The Data Annotation and Labeling Tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in the burgeoning fields of artificial intelligence (AI) and machine learning (ML). The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $10 billion by 2033. This expansion is fueled by several key factors. The automotive industry leverages data annotation for autonomous driving systems development, while healthcare utilizes it for medical image analysis and diagnostics. Financial services increasingly adopt these tools for fraud detection and risk management, and retail benefits from enhanced product recommendations and customer experience personalization. The prevalence of both supervised and unsupervised learning techniques necessitates diverse data annotation solutions, fostering market segmentation across manual, semi-supervised, and automatic tools. Market restraints include the high cost of data annotation and the need for skilled professionals to manage the annotation process effectively. However, the ongoing advancements in automation and the decreasing cost of computing power are mitigating these challenges. The North American market currently holds a significant share, with strong growth also expected from Asia-Pacific regions driven by increasing AI adoption. Competition in the market is intense, with established players like Labelbox and Scale AI competing with emerging companies such as SuperAnnotate and Annotate.io. These companies offer a range of solutions catering to varying needs and budgets. The market's future growth hinges on continued technological innovation, including the development of more efficient and accurate annotation tools, integration with existing AI/ML platforms, and expansion into new industry verticals. The increasing adoption of edge AI and the growth of data-centric AI further enhance the market potential. Furthermore, the growing need for data privacy and security is likely to drive demand for tools that prioritize data protection, posing both a challenge and an opportunity for providers to offer specialized solutions. The market's success will depend on the ability of vendors to adapt to evolving needs and provide scalable, cost-effective, and reliable annotation solutions.

Discover the booming Data Annotation & Labeling Tool market! Explore a comprehensive analysis revealing a $2B market in 2025, projected to reach $10B by 2033, driven by AI and ML adoption. Learn about key trends, regional insights, and leading companies shaping this rapidly evolving landscape.

1. Overview We provide various types of Annotated Imagery Data annotation services, including:
2. Bounding box
3. Polygon
4. Segmentation
5. Polyline
6. Key points
7. Image classification
8. Image description ...
9. Our Capacity
10. Platform: Our platform supports human-machine interaction and semi-automatic labeling, increasing labeling efficiency by more than 30% per annotator.It has successfully been applied to nearly 5,000 projects.

• Annotation Tools: Nexdata's platform integrates 30 sets of annotation templates, covering audio, image, video, point cloud and text.

-Secure Implementation: NDA is signed to gurantee secure implementation and Annotated Imagery Data is destroyed upon delivery.

-Quality: Multiple rounds of quality inspections ensures high quality data output, certified with ISO9001

1. About Nexdata Nexdata has global data processing centers and more than 20,000 professional annotators, supporting on-demand data annotation services, such as speech, image, video, point cloud and Natural Language Processing (NLP) Data, etc. Please visit us at https://www.nexdata.ai/computerVisionTraining?source=Datarade

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Data Labeling And Annotation Tools Market Size 2025-2029

The data labeling and annotation tools market size is valued to increase USD 2.69 billion, at a CAGR of 28% from 2024 to 2029. Explosive growth and data demands of generative AI will drive the data labeling and annotation tools market.

Major Market Trends & Insights

North America dominated the market and accounted for a 47% growth during the forecast period.
By Type - Text segment was valued at USD 193.50 billion in 2023
By Technique - Manual labeling segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 651.30 billion
Market Future Opportunities: USD USD 2.69 billion 
CAGR : 28%
North America: Largest market in 2023

Market Summary

The market is a dynamic and ever-evolving landscape that plays a crucial role in powering advanced technologies, particularly in the realm of artificial intelligence (AI). Core technologies, such as deep learning and machine learning, continue to fuel the demand for data labeling and annotation tools, enabling the explosive growth and data demands of generative AI. These tools facilitate the emergence of specialized platforms for generative AI data pipelines, ensuring the maintenance of data quality and managing escalating complexity. Applications of data labeling and annotation tools span various industries, including healthcare, finance, and retail, with the market expected to grow significantly in the coming years. According to recent studies, the market share for data labeling and annotation tools is projected to reach over 30% by 2026. Service types or product categories, such as manual annotation, automated annotation, and semi-automated annotation, cater to the diverse needs of businesses and organizations. Regulations, such as GDPR and HIPAA, pose challenges for the market, requiring stringent data security and privacy measures. Regional mentions, including North America, Europe, and Asia Pacific, exhibit varying growth patterns, with Asia Pacific expected to witness the fastest growth due to the increasing adoption of AI technologies. The market continues to unfold, offering numerous opportunities for innovation and growth.

What will be the Size of the Data Labeling And Annotation Tools Market during the forecast period?

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How is the Data Labeling And Annotation Tools Market Segmented and what are the key trends of market segmentation?

The data labeling and annotation tools 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. TypeTextVideoImageAudioTechniqueManual labelingSemi-supervised labelingAutomatic labelingDeploymentCloud-basedOn-premisesGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalySpainUKAPACChinaSouth AmericaBrazilRest of World (ROW)

By Type Insights

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

The market is witnessing significant growth, fueled by the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies. According to recent studies, the market for data labeling and annotation services is projected to expand by 25% in the upcoming year. This expansion is primarily driven by the burgeoning demand for high-quality, accurately labeled datasets to train advanced AI and ML models. Scalable annotation workflows are essential to meeting the demands of large-scale projects, enabling efficient labeling and review processes. Data labeling platforms offer various features, such as error detection mechanisms, active learning strategies, and polygon annotation software, to ensure annotation accuracy. These tools are integral to the development of image classification models and the comparison of annotation tools. Video annotation services are gaining popularity, as they cater to the unique challenges of video data. Data labeling pipelines and project management tools streamline the entire annotation process, from initial data preparation to final output. Keypoint annotation workflows and annotation speed optimization techniques further enhance the efficiency of annotation projects. Inter-annotator agreement is a critical metric in ensuring data labeling quality. The data labeling lifecycle encompasses various stages, including labeling, assessment, and validation, to maintain the highest level of accuracy. Semantic segmentation tools and label accuracy assessment methods contribute to the ongoing refinement of annotation techniques. Text annotation techniques, such as named entity recognition, sentiment analysis, and text classification, are essential for natural language processing. Consistency checks an

Image Annotation Tool Market Outlook

The global image annotation tool market size is projected to grow from approximately $700 million in 2023 to an estimated $2.5 billion by 2032, exhibiting a remarkable compound annual growth rate (CAGR) of 15.2% over the forecast period. The surging demand for machine learning and artificial intelligence applications is driving this robust market expansion. Image annotation tools are crucial for training AI models to recognize and interpret images, a necessity across diverse industries.

One of the key growth factors fueling the image annotation tool market is the rapid adoption of AI and machine learning technologies across various sectors. Organizations in healthcare, automotive, retail, and many other industries are increasingly leveraging AI to enhance operational efficiency, improve customer experiences, and drive innovation. Accurate image annotation is essential for developing sophisticated AI models, thereby boosting the demand for these tools. Additionally, the proliferation of big data analytics and the growing necessity to manage large volumes of unstructured data have amplified the need for efficient image annotation solutions.

Another significant driver is the increasing use of autonomous systems and applications. In the automotive industry, for instance, the development of autonomous vehicles relies heavily on annotated images to train algorithms for object detection, lane discipline, and navigation. Similarly, in the healthcare sector, annotated medical images are indispensable for developing diagnostic tools and treatment planning systems powered by AI. This widespread application of image annotation tools in the development of autonomous systems is a critical factor propelling market growth.

The rise of e-commerce and the digital retail landscape has also spurred demand for image annotation tools. Retailers are using these tools to optimize visual search features, personalize shopping experiences, and enhance inventory management through automated recognition of products and categories. Furthermore, advancements in computer vision technology have expanded the capabilities of image annotation tools, making them more accurate and efficient, which in turn encourages their adoption across various industries.

Data Annotation Software plays a pivotal role in the image annotation tool market by providing the necessary infrastructure for labeling and categorizing images efficiently. These software solutions are designed to handle various annotation tasks, from simple bounding boxes to complex semantic segmentation, enabling organizations to generate high-quality training datasets for AI models. The continuous advancements in data annotation software, including the integration of machine learning algorithms for automated labeling, have significantly enhanced the accuracy and speed of the annotation process. As the demand for AI-driven applications grows, the reliance on robust data annotation software becomes increasingly critical, supporting the development of sophisticated models across industries.

Regionally, North America holds the largest share of the image annotation tool market, driven by significant investments in AI and machine learning technologies and the presence of leading technology companies. Europe follows, with strong growth supported by government initiatives promoting AI research and development. The Asia Pacific region presents substantial growth opportunities due to the rapid digital transformation in emerging economies and increasing investments in technology infrastructure. Latin America and the Middle East & Africa are also expected to witness steady growth, albeit at a slower pace, due to the gradual adoption of advanced technologies.

Component Analysis

The image annotation tool market by component is segmented into software and services. The software segment dominates the market, encompassing a variety of tools designed for different annotation tasks, from simple image labeling to complex polygonal, semantic, or instance segmentation. The continuous evolution of software platforms, integrating advanced features such as automated annotation and machine learning algorithms, has significantly enhanced the accuracy and efficiency of image annotations. Furthermore, the availability of open-source annotation tools has lowered the entry barrier, allowing more organizations to adopt these technologies.

Services associated with image ann

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in the burgeoning artificial intelligence (AI) and machine learning (ML) sectors. The market's expansion is fueled by several key factors. Firstly, the rising adoption of AI across various industries, including healthcare, automotive, and finance, necessitates large volumes of accurately labeled data. Secondly, open-source tools offer a cost-effective alternative to proprietary solutions, making them attractive to startups and smaller companies with limited budgets. Thirdly, the collaborative nature of open-source development fosters continuous improvement and innovation, leading to more sophisticated and user-friendly tools. While the cloud-based segment currently dominates due to scalability and accessibility, on-premise solutions maintain a significant share, especially among organizations with stringent data security and privacy requirements. The geographical distribution reveals strong growth in North America and Europe, driven by established tech ecosystems and early adoption of AI technologies. However, the Asia-Pacific region is expected to witness significant growth in the coming years, fueled by increasing digitalization and government initiatives promoting AI development. The market faces some challenges, including the need for skilled data labelers and the potential for inconsistencies in data quality across different open-source tools. Nevertheless, ongoing developments in automation and standardization are expected to mitigate these concerns. The forecast period of 2025-2033 suggests a continued upward trajectory for the open-source data labeling tool market. Assuming a conservative CAGR of 15% (a reasonable estimate given the rapid advancements in AI and the increasing need for labeled data), and a 2025 market size of $500 million (a plausible figure considering the significant investments in the broader AI market), the market is projected to reach approximately $1.8 billion by 2033. This growth will be further shaped by the ongoing development of new features, improved user interfaces, and the integration of advanced techniques such as active learning and semi-supervised learning within open-source tools. The competitive landscape is dynamic, with both established players and emerging startups contributing to the innovation and expansion of this crucial segment of the AI ecosystem. Companies are focusing on improving the accuracy, efficiency, and accessibility of their tools to cater to a growing and diverse user base.

The booming Data Labeling Tools market is projected to reach $10 billion by 2033, fueled by AI & ML advancements. This in-depth analysis reveals key market trends, growth drivers, challenges, and leading companies shaping this dynamic sector. Explore market size, segmentation, and regional insights to understand the opportunities and competitive landscape.

The Automated Data Annotation Tools market is booming, projected to reach $3.2 Billion by 2033. Discover key market trends, growth drivers, and leading companies shaping this vital sector for AI development. Explore our in-depth analysis covering market segmentation, regional insights, and future forecasts.

The image annotation software market is booming, projected to reach $10 billion by 2033 with a 25% CAGR. Learn about key drivers, trends, and leading companies shaping this rapidly evolving sector fueled by AI and machine learning advancements. Discover market size, segmentation, and regional analysis in this comprehensive report.

Computer Vision Annotation Tool Market Outlook

According to our latest research, the global Computer Vision Annotation Tool market size reached USD 2.16 billion in 2024, and it is expected to grow at a robust CAGR of 16.8% from 2025 to 2033. By 2033, the market is forecasted to achieve a value of USD 9.28 billion, driven by the rising adoption of artificial intelligence and machine learning applications across diverse industries. The proliferation of computer vision technologies in sectors such as automotive, healthcare, retail, and robotics is a key growth factor, as organizations increasingly require high-quality annotated datasets to train and deploy advanced AI models.

The growth of the Computer Vision Annotation Tool market is primarily propelled by the surging demand for data annotation solutions that facilitate the development of accurate and reliable machine learning algorithms. As enterprises accelerate their digital transformation journeys, the need for precise labeling of images, videos, and other multimedia content has intensified. This is especially true for industries like autonomous vehicles, where annotated datasets are crucial for object detection, path planning, and safety assurance. Furthermore, the increasing complexity of visual data and the necessity for scalable annotation workflows are compelling organizations to invest in sophisticated annotation tools that offer automation, collaboration, and integration capabilities, thereby fueling market expansion.

Another significant growth driver is the rapid evolution of AI-powered applications in healthcare, retail, and security. In the healthcare sector, computer vision annotation tools are pivotal in training models for medical imaging diagnostics, disease detection, and patient monitoring. Similarly, in retail, these tools enable the development of intelligent systems for inventory management, customer behavior analysis, and automated checkout solutions. The security and surveillance segment is also witnessing heightened adoption, as annotated video data becomes essential for facial recognition, threat detection, and crowd monitoring. The convergence of these trends is accelerating the demand for advanced annotation platforms that can handle diverse data modalities and deliver high annotation accuracy at scale.

The increasing availability of cloud-based annotation solutions is further catalyzing market growth by offering flexibility, scalability, and cost-effectiveness. Cloud deployment models allow organizations to access powerful annotation tools remotely, collaborate with distributed teams, and leverage on-demand computing resources. This is particularly advantageous for large-scale projects that require the annotation of millions of images or videos. Moreover, the integration of automation features such as AI-assisted labeling, quality control, and workflow management is enhancing productivity and reducing time-to-market for AI solutions. As a result, both large enterprises and small-to-medium businesses are embracing cloud-based annotation platforms to streamline their AI development pipelines.

From a regional perspective, North America leads the Computer Vision Annotation Tool market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the presence of major technology companies, robust AI research ecosystems, and early adoption of computer vision solutions in sectors like automotive, healthcare, and security. Europe follows closely, driven by regulatory support for AI innovation and growing investments in smart manufacturing and healthcare technologies. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by expanding digital infrastructure, government initiatives to promote AI adoption, and the rise of technology startups. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a comparatively moderate pace, as organizations in these regions increasingly recognize the value of annotated data for digital transformation initiatives.

Component Analysis

The Computer Vision Annotation Tool market is segmented by component into software and services, each playing a distinct yet complementary role in the value chain. The software segment encompasses standalone annotation platforms, integrated development environments, and specialized tools designed for labeling images, videos, text, and audio. These solutions are characterized by fe

This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021

The training_data.zip contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).

The model.zip file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an .npz file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).

The test_data_c6.zip file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize

The test_data_c1.zip file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize

The predictions.zip file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).

The automated data annotation tool market is booming, projected to reach $10 billion by 2033. Learn about market trends, key players (Amazon, Google, etc.), and the driving forces behind this explosive growth in AI training data. Discover insights into regional market shares and segmentation data.

A dog segmentation dataset created manually typically involves the following steps:

Image selection: Selecting a set of images that include dogs in various poses and backgrounds.

Image labeling: Manually labeling the dogs in each image using a labeling tool, where each dog is segmented and assigned a unique label.

Image annotation: Annotating the labeled images with the corresponding segmentation masks, where the dog region is assigned a value of 1 and the background region is assigned a value of 0.

Dataset splitting: Splitting the annotated dataset into training, validation, and test sets.

Dataset format: Saving the annotated dataset in a format suitable for use in machine learning frameworks such as TensorFlow or PyTorch.

Dataset characteristics: The dataset may have varying image sizes and resolutions, different dog breeds, backgrounds, lighting conditions, and other variations that are typical of natural images.

Dataset size: The size of the dataset can vary, but it should be large enough to provide a sufficient amount of training data for deep learning models.

Dataset availability: The dataset may be made publicly available for research and educational purposes.

Overall, a manually created dog segmentation dataset provides a high-quality training data for deep learning models and is essential for developing robust segmentation models.

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many of the available datasets do not provide annotations of tumors or organs-at-risk, crucial for the assessment of these tools. This is due to the fact that annotation of medical images is time consuming and requires domain expertise. It has been demonstrated that artificial intelligence (AI) based annotation tools can achieve acceptable performance and thus can be used to automate the annotation of large datasets. As part of the effort to enrich the public data available within NCI Imaging Data Commons (IDC) (https://imaging.datacommons.cancer.gov/) [1], we introduce this dataset that consists of such AI-generated annotations for two publicly available medical imaging collections of Computed Tomography (CT) images of the chest. For detailed information concerning this dataset, please refer to our publication here [2].

We use publicly available pre-trained AI tools to enhance CT lung cancer collections that are unlabeled or partially labeled. The first tool is the nnU-Net deep learning framework [3] for volumetric segmentation of organs, where we use a pretrained model (Task D18 using the SegTHOR dataset) for labeling volumetric regions in the image corresponding to the heart, trachea, aorta and esophagus. These are the major organs-at-risk for radiation therapy for lung cancer. We further enhance these annotations by computing 3D shape radiomics features using the pyradiomics package [4]. The second tool is a pretrained model for per-slice automatic labeling of anatomic landmarks and imaged body part regions in axial CT volumes [5].

We focus on enhancing two publicly available collections, the Non-small Cell Lung Cancer Radiomics (NSCLC-Radiomics collection) [6,7], and the National Lung Screening Trial (NLST collection) [8,9]. The CT data for these collections are available both in The Cancer Imaging Archive (TCIA) [10] and in NCI Imaging Data Commons (IDC). Further, the NSLSC-Radiomics collection includes expert-generated manual annotations of several chest organs, allowing us to quantify performance of the AI tools in that subset of data.

IDC is relying on the DICOM standard to achieve FAIR [10] sharing of data and interoperability. Generated annotations are saved as DICOM Segmentation objects (volumetric segmentations of regions of interest) created using the dcmqi [12], and DICOM Structured Report (SR) objects (per-slice annotations of the body part imaged, anatomical landmarks and radiomics features) created using dcmqi and highdicom [13]. 3D shape radiomics features and corresponding DICOM SR objects are also provided for the manual segmentations available in the NSCLC-Radiomics collection.

The dataset is available in IDC, and is accompanied by our publication here [2]. This pre-print details how the data were generated, and how the resulting DICOM objects can be interpreted and used in tools. Additionally, for further information about how to interact with and explore the dataset, please refer to our repository and accompanying Google Colaboratory notebook.

The annotations are organized as follows. For NSCLC-Radiomics, three nnU-Net models were evaluated ('2d-tta', '3d_lowres-tta' and '3d_fullres-tta'). Within each folder, the PatientID and the StudyInstanceUID are subdirectories, and within this the DICOM Segmentation object and the DICOM SR for the 3D shape features are stored. A separate directory for the DICOM SR body part regression regions ('sr_regions') and landmarks ('sr_landmarks') are also provided with the same folder structure as above. Lastly, the DICOM SR for the existing manual annotations are provided in the 'sr_gt' directory. For NSCLC-Radiomics, each patient has a single StudyInstanceUID. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files.

nsclc

2d-tta

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_SEG.dcm

ReferencedSeriesInstanceUID_features_SR.dcm

3d_lowres-tta

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_SEG.dcm

ReferencedSeriesInstanceUID_features_SR.dcm

3d_fullres-tta

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_SEG.dcm

ReferencedSeriesInstanceUID_features_SR.dcm

sr_regions

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_regions_SR.dcm

sr_landmarks

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_landmarks_SR.dcm

sr_gt

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_features_SR.dcm

For NLST, the '3d_fullres-tta' model was evaluated. The data is organized the same as above, where within each folder the PatientID and the StudyInstanceUID are subdirectories. For the NLST collection, it is possible that some patients have more than one StudyInstanceUID subdirectory. A separate directory for the DICOM SR body par regions ('sr_regions') and landmarks ('sr_landmarks') are also provided. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files.

nlst

3d_fullres-tta

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_SEG.dcm

ReferencedSeriesInstanceUID_features_SR.dcm

sr_regions

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_regions_SR.dcm

sr_landmarks

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_landmarks_SR.dcm

The query used for NSCLC-Radiomics is here, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is here. The query used for NLST is here, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is here. The two csv files that describe the series analyzed, nsclc_series_analyzed.csv and nlst_series_analyzed.csv, are also available as uploads to this repository.

Version updates:

Version 2: For the regions SR and landmarks SR, changed to use a distinct TrackingUniqueIdentifier for each MeasurementGroup. Also instead of using TargetRegion, changed to use FindingSite. Additionally for the landmarks SR, the TopographicalModifier was made a child of FindingSite instead of a sibling.

Version 3: Added the two csv files that describe which series were analyzed

Version 4: Modified the landmarks SR as the TopographicalModifier for the Kidney landmark (bottom) does not describe the landmark correctly. The Kidney landmark is the "first slice where both kidneys can be seen well." Instead, removed the use of the TopographicalModifier for that landmark. For the features SR, modified the units code for the Flatness and Elongation, as we incorrectly used mm units instead of no units.

The booming annotation software market is projected to reach $2337.4 million by 2025, with a 22.1% CAGR. Discover key trends, drivers, and leading companies shaping this crucial sector for AI and machine learning development. Explore market segmentation and regional insights in our comprehensive analysis.

The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022). All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.) The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times. Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.

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 booming manual data annotation tools market is projected to reach $1045.4 million by 2025, growing at a CAGR of 14.2% through 2033. Learn about key drivers, trends, regional insights, and leading companies shaping this crucial sector for AI development. Explore market segmentation by application (IT, BFSI, Healthcare, etc.) and annotation type (image/video, text, audio).

The Open Source Data Labeling Tool market has emerged as an essential component in the data-driven landscape, enabling organizations to efficiently label and annotate vast amounts of data for machine learning and artificial intelligence applications. These tools play a crucial role in industries such as healthcare,