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

The dataset contains multi-modal data from over 70,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.

More than 90,000 patients and 280,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.

Refer to the examples showcased in this GitHub repository to understand how to optimize the use of this dataset.The license of the dataset as a whole is CC BY-NC-SA. However, its individual contents may have less restrictive license types (CC BY, CC BY-NC, CC0). For instance, regarding image filess, 66K of them are CC BY, 32K are CC BY-NC-SA, 32K are CC BY-NC, and 20 of them are CC0.

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?

Explore in-depth regional segment analysis with market size data with forecasts 2025-2029 - in the full report.
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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

Automotive Data Labeling Services Market Outlook

According to our latest research, the global Automotive Data Labeling Services market size stood at USD 1.85 billion in 2024, and is projected to reach USD 10.49 billion by 2033, growing at a robust CAGR of 21.5% during the forecast period. This impressive growth trajectory is fueled by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in the automotive sector, particularly for applications such as autonomous driving and advanced driver assistance systems (ADAS). The market's expansion is further supported by the surge in data generation from connected vehicles and the critical need for high-quality labeled datasets to train sophisticated automotive algorithms.

The primary growth driver for the Automotive Data Labeling Services market is the rapid evolution and deployment of autonomous vehicles and ADAS. As automotive manufacturers and technology companies race to bring fully autonomous vehicles to market, the demand for accurately labeled data has skyrocketed. Data labeling is essential for training ML models to recognize objects, interpret road signs, and make real-time decisions in complex driving environments. The proliferation of sensors, cameras, LiDAR, and radar systems in modern vehicles has led to an exponential increase in raw data generation, necessitating advanced data annotation capabilities to ensure safety, reliability, and regulatory compliance in autonomous driving technologies.

Another significant growth factor is the expansion of connected vehicle ecosystems, which generate vast volumes of multimodal data, including images, videos, text, and sensor signals. Automotive data labeling services play a pivotal role in transforming this raw data into actionable insights by categorizing, tagging, and annotating it for various AI-driven applications. As OEMs and Tier 1 suppliers intensify their investments in next-generation vehicle platforms, the need for scalable, accurate, and cost-effective data labeling solutions becomes paramount. Moreover, the increasing complexity of automotive use cases, such as predictive maintenance, fleet management, and in-vehicle infotainment, further amplifies the demand for specialized data annotation services tailored to diverse data types and formats.

The market is also witnessing a surge in partnerships and collaborations between automotive companies and data labeling service providers, aimed at accelerating the development and deployment of AI-powered automotive solutions. Outsourcing data annotation tasks to specialized vendors enables automotive firms to focus on core competencies while ensuring the availability of high-quality labeled datasets. Furthermore, the emergence of semi-automated and fully automated labeling technologies, powered by AI and deep learning, is enhancing the scalability and efficiency of data labeling processes, thereby reducing turnaround times and operational costs. These technological advancements are expected to further catalyze market growth over the forecast period.

From a regional perspective, North America currently leads the Automotive Data Labeling Services market, driven by the strong presence of major automotive OEMs, technology giants, and a vibrant ecosystem of AI startups. Europe follows closely, benefiting from stringent regulatory standards for vehicle safety and a robust automotive manufacturing base. The Asia Pacific region is poised for the fastest growth, supported by rapid digitization, increasing vehicle production, and rising investments in smart mobility solutions across China, Japan, South Korea, and India. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a relatively nascent stage, as local automotive industries gradually embrace data-driven innovation.

Data Type Analysis

The Data Type segment of the Automotive Data Labeling Services market encompasses a diverse array of data modalities, including Image/Video, Text, Sensor Data, LiDAR, and Others. Among these, image and video data labeling accounts for the largest market share, as visual data forms the cornerstone of perception systems in autonomous vehicles and ADAS applications. Annotating images and videos enables machine learning models to detect, classify, and track objects such as pedestrians, vehicles, traffic signs, and lane markings. The complexity and volume of visual data generated by

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

Abstract: The prediction of the motion of traffic participants is a crucial aspect for the research and development of Automated Driving Systems (ADSs). Recent approaches are based on multi-modal motion prediction, which requires the assignment of a probability score to each of the multiple predicted motion hypotheses. However, there is a lack of ground truth for this probability score in the existing datasets. This implies that current Machine Learning (ML) models evaluate the multiple predictions by comparing them with the single real trajectory labeled in the dataset. In this work, a novel data-based method named Probabilistic Traffic Motion Labeling (PROMOTING) is introduced in order to (a) generate probable future routes and (b) estimate their probabilities. PROMOTING is presented with the focus on urban intersections. The generation of probable future routes is (a) based on a real traffic dataset and consists of two steps: first, a clustering of intersections with similar road topology, and second, a clustering of similar routes that are driven in each cluster from the first step. The estimation of the route probabilities is (b) based on a frequentist approach that considers how traffic participants will move in the future given their motion history. PROMOTING is evaluated with the publicly available Lyft database. The results show that PROMOTING is an appropriate approach to estimate the probabilities of the future motion of traffic participants in urban intersections. In this regard, PROMOTING can be used as a labeling approach for the generation of a labeled dataset that provides a probability score for probable future routes. Such a labeled dataset currently does not exist and would be highly valuable for ML approaches with the task of multi-modal motion prediction. The code is made open source.

The dataset contains multi-modal data from over 75,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 130,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.

Almost 100,000 patients and almost 400,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.

Refer to the examples showcased in this GitHub repository to understand how to optimize the use of this dataset.

For a detailed insight about the contents of this dataset, please refer to this data article published in Data In Brief.

OpenPack is an open-access logistics dataset for human activity recognition, which contains human movement and package information from 16 subjects in four scenarios. Human movement information is subdivided into three types of data, acceleration, physiological, and depth-sensing. The package information includes the size and number of items included in each packaging job.

In the "Humanware laboratory" at IST Osaka University, with the supervision of industrial engineers, an experiment to mimic logistic center labor was designed. 12 workers with previous packaging experience and 4 without experience performed a set of packaging tasks according to an instruction manual from a real-life logistics center. During the different scenarios, subjects were recorded while performing packing operations using Lidar, Kinect, and Realsense depth sensors while wearing 4 ATR IMU devices and 2 Empatica E4 wearable sensors. Besides sensor data, this dataset contains timestamp information collected from the hand terminal used to register product, packet, and address label codes as well as package details that can be useful to relate operations to specific packages.

The 4 different scenarios include; sequential packing, worker-decided sequence changes, pre-ordered item packing, and time-sensitive stressors. Each of the subjects performed 20 packing jobs in 5 work sessions for a total of 100 packing jobs. 53+ hours of packaging operations have been labeled into 10 global operation classes and 16 sub-action classes for this dataset. Action classes are not unique to each operation but may only appear in one or two operations.

You can find information on how to use this dataset at: https://open-pack.github.io/. For details on how this dataset was collected please check the following publication "OpenPack: A Large-Scale Dataset for Recognizing Packaging Works in IoT-Enabled Logistic Environments" 10.1109/PerCom59722.2024.10494448.

Full Dataset

In this repository, the data and label files are contained in separate files for each worker. Each worker's file contains; IMU, E4, 2d keypoint, 3d keypoint, annotation, and system-related data.

Preprocessed Dataset (IMU with operation and action Labels)

We have received many comments that it was difficult to combine multiple workers' IMU and annotation data. Therefore, we have created several CSV files containing the four IMU's sensor data and the operation labels in a single file. These files are now included as "imu-with-operation-action-labels.zip".

Preprocessed Dataset (Kinect 2D and 3D keypoint data with operation and action Labels)

We have received several requests for a preprocessed dataset containing only specific types of keypoint data with its assigned operation and action labels. Two new preprocessed files have been added for 2D and 3D keypoint data extracted from the frontal view Kinect camera. These files are:

"kinect-2d-kpt-with-operation-action-labels.zip", and

"kinect-3d-kpt-with-operation-action-labels.zip".

Work is continuously being done to update and improve this dataset. When downloading and using this dataset please verify that the version is up to date with the latest release. The latest release [1.1.0] was uploaded on 24/04/2024.

Changes LOG:

v1.0.0: Add tutorial preprocessed dataset for IMU data with operation labels.

v1.1.0: Update preprocessed datasets. (Include Kinect 2d and 3d keypoint data with Operation and action labels)

We hosted an activity recognition competition using this dataset (OpenPack v0.3.x) awarded at a PerCom 2023 Workshop! The task was very simple: Recognize 10 work operations from the OpenPack dataset. You can refer to this website for coding materials relevant to this dataset. https://open-pack.github.io/challenge2022

The FaciaVox dataset is an extensive multimodal biometric resource designed to enable in-depth exploration of face-image and voice recording research areas in both masked and unmasked scenarios.

Features of the Dataset:

1. Multimodal Data: A total of 1,800 face images (JPG) and 6,000 audio recordings (WAV) were collected, enabling cross-domain analysis of visual and auditory biometrics.

2. Participants were categorized into four age groups for structured labeling:
Label 1: Under 16 years
Label 2: 16 to less than 31 years
Label 3: 31 to less than 46 years
Label 4: 46 years and above

3. Sibling Data: Some participants are siblings, adding a challenging layer for speaker identification and facial recognition tasks due to genetic similarities in vocal and facial features. Sibling relationships are documented in the accompanying "FaciaVox List" data file.

4. Standardized Filenames: The dataset uses a consistent, intuitive naming convention for both facial images and voice recordings. Each filename includes:
Type (F: Face Image, V: Voice Recording)
Participant ID (e.g., sub001)
Mask Type (e.g., a: unmasked, b: disposable mask, etc.)
Zoom Level or Sentence ID (e.g., 1x, 3x, 5x for images or specific sentence identifier {01, 02, 03, ..., 10} for recordings)

5. Diverse Demographics: 19 different countries.

6. A challenging face recognition problem involving reflective mask shields and severe lighting conditions.

7. Each participant uttered 7 English statements and 3 Arabic statements, regardless of their native language. This adds a challenge for speaker identification.

Research Applications

FaciaVox is a versatile dataset supporting a wide range of research domains, including but not limited to:
• Speaker Identification (SI) and Face Recognition (FR): Evaluating biometric systems under varying conditions.
• Impact of Masks on Biometrics: Investigating how different facial coverings affect recognition performance.
• Language Impact on SI: Exploring the effects of native and non-native speech on speaker identification.
• Age and Gender Estimation: Inferring demographic information from voice and facial features.
• Race and Ethnicity Matching: Studying biometrics across diverse populations.
• Synthetic Voice and Deepfake Detection: Detecting cloned or generated speech.
• Cross-Domain Biometric Fusion: Combining facial and vocal data for robust authentication.
• Speech Intelligibility: Assessing how masks influence speech clarity.
• Image Inpainting: Reconstructing occluded facial regions for improved recognition.

Researchers can use the facial images and voice recordings independently or in combination to explore multimodal biometric systems. The standardized filenames and accompanying metadata make it easy to align visual and auditory data for cross-domain analyses. Sibling relationships and demographic labels add depth for tasks such as familial voice recognition, demographic profiling, and model bias evaluation.

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

MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs.

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review.

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.

• MuMu dataset (mapping, metadata, annotations and text reviews)
• Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments

These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

Scientific References

Please cite the following paper if using MuMu dataset or Tartarus library.

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916

Traditional knowledge graphs of water conservancy project risks have supported risk decision-making. However, they are constrained by limited data modalities and low accuracy in information extraction. A multimodal water conservancy project risk knowledge graph is proposed in this study, along with a synergistic strategy involving multimodal large language models Risk decision-making generation is facilitated through a multi-agent agentic retrieval-augmented generation framework. To enhance visual recognition, a DenseNet-based image classification model is improved by incorporating single-head self-attention and coordinate attention mechanisms. For textual data, risk entities such as locations, components, and events are extracted using a BERT-BiLSTM-CRF architecture. These extracted entities serve as the foundation for constructing the multimodal knowledge graph. To support generation, a multi-agent agentic retrieval-augmented generation mechanism is introduced. This mechanism enhances the reliability and interpretability of risk decision-making outputs. In experiments, the enhanced DenseNet model outperforms the original baseline in both precision and recall for image recognition tasks. In risk decision-making tasks, the proposed approach—combining a multimodal knowledge graph with a multi-agent agentic retrieval-augmented generation method—achieves strong performance on BERTScore and ROUGE-L metrics. This work presents a novel perspective for leveraging multimodal knowledge graphs in water conservancy project risk management.

ugsburg data set and Berlin data set for multimodal classification.This data set is a public data set, and the download address of the data set is provided in the related research articles. You can download it by following the link address. The data set is preprocessed into training data, test data and real label data respectively, and the real label data can be divided into training label data and test label data. The processing method is realized by writing a preprocessor in python.Augsburg data set:The data set contains HS data, SAR data and DSM data, and the data is divided into training sets, test sets and real label data.Berlin data set:The data set includes HS data, SAR data, and data has been divided into training set, test set and real label data.

In this experiment an additional modality containing labels, L, is considered. The results are computed on average for a cross-validation of the train and test sets; standard deviations are also given.

Annotation Services for Roadway AI Models Market Outlook

According to our latest research, the global Annotation Services for Roadway AI Models market size reached USD 1.47 billion in 2024, driven by rising investments in intelligent transportation and increasing adoption of autonomous vehicle technologies. The market is expected to grow at a robust CAGR of 22.8% from 2025 to 2033, reaching a projected value of USD 11.9 billion by 2033. This remarkable growth is primarily attributed to the surging demand for high-quality annotated data to train, validate, and test AI models for roadway applications, as well as the proliferation of smart city initiatives and government mandates for road safety and efficiency.

One of the primary growth factors driving the Annotation Services for Roadway AI Models market is the rapid evolution and deployment of autonomous vehicles. As the automotive industry transitions toward self-driving technologies, the need for accurately labeled datasets to train perception, navigation, and decision-making systems becomes paramount. Image, video, and sensor data annotation services are essential for enabling AI models to recognize road signs, lane markings, pedestrians, and other critical elements in real-world environments. The complexity of roadway scenarios requires vast quantities of diverse, high-precision annotated data, fueling the demand for specialized annotation service providers. Furthermore, regulatory requirements for autonomous vehicle safety and validation have intensified, compelling OEMs and technology developers to invest heavily in comprehensive annotation workflows.

Another significant driver is the increasing implementation of AI-powered traffic management and road infrastructure monitoring solutions. Governments and urban planners are leveraging artificial intelligence to optimize traffic flow, reduce congestion, and enhance road safety. Annotation services play a crucial role in enabling these AI systems to interpret real-time data from surveillance cameras, drones, and sensor networks. By providing meticulously labeled datasets, annotation providers facilitate the development of models capable of detecting incidents, monitoring road conditions, and predicting traffic patterns. The growing emphasis on smart city initiatives and intelligent transportation systems worldwide is expected to further accelerate the adoption of annotation services for roadway AI models, as cities seek to improve mobility and sustainability.

In addition, advancements in sensor technologies and the integration of multimodal data sources are expanding the scope of annotation services within the roadway AI ecosystem. Modern vehicles and infrastructure are equipped with a variety of sensors, including LiDAR, radar, and ultrasonic devices, generating complex datasets that require expert annotation. The ability to accurately label and synchronize data from multiple sensor modalities is critical for developing robust AI models capable of operating in diverse and challenging environments. As the industry moves toward higher levels of vehicle autonomy and more sophisticated traffic management systems, the demand for comprehensive, multimodal annotation services is expected to surge, creating new opportunities for service providers and technology vendors alike.

The role of Data Annotationplace in the development of AI models for roadway applications cannot be overstated. As the demand for precise and reliable data increases, Data Annotationplace has emerged as a critical component in the AI training pipeline. This process involves meticulously labeling data to ensure that AI systems can accurately interpret and respond to real-world scenarios. By providing high-quality annotated datasets, Data Annotationplace enables the creation of robust AI models that enhance the safety and efficiency of autonomous vehicles and intelligent transportation systems. As the complexity of roadway environments continues to evolve, the importance of Data Annotationplace in supporting AI innovation and deployment will only grow.

From a regional perspective, North America currently leads the Annotation Services for Roadway AI Models market, driven by substantial investments in autonomous vehicle development, a strong presence of automotive OEMs, and supportive regulatory frameworks. The region's advanced infrastructure and early ado

1. 150,000 hours TV videos data Data content: 150,000 hours high-quality TV videos, with legitimate copyright and metadata like titles

Data distribution: covers various video types including TV dramas and self-produced programs Data quality: complete video content with audio and resolutions available in both 1080P and 4K, free from watermarks, mosaics and other noise

1. 20 Million high-quality video data Data content: 20 million high-quality videos captured by photographers, with legitimate copyright, including labels and captions in Chinese and English as metadata Data distribution: covers various video contents such as portraits, animals, plants, aerial shots, landscapes, urban scenes, and supports filtering by keywords such as subject, background, motion, cinematography, rendered video Data quality: complete video content with resolutions available in both 1080P and 4K, free from watermarks, mosaics, and other noise

2. 250 Million high-quality image data Data content: 250 million high-quality images captured by photographers, with legitimate copyright, including labels and captions in Chinese and English as metadata Data distribution: covers various image contents such as portraits, animals, plants, food, landscapes and Chinese elements, as well as multiple image types like illustrations and vector graphics Data quality: complete image content with resolutions available in both 1080P and 4K, free from watermarks, mosaics, and other noise

3. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Audio Data and 800TB of computer vision data. These ready-to-go Machine Learning (ML) Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/llm?source=Datarade

This dataset is developed to support the creation of intelligent, explainable models for evaluating Physical Education (PE) teaching effectiveness using multimodal data. It simulates real-world classroom settings by integrating information from physical movement sensors, verbal instruction (via audio/video abstraction), and student perception surveys.

The dataset comprises 1,000 instances, each representing a student’s interaction in a PE class. Data is generated across three modalities:

Sensor-based physical movement data – mimicking inertial measurements from wearable devices such as accelerometers and gyroscopes.

Instructional interaction data – posture labels and sentiment scores from audio/video streams.

Student perception data – capturing subjective experiences through survey-style responses (e.g., clarity of instruction, engagement, comfort).

A target label, teaching_effectiveness_category, classifies each entry as Low, Medium, or High based on a calculated effectiveness score derived from key educational metrics.

Key Features 📡 Multimodal Inputs: Combines motion sensor data, labeled postures, and survey responses.

📊 Categorical Target: Teaching effectiveness labeled as Low, Medium, or High.

🎯 Grounded Scoring Logic: Effectiveness score computed from clarity, energy, and engagement.

🧠 Supports Interpretability: Ideal for explainable AI models such as EBM, SHAP, etc.

🏃 Activity-Oriented Labels: Includes common physical actions like Jump, Squat, Idle, and Stretch.

📅 Timestamped Entries: All sensor data are timestamped (set in 2024) to enable temporal analysis.

🔍 Suitable for Research: Useful for ML tasks in education, human activity recognition, and model evaluation.

We collect a unique data resource from multiple sensor modalities for the purpose of training and evaluating algorithms for monitoring electrical critical infrastructure within the Canadian environment. Two different sensor modes were considered for inclusion in the data resource: colour images and 3D Light Detection And Ranging (LiDAR) sensors. This research examined these different sensors for their potential use in monitoring electrical critical infrastructure, such as poles, high-tension wires, and transformers. A multimodal data acquisition system was assembled using commercially available sensors. The acquisition system was deployed on a ground vehicle in the National Capital Region to collect multimodal data of power critical infrastructure along Canadian road corridors. Data was collected and registered in both time and space in order to enable multi-sensor fusion. Data Description: A total of 1.77 GB of data captured from total of 440 scenes, for each scenes we included images, Segmentation label and LiDAR data.The image data was annotated for semantic segmentation with five different classes. We have 440 scenes and for each of scene we have following data: |_Image |_Labels | |_NPY | |_PNG |_LiDAR

REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly

📋 Introduction

Robotic manipulation remains a core challenge in robotics, particularly for contact-rich tasks such as industrial assembly and disassembly. Existing datasets have significantly advanced learning in manipulation but are primarily focused on simpler tasks like object rearrangement, falling short of capturing the complexity and physical dynamics involved in assembly and disassembly. To bridge this gap, we present REASSEMBLE (Robotic assEmbly disASSEMBLy datasEt), a new dataset designed specifically for contact-rich manipulation tasks. Built around the NIST Assembly Task Board 1 benchmark, REASSEMBLE includes four actions (pick, insert, remove, and place) involving 17 objects. The dataset contains 4,551 demonstrations, of which 4,035 were successful, spanning a total of 781 minutes. Our dataset features multi-modal sensor data including event cameras, force-torque sensors, microphones, and multi-view RGB cameras. This diverse dataset supports research in areas such as learning contact-rich manipulation, task condition identification, action segmentation, and more. We believe REASSEMBLE will be a valuable resource for advancing robotic manipulation in complex, real-world scenarios.

{"Hama1": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "Hama2": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "DAVIS346": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ], 
 "NIST_Board1": [
    [x ,y, z],
    [qx, qy, qz, qw]
 ]
}

[x, y, z] represent the position of the object, and [qx, qy, qz, qw] represent its orientation as a quaternion.

The HDF5 (.h5) format organizes data into two main types of structures: datasets, which hold the actual data, and groups, which act like folders that can contain datasets or other groups. In the diagram below, groups are shown as folder icons, and datasets as file icons. The main group of the file directly contains the video, audio, and event data. To save memory, video and audio are stored as encoded byte strings, while event data is stored as arrays. The robot’s proprioceptive information is kept in the robot_state group as arrays. Because different sensors record data at different rates, the arrays vary in length (signified by the N_xxx variable in the data shapes). To align the sensory data, each sensor’s timestamps are stored separately in the timestamps group. Information about action segments is stored in the segments_info group. Each segment is saved as a subgroup, named according to its order in the demonstration, and includes a start timestamp, end timestamp, a success indicator, and a natural language description of the action. Within each segment, low-level skills are organized under a low_level subgroup, following the same structure as the high-level annotations.

📁

The splits folder contains two text files which list the h5 files used for the traning and validation splits.

📌 Important Resources

The project website contains more details about the REASSEMBLE dataset. The Code for loading and visualizing the data is avaibile on our github repository.

📄 Project website: https://tuwien-asl.github.io/REASSEMBLE_page/
💻 Code: https://github.com/TUWIEN-ASL/REASSEMBLE

Dataset Description

This dataset contains Reddit posts with their associated texts and thumbnails, labeled for binary sentiment based on the source community context.

Dataset Structure

Format: 3 CSV files (training, validation, test sets) Associated files: Folder of thumbnail images

Features

target - Type: Binary (0/1) - Description: Sentiment label derived from source subreddit/community - Values: 0 and 1

text

• Type: String
• Description: Combined title and body text from Reddit posts
• Processing: Raw text concatenation of post title and body

thumbnail

• Type: String
• Description: Filename reference to associated image in thumbnails folder
• Format: Image filename

processed_text

• Type: String
• Description: Preprocessed version of the text field
• Processing: Language detection, text cleaning and standarization

Data Collection

Collection method: Extracted from various Reddit communities/threads, posts originally made in 2024 ( r/MadeMeSmile, r/happy, r/UpliftingNews, r/PublicFreakout, r/ActualPublicFreakouts ) Labeling: Binary sentiment labels assigned based on source community