-automated)

All predictions are aligned to the CE-T1w space and located under:

/derivatives/segmentations/

Benchmark Results

Metrics used:
- Dice Similarity Coefficient (DSC)
- Intersection over Union (IoU)
- Hausdorff Distance (HD)

https://raw.githubusercontent.com/DrMartinCerny/pituitary_adenoma_multimodal_MRI_dataset/main/img/figure_4.png" alt="4 A visual comparison of Dice similarity coefficient of assessed segmentation models for tumor label">

A visual comparison of Dice similarity coefficient of assessed segmentation models for tumor label class a) on key frames and b) full volume

See the full article for a detailed analysis including other label classes.

Tools and Code

This dataset includes scripts for: - DICOM → NIfTI BIDS conversion
- Segmentation benchmarking and label harmonization
- Manual defacing of 3D scans

The full codebase is available at:
github.com/DrMartinCerny/pituitary_adenoma_multimodal_MRI_dataset

A copy of all code files is also included with the dataset.

License

This dataset is licensed under a
Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0).

Use and adapt for non-commercial purposes with proper attribution.

creativecommons.org/licenses/by-nc/4.0

Citation

If you use this dataset in your work, please cite:

Černý M, Májovský M, Valošek J, et al. Open-access multiparametric MRI dataset of pituitary adenoma and benchmark analysis of five segmentation models. Scientific Data. [Under review].

Contact

For questions or collaboration, contact:
Martin Černý – cerny.martin@uvn.cz

Data Versioning for ADAS Datasets Market Outlook

According to our latest research, the global Data Versioning for ADAS Datasets market size reached USD 1.14 billion in 2024, reflecting the rapidly growing demand for robust data management solutions within automotive development ecosystems. The market is expected to expand at a CAGR of 18.5% from 2025 to 2033, with the projected market size reaching USD 6.17 billion by 2033. This impressive growth is primarily fueled by the increasing sophistication of Advanced Driver Assistance Systems (ADAS) and the surging adoption of autonomous vehicle technologies, which require highly accurate, traceable, and up-to-date datasets to ensure safety, compliance, and innovation.

One of the primary growth factors propelling the Data Versioning for ADAS Datasets market is the escalating complexity of ADAS and autonomous driving algorithms. As vehicles become more intelligent and capable of making critical decisions in real time, the need for high-quality, version-controlled datasets becomes paramount. The data generated from a multitude of sensors—such as cameras, LiDAR, radar, and ultrasonic devices—must be meticulously managed, annotated, and tracked across various developmental stages. Data versioning platforms enable automotive engineers to efficiently handle dataset iterations, ensuring that modifications, updates, and enhancements are systematically documented. This not only accelerates the pace of innovation but also supports traceability and regulatory compliance, which are vital in the automotive industry where safety standards are uncompromising.

Another significant driver is the increasing regulatory scrutiny and the necessity for data transparency in the automotive sector. Regulatory bodies worldwide are mandating stringent safety standards for ADAS and autonomous vehicles, necessitating rigorous testing and validation processes. Data versioning solutions facilitate the ability to reproduce test scenarios, validate algorithm performance, and provide auditable records for compliance purposes. The traceability offered by these systems is invaluable for automotive OEMs and suppliers, as it allows for the identification of data lineage and the management of data provenance, which are critical when investigating anomalies or addressing recalls. As regulatory frameworks continue to evolve, the reliance on sophisticated data versioning tools is expected to intensify, further boosting market growth.

Technological advancements in cloud computing and artificial intelligence are also playing a pivotal role in shaping the Data Versioning for ADAS Datasets market. The integration of AI-driven data management tools with scalable cloud infrastructure enables organizations to handle vast volumes of multimodal data efficiently. Cloud-based solutions offer flexibility, scalability, and remote accessibility, making it easier for global teams to collaborate on dataset curation, annotation, and version control. Furthermore, the adoption of machine learning techniques for automated data labeling and quality assurance is streamlining the data preparation process, reducing manual labor, and minimizing errors. These technological trends are creating new avenues for market expansion, attracting investments from both established players and innovative startups.

Regionally, North America and Europe are leading the adoption of data versioning solutions for ADAS datasets, driven by the presence of major automotive OEMs, advanced research institutes, and supportive regulatory environments. Asia Pacific is emerging as a lucrative market, fueled by the rapid growth of the automotive sector, increasing investments in smart mobility, and the proliferation of connected vehicles. The Middle East & Africa and Latin America are also witnessing gradual adoption, supported by government initiatives and the entry of global automotive players. The global landscape is characterized by a dynamic interplay of technological innovation, regulatory compliance, and competitive strategies, positioning the Data Versioning for ADAS Datasets market for robust growth over the forecast period.

AI Dataset Version Control for ADAS Safety Market Outlook

According to our latest research, the global AI Dataset Version Control for ADAS Safety market size stood at USD 1.14 billion in 2024, reflecting the rapid integration of artificial intelligence and advanced driver-assistance systems (ADAS) across the automotive industry. The market is expected to expand at a robust CAGR of 21.7% from 2025 to 2033, reaching approximately USD 8.34 billion by 2033. This significant growth is primarily driven by the escalating adoption of AI-powered safety features, the proliferation of autonomous and semi-autonomous vehicles, and the increasing regulatory emphasis on vehicle and passenger safety worldwide.

The surge in demand for AI Dataset Version Control for ADAS Safety is fundamentally influenced by the complexities involved in developing, testing, and deploying AI models for automotive safety systems. As ADAS functionalities become more sophisticated, managing vast and evolving datasets becomes critical for ensuring the reliability and accuracy of AI-driven decisions. The need for precise version control enables automotive OEMs, Tier 1 suppliers, and technology partners to track, validate, and audit changes in training datasets, thereby reducing the risk of model drift and ensuring compliance with stringent safety standards. This factor has been instrumental in accelerating the adoption of dedicated dataset version control solutions across the automotive value chain.

Another key growth driver is the increasing collaboration between automotive manufacturers and technology providers to develop robust data infrastructures. The proliferation of sensors, cameras, and LiDAR systems in modern vehicles generates massive volumes of data, necessitating advanced tools to manage dataset iterations and maintain data integrity throughout the ADAS development lifecycle. AI dataset version control platforms facilitate seamless collaboration across geographically distributed teams, streamline the integration of new data sources, and support continuous learning for AI models. This collaborative ecosystem not only enhances the pace of innovation but also ensures that new safety features are rigorously tested and validated before deployment.

Moreover, regulatory pressures and evolving safety standards are compelling automakers to invest heavily in data governance and traceability. Governments and safety organizations worldwide are mandating stricter compliance protocols for AI-powered vehicle systems, emphasizing the importance of transparency and accountability in AI model training and validation. The implementation of dataset version control solutions enables organizations to maintain detailed audit trails, support regulatory reporting, and demonstrate adherence to safety benchmarks. As a result, the market is witnessing increased traction among both established automotive giants and emerging players focused on next-generation mobility solutions.

Regionally, North America and Europe are leading the adoption of AI Dataset Version Control for ADAS Safety solutions, driven by the presence of major automotive OEMs, advanced research institutes, and a robust regulatory framework. However, the Asia Pacific region is rapidly emerging as a high-growth market, fueled by the expansion of the automotive sector, rising investments in smart mobility, and increasing government initiatives to enhance road safety. Latin America and the Middle East & Africa are also showing promising potential, albeit at a relatively nascent stage, as local manufacturers and technology providers begin to embrace AI-driven safety innovations.

Component Analysis

The Component segment of the AI Dataset Version Control for ADAS Safety market is bifurcated into Software and Services. Software solutions form the backbone of this market, providing robust platforms for dataset management, versioning, and traceability. These platforms are designed to handle the unique requirements of automotive data, such as high-volume sensor inputs, real-time data labeling, and multi-modal data integration. Leading software providers are continuously enhancing their offerings with features like automated data lineage tracking, AI-driven anomaly detection, and seamless integration with popular machine learning frameworks. This ongoing innovation is crucial for supporting the rapid iteration cycles required in ADAS development and deployment.<br

Veterinary Synthetic Training Data Sets Market Outlook

According to our latest research, the Veterinary Synthetic Training Data Sets market size reached USD 328 million in 2024, demonstrating robust momentum driven by technological advancements and the rising adoption of artificial intelligence in veterinary medicine. The market is poised to expand at a CAGR of 22.4% from 2025 to 2033, culminating in an estimated value of USD 2.1 billion by 2033. This remarkable growth trajectory is fueled by the increasing demand for high-quality, diverse, and ethically sourced data sets to train machine learning and AI models for veterinary applications, as well as the accelerating integration of digital solutions in animal healthcare.

The primary growth driver for the veterinary synthetic training data sets market is the escalating need for advanced data solutions to support AI-powered diagnostic and treatment systems. As veterinary healthcare providers seek to enhance the accuracy and efficiency of disease diagnosis, synthetic data sets have become invaluable in overcoming the limitations of real-world data, such as privacy concerns, data scarcity, and labeling challenges. By generating realistic, high-volume, and customizable data, synthetic data platforms enable the development of robust AI models that can generalize across diverse animal populations, species, and clinical scenarios. This capability is especially critical in veterinary medicine, where the heterogeneity of cases and the limited availability of annotated data often hinder the progress of data-driven innovations.

Another significant factor propelling the veterinary synthetic training data sets market is the rapid digital transformation across the animal health ecosystem. Veterinary hospitals, clinics, academic institutions, and pharmaceutical companies are increasingly investing in digital infrastructure and AI-powered tools to streamline workflows, accelerate research, and improve patient outcomes. Synthetic data sets play a pivotal role in these initiatives by providing scalable, bias-mitigated, and privacy-compliant data for training, validation, and testing of AI algorithms. The growing adoption of telemedicine, wearable devices, and connected diagnostics in veterinary practice further amplifies the need for synthetic data solutions that can simulate complex, multimodal inputs and support a wide range of clinical and research applications.

Furthermore, regulatory and ethical considerations are shaping the trajectory of the veterinary synthetic training data sets market. With mounting concerns over the privacy and security of real animal health records, synthetic data offers a viable alternative that preserves sensitive information while facilitating innovation. Regulatory agencies and industry bodies are increasingly endorsing the use of synthetic data in AI model development, validation, and compliance processes. This regulatory support, combined with the ongoing advancements in generative AI, computer vision, and data augmentation techniques, is fostering a favorable environment for the adoption and commercialization of synthetic training data sets in veterinary medicine.

From a regional perspective, North America currently dominates the veterinary synthetic training data sets market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The high concentration of leading AI technology providers, veterinary research institutions, and animal health companies in the United States and Canada has fueled early adoption and innovation in this domain. Europe is witnessing steady growth, driven by strong regulatory frameworks, increasing investments in veterinary research, and the presence of prominent academic and pharmaceutical players. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by expanding animal healthcare infrastructure, rising pet ownership, and government initiatives to promote digital health. Latin America and the Middle East & Africa are gradually catching up, with growing awareness and adoption of AI-driven veterinary solutions.

Component Analysis

The component segment of the veterinary synthetic training data sets market is bifurcated into software and services, each playing a distinct yet complementary role in the ecosystem. Software solutions encompass platforms and tools for the generation, management, and deployment of synthetic data sets tailored for veterinary applications. These platforms leverage a

Training Data Platform Market Outlook

According to our latest research, the global Training Data Platform market size reached USD 2.86 billion in 2024, demonstrating robust momentum as organizations across industries accelerate their artificial intelligence (AI) and machine learning (ML) initiatives. The market is expected to expand at a CAGR of 21.4% from 2025 to 2033, reaching a projected value of USD 20.18 billion by 2033. This remarkable growth is primarily driven by the increasing demand for high-quality, large-scale training datasets to fuel advanced AI models, the proliferation of data-centric business strategies, and the expanding adoption of automation technologies across sectors.

One of the primary growth factors propelling the Training Data Platform market is the exponential rise in AI and ML adoption across diverse industries. Enterprises are increasingly leveraging AI-driven solutions to enhance operational efficiency, automate repetitive tasks, and gain actionable insights from vast amounts of unstructured and structured data. As these AI models require accurate and comprehensive training data to achieve optimal performance, organizations are turning to specialized platforms that facilitate data collection, labeling, augmentation, and management. The growing complexity and scale of AI applications, such as autonomous vehicles, predictive analytics, and personalized customer experiences, have further heightened the need for robust training data platforms capable of handling multimodal datasets and ensuring data quality.

Another significant driver fueling market growth is the evolution of data privacy regulations and the need for secure, compliant data management solutions. With regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) setting stringent standards for data handling, organizations are seeking training data platforms that offer advanced governance, anonymization, and auditability features. These platforms enable enterprises to maintain compliance while leveraging sensitive data for AI training purposes. Additionally, the increasing use of synthetic data generation, federated learning, and data augmentation techniques is expanding the scope of training data platforms, allowing organizations to overcome data scarcity and address bias or imbalance in datasets.

The surge in demand for domain-specific and application-tailored training datasets is also shaping the market landscape. Industries such as healthcare, automotive, and finance require highly specialized datasets to train models for tasks like medical image analysis, autonomous driving, and fraud detection. Training data platforms are evolving to offer industry-specific data curation, annotation tools, and integration with proprietary data sources. This trend is fostering partnerships between platform providers and domain experts, enhancing the accuracy and relevance of AI solutions. Moreover, the rise of edge computing and IoT devices is generating new data streams, further amplifying the need for scalable, cloud-native training data platforms that can ingest, process, and manage data from distributed sources.

From a regional perspective, North America currently dominates the Training Data Platform market, accounting for the largest revenue share in 2024. This leadership is attributed to the high concentration of AI technology providers, significant R&D investments, and the early adoption of digital transformation strategies across industries in the region. Europe follows closely, driven by strong regulatory frameworks and a growing emphasis on ethical AI development. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digitization, expanding IT infrastructure, and increasing government initiatives to promote AI research and innovation. Latin America and the Middle East & Africa are also emerging as promising markets, supported by rising investments in AI and data-driven business models.

Component Analysis

T

[UPDATE] You can now access MultiSen (GE and NA) collection though this portal : https://doi.theia.data-terra.org/ai4lcc/?lang=en

MultiSenGE is a new large-scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. It contains 8,157 patches of 256 * 256 pixels for Sentinel-2 L2A, Sentinel-1 GRD and a regional LULC topographic regional database.

Every file has a specific nomenclature :

Sentinel-1 patches: {tile}_{date}_S1_{x-pixel-coordinate}_{y-pixel-coordinate}.tif

Sentinel-2 patches: {tile}_{date}_S2_{x-pixel-coordinate}_{y-pixel-coordinate}.tif

Ground reference patches: {tile}_GR_{x-pixel-coordinate}_{y-pixel-coordinate}.tif

JSON Labels: {tile}_{x-pixel-coordinate}_{y-pixel-coordinate}.json

where tile is the Sentinel-2 tile number, date the date of acquisition of the patch, x-pixel-coordinate and y-pixel-coordinate are the coordinates of the patch in the tile.

In addition, you can find a set of useful python tools for extracting information about the dataset on Github : https://github.com/r-wenger/MultiSenGE-Tools

First experiments based on this dataset is in press in ISPRS Annals : Wenger, R., Puissant, A., Weber, J., Idoumghar, L., and Forestier, G.: MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 635–640, https://doi.org/10.5194/isprs-annals-V-3-2022-635-2022, 2022.

Due to the large size of the dataset, you will only find the associated JSON files on this Zenodo repository. To download the Sentinel-1, Sentinel-2 patches and the reference data, please do so via these links:

Sentinel-1 temporal serie patches: https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz

Sentinel-2 temporal serie patches: https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz

Ground reference patches: https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz

JSON files for each patch: https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz