The open-source data annotation 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's expansion is fueled by the need for efficient and cost-effective annotation solutions, particularly for large datasets. Organizations across various sectors, including automotive, healthcare, and finance, are leveraging these tools to improve the accuracy and performance of their AI models. The availability of open-source alternatives offers a significant advantage over proprietary solutions, enabling developers and researchers to customize tools according to their specific needs and avoid vendor lock-in. Furthermore, the collaborative nature of open-source projects fosters innovation and continuous improvement, resulting in a more dynamic and rapidly evolving ecosystem. While the market is relatively nascent, it exhibits a substantial growth trajectory, attracting numerous companies and developers, as evidenced by the active participation of organizations such as Alecion, Amazon Mechanical Turk, and Appen Limited. This competitive landscape further accelerates innovation and accessibility. The open-source nature of these tools also democratizes access to advanced AI development capabilities. Smaller companies and individual researchers can now participate in the development and deployment of AI solutions, leveling the playing field and fostering wider adoption. However, the market faces challenges such as the need for ongoing community support and maintenance of these tools, ensuring their long-term viability and preventing fragmentation. Despite these challenges, the future outlook for the open-source data annotation tool market remains positive, with continued growth driven by increased adoption in various industries and advancements in AI and ML technologies. The market is predicted to maintain a healthy compound annual growth rate (CAGR) over the forecast period, reflecting the sustained demand for efficient and accessible data annotation solutions.

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in various AI applications. The market's expansion is fueled by several key factors: the rising adoption of machine learning and deep learning algorithms across industries, the need for efficient and cost-effective data annotation solutions, and a growing preference for customizable and flexible tools that can adapt to diverse data types and project requirements. While proprietary solutions exist, the open-source ecosystem offers advantages including community support, transparency, cost-effectiveness, and the ability to tailor tools to specific needs, fostering innovation and accessibility. The market is segmented by tool type (image, text, video, audio), deployment model (cloud, on-premise), and industry (automotive, healthcare, finance). We project a market size of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of 25% from 2025 to 2033, reaching approximately $2.7 billion by 2033. This growth is tempered by challenges such as the complexities associated with data security, the need for skilled personnel to manage and use these tools effectively, and the inherent limitations of certain open-source solutions compared to their commercial counterparts. Despite these restraints, the open-source model's inherent flexibility and cost advantages will continue to attract a significant user base. The market's competitive landscape includes established players like Alecion and Appen, alongside numerous smaller companies and open-source communities actively contributing to the development and improvement of these tools. Geographical expansion is expected across North America, Europe, and Asia-Pacific, with the latter projected to witness significant growth due to the increasing adoption of AI and machine learning in developing economies. Future market trends point towards increased integration of automated labeling techniques within open-source tools, enhanced collaborative features to improve efficiency, and further specialization to cater to specific data types and industry-specific requirements. Continuous innovation and community contributions will remain crucial drivers of growth in this dynamic market segment.

Discover the booming market for open-source data labeling tools! Learn about its $500 million valuation in 2025, projected 25% CAGR, key drivers, and top players shaping this rapidly expanding sector within the AI revolution. Explore market trends and forecasts through 2033.

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

Discover the booming open-source data labeling tool market! Explore key trends, growth drivers, and regional insights from 2019-2033. Learn about leading companies and the future of AI-powered data annotation.

INCEpTION is an open-source text annotation tool primarily designed to annotate text documents. It supports annotations of words and sentences as well as linking annotations to each other.

• Collaborative Annotation: Multiple users can work on the same project, with built-in support for inter-annotator agreement metrics and different workflow management schemes.
• Customizable Annotation Layers: Users can define custom annotation schemas and layers tailored to specific project needs.
• Knowledge Base Integration: Annotations can be linked to knowledge bases and terminologies such as Wikidata, SNOMED CT and other RDF/OWL/OBO resources.
• Machine Learning Integration: INCEpTION can train and use machine learning models to suggest annotations, improving the efficiency of the annotation process.
• Interoperability: It supports a wide range of data formats (e.g., XMI, CoNLL, TSV), making it easy to import and export data for use with other NLP tools.

These features make INCEpTION a comprehensive solution for building and managing annotated corpora.

Explore the burgeoning Open Source Data Labeling Tool market, driven by AI/ML adoption, cloud solutions, and key industry applications. Discover market size, growth forecasts, and trends.

The data annotation and labeling tools market is experiencing robust growth, driven by the escalating demand for high-quality training data in the burgeoning fields of artificial intelligence (AI) and machine learning (ML). The market's expansion is fueled by the increasing adoption of AI across diverse sectors, including autonomous vehicles, healthcare, and finance. These industries require vast amounts of accurately labeled data to train their AI models, leading to a significant surge in the demand for efficient and scalable annotation tools. While precise market sizing for 2025 is unavailable, considering a conservative estimate and assuming a CAGR of 25% (a reasonable figure given industry growth), we can project a market value exceeding $2 billion in 2025, rising significantly over the forecast period (2025-2033). Key trends include the growing adoption of cloud-based solutions, increased automation in the annotation process through AI-assisted tools, and a heightened focus on data privacy and security. The rise of synthetic data generation is also beginning to impact the market, offering potential cost savings and improved data diversity. However, challenges remain. The high cost of skilled annotators, the need for continuous quality control, and the inherent complexities of labeling diverse data types (images, text, audio, video) pose significant restraints on market growth. While leading players like Labelbox, Scale AI, and SuperAnnotate dominate the market with advanced features and robust scalability, smaller companies and open-source tools continue to compete, often focusing on niche applications or offering cost-effective alternatives. The competitive landscape is dynamic, with continuous innovation and mergers and acquisitions shaping the future of this rapidly evolving market. Regional variations in adoption are also expected, with North America and Europe likely leading the market, followed by Asia-Pacific and other regions. This continuous evolution necessitates careful strategic planning and adaptation for businesses operating in or considering entry into this space.

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

The Open Source Data Annotation Tool market is rapidly evolving as businesses and researchers increasingly recognize the significance of high-quality, labeled data for training machine learning models. These tools facilitate the efficient tagging and classification of various data types, including images, text, and

Automated Ultrastructure Annotation Software Market Outlook

According to our latest research, the global automated ultrastructure annotation software market size reached USD 1.42 billion in 2024, demonstrating a robust momentum driven by the increasing demand for advanced digital pathology and high-throughput life sciences research. The market is projected to expand at a CAGR of 12.7% from 2025 to 2033, reaching an estimated USD 4.19 billion by 2033. This impressive growth is primarily fueled by rapid advancements in artificial intelligence (AI), machine learning (ML), and imaging technologies, which are enabling more precise, efficient, and scalable annotation of ultrastructural data across medical and research domains.

One of the primary growth factors for the automated ultrastructure annotation software market is the surging adoption of AI-powered solutions in medical imaging and life sciences research. As the volume and complexity of ultrastructural data generated by high-resolution imaging techniques such as electron microscopy continue to escalate, traditional manual annotation methods are becoming increasingly unsustainable. Automated annotation software leverages sophisticated algorithms to deliver accurate, reproducible, and rapid analyses, significantly reducing human error and enhancing throughput. This capability is particularly critical in clinical diagnostics, drug discovery, and disease research, where timely and reliable data interpretation can directly impact patient outcomes and research productivity.

Another significant driver is the growing integration of automated ultrastructure annotation software into drug discovery pipelines and translational research. Pharmaceutical and biotechnology companies are increasingly relying on these advanced tools to accelerate the identification and characterization of cellular and subcellular structures, enabling more efficient target validation and compound screening. By automating the annotation process, organizations can substantially shorten research timelines, reduce operational costs, and improve the reproducibility of experimental results. Furthermore, regulatory agencies are endorsing digital pathology and automated image analysis, fostering a supportive environment for the widespread adoption of these solutions in both preclinical and clinical settings.

The rising prevalence of chronic diseases and the ongoing digital transformation of healthcare systems globally are also contributing to the market's expansion. Hospitals, clinics, and research institutions are investing heavily in state-of-the-art imaging infrastructure and informatics platforms to enhance diagnostic accuracy and support personalized medicine initiatives. The integration of automated ultrastructure annotation software with electronic health records (EHRs) and laboratory information management systems (LIMS) is streamlining workflows, facilitating interdisciplinary collaboration, and supporting data-driven decision-making. As healthcare providers increasingly recognize the value of automated annotation in improving patient care and operational efficiency, the demand for these solutions is expected to surge in the coming years.

Regionally, North America continues to dominate the global automated ultrastructure annotation software market, accounting for the largest revenue share in 2024. This leadership can be attributed to the presence of leading technology vendors, well-established healthcare infrastructure, and significant investments in biomedical research. Europe and Asia Pacific are also witnessing substantial growth, driven by increasing research funding, expanding healthcare IT adoption, and rising awareness of the benefits of automated imaging analysis. The Asia Pacific region, in particular, is expected to exhibit the highest CAGR over the forecast period, supported by the rapid development of healthcare and research ecosystems in countries such as China, Japan, and India.

Component Analysis

The automated ultrastructure annotation software market is segmented by component into software and services. The software segment encompasses proprietary and open-source platforms designed to automate the annotation of ultrastructural images, leveraging AI, ML, and advanced image processing algorithms. These solutions are increasingly being adopted across medical, research, and industrial settings due to their ability to enhance accuracy, scalability, and efficiency. The software segment currently holds the largest share of the market, as org

Automated Image Annotation for Microscopy Market Outlook

According to our latest research, the global Automated Image Annotation for Microscopy market size reached USD 542.7 million in 2024, reflecting robust adoption across life sciences and healthcare research. The market is projected to expand at a CAGR of 18.2% from 2025 to 2033, with the total market value anticipated to reach USD 2,464.8 million by 2033. This remarkable growth is being driven by the increasing demand for high-throughput, accurate, and scalable image analysis solutions in medical diagnostics, pharmaceutical research, and academic settings.

The primary growth factor propelling the Automated Image Annotation for Microscopy market is the exponential rise in the volume and complexity of microscopy image data generated in life sciences research and clinical diagnostics. As advanced imaging modalities such as confocal, super-resolution, and electron microscopy become commonplace, researchers face mounting challenges in manually annotating vast datasets. Automated image annotation platforms, leveraging artificial intelligence and deep learning, provide significant efficiency gains by streamlining annotation workflows, minimizing human error, and enabling reproducible data labeling at scale. This technological leap is particularly critical in fields like cell biology, pathology, and neuroscience, where precise annotation is essential for downstream analysis, disease modeling, and biomarker discovery.

Another key driver is the growing integration of automated annotation tools into end-to-end digital pathology and drug discovery pipelines. Pharmaceutical and biotechnology companies are increasingly investing in automation to accelerate preclinical research, reduce time-to-market for new therapeutics, and enhance the reliability of high-content screening assays. Automated image annotation not only expedites the identification and classification of cellular structures but also supports quantitative analysis required for regulatory submissions and clinical trials. Furthermore, the rising adoption of cloud-based platforms is democratizing access to advanced annotation tools, enabling collaboration across geographically dispersed research teams and facilitating the aggregation of large annotated datasets for AI model training.

The market is also benefitting from significant advancements in machine learning algorithms, including semantic segmentation, instance segmentation, and object detection, which have dramatically improved annotation accuracy and versatility. These innovations are reducing the barriers for adoption among academic and research institutions, which often operate under tight resource constraints. Additionally, the increasing prevalence of open-source annotation frameworks and interoperability standards is fostering an ecosystem where automated annotation solutions can be seamlessly integrated with existing microscopy workflows. As a result, the Automated Image Annotation for Microscopy market is poised for sustained growth, with emerging applications in personalized medicine, digital pathology, and precision oncology further expanding its addressable market.

From a regional perspective, North America currently leads the global Automated Image Annotation for Microscopy market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is attributed to the high concentration of pharmaceutical companies, advanced healthcare infrastructure, and significant investments in AI-driven healthcare solutions. However, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by increasing R&D expenditure, expanding biotechnology sectors, and rising adoption of digital pathology solutions in countries such as China, Japan, and India. This regional diversification is expected to fuel market expansion and foster innovation in automated image annotation technologies worldwide.

Component Analysis

The Automated Image Annotation for

AdA Project Public Data Release

This repository holds public data provided by the AdA project (Affektrhetoriken des Audiovisuellen - BMBF eHumanities Research Group Audio-Visual Rhetorics of Affect).

See: http://www.ada.cinepoetics.fu-berlin.de/en/index.html The data is made accessible under the terms of the Creative Commons Attribution-ShareAlike 3.0 License. The data can be accessed also at the project's public GitHub repository: https://github.com/ProjectAdA/public

Further explanations of the data can be found on our AdA project website: https://projectada.github.io/. See also the peer-reviewed data paper for this dataset that is in review to be published in NECSUS_European Journal of Media Studies, and will be available from https://necsus-ejms.org/ and https://mediarep.org

The data currently includes:

AdA Filmontology

The latest public release of the AdA Filmontology: https://github.com/ProjectAdA/public/tree/master/ontology

A vocabulary of film-analytical terms and concepts for fine-grained semantic video annotation.

The vocabulary is also available online in our triplestore: https://ada.cinepoetics.org/resource/2021/05/19/eMAEXannotationMethod.html

Advene Annotation Template

The latest public release of the template for the Advene annotation software: https://github.com/ProjectAdA/public/tree/master/advene_template

The template provides the developed semantic vocabulary in the Advene software with ready-to-use annotation tracks and predefined values.

In order to use the template you have to install and use Advene: https://www.advene.org/

Annotation Data

The latest public releases of our annotation datasets based on the AdA vocabulary: https://github.com/ProjectAdA/public/tree/master/annotations

The dataset of news reports, documentaries and feature films on the topic of "financial crisis" contains more than 92.000 manual & semi-automatic annotations authored in the open-source-software Advene (Aubert/Prié 2005) by expert annotators as well as more than 400.000 automatically generated annotations for wider corpus exploration. The annotations are published as Linked Open Data under the CC BY-SA 3.0 licence and available as rdf triples in turtle exports (ttl files) and in Advene's non-proprietary azp-file format, which allows instant access through the graphical interface of the software.

The annotation data can also be queried at our public SPARQL Endpoint: http://ada.filmontology.org/sparql

• The dataset contains furthermore sample files for two different export capabilities of the web application AdA Annotation explorer: 1) all manual annotations of the type "field size" throughout the corpus as csv files. 2) static html exports of different queries conducted in the AdA Annotation Explorer.

Manuals

The data set includes different manuals and documentations in German and English: https://github.com/ProjectAdA/public/tree/master/manuals

"AdA Filmontology – Levels, Types, Values": an overview over all analytical concepts and their definitions.

"Manual: Annotating with Advene and the AdA Filmontology". A manual on the usage of Advene and the AdA Annotation Explorer that provides the basics for annotating audiovisual aesthetics and visualizing them.

"Notes on collaborative annotation with the AdA Filmontology"

This dataset consists of sentences extracted from BGS memoirs, DECC/OGA onshore hydrocarbons well reports and Mineral Reconnaissance Programme (MRP) reports. The sentences have been annotated to enable the dataset to be used as labelled training data for a Named Entity Recognition model and Entity Relation Extraction model, both of which are Natural Language Processing (NLP) techniques that assist with extracting structured data from unstructured text. The entities of interest are rock formations, geological ages, rock types, physical properties and locations, with inter-relations such as overlies, observedIn. The entity labels for rock formations and geological ages in the BGS memoirs were an extract from earlier published work https://github.com/BritishGeologicalSurvey/geo-ner-model https://zenodo.org/records/4181488 . The data can be used to fine tune a pre-trained large language model using transfer learning, to create a model that can be used in inference mode to automatically create the labels, thereby creating structured data useful for geological modelling and subsurface characterisation. The data is provided in JSONL(Relation) format which is the export format from doccano open source text annotation software (https://doccano.github.io/doccano/) used to create the labels. The source documents are already publicly available, but the MRP and DECC reports are only published in pdf image form. These latter documents had to undergo OCR and resulted in lower quality text and a lower quality training data. The majority of the labelled data is from the higher quality BGS memoirs text. The dataset is a proof of concept. Minimal peer review of the labelling has been conducted so this should not be treated as a gold standard labelled dataset, and it is of insufficient volume to build a performant model. The development of this training data and the text processing scripts were supported by a grant from UK Government Office for Technology Transfer (GOTT) Knowledge Asset Grant Fund Project 10083604

We provide annotated fish imagery data for use in deep learning models (e.g., convolutional neural networks) for individual and species recognition. For individual recognition models, the dataset consists of annotated .json files of individual brook trout imagery collected at the Eastern Ecological Science Center's Experimental Stream Laboratory. For species recognition models, the dataset consists of annotated .json files for 7 freshwater fish species: lake trout, largemouth bass, smallmouth bass, brook trout, rainbow trout, walleye, and northern pike. Species imagery was compiled from Anglers Atlas and modified to remove human faces for privacy protection. We used open-source VGG image annotation software developed by Oxford University: https://www.robots.ox.ac.uk/~vgg/software/via/via-1.0.6.html.

DESCRIPTION
For this task, we use a subset of the MIRFLICKR (http://mirflickr.liacs.nl) collection. The entire collection contains 1 million images from the social photo sharing website Flickr and was formed by downloading up to a thousand photos per day that were deemed to be the most interesting according to Flickr. All photos in this collection were released by their users under a Creative Commons license, allowing them to be freely used for research purposes. Of the entire collection, 25 thousand images were manually annotated with a limited number of concepts and many of these annotations have been further refined and expanded over the lifetime of the ImageCLEF photo annotation task. This year we used crowd sourcing to annotate all of these 25 thousand images with the concepts.

On this page we provide you with more information about the textual features, visual features and concept features we supply with each image in the collection we use for this year's task.

TEXTUAL FEATURES
All images are accompanied by the following textual features:

- Flickr user tags
These are the tags that the users assigned to the photos their uploaded to Flickr. The 'raw' tags are the original tags, while the 'clean' tags are those collapsed to lowercase and condensed to removed spaces.

- EXIF metadata
If available, the EXIF metadata contains information about the camera that took the photo and the parameters used. The 'raw' exif is the original camera data, while the 'clean' exif reduces the verbosity.

- User information and Creative Commons license information
This contains information about the user that took the photo and the license associated with it.

VISUAL FEATURES
Over the previous years of the photo annotation task we noticed that often the same types of visual features are used by the participants, in particular features based on interest points and bag-of-words are popular. To assist you we have extracted several features for you that you may want to use, so you can focus on the concept detection instead. We additionally give you some pointers to easy to use toolkits that will help you extract other features or the same features but with different default settings.

- SIFT, C-SIFT, RGB-SIFT, OPPONENT-SIFT
We used the ISIS Color Descriptors (http://www.colordescriptors.com) toolkit to extract these descriptors. This package provides you with many different types of features based on interest points, mostly using SIFT. It furthermore assists you with building codebooks for bag-of-words. The toolkit is available for Windows, Linux and Mac OS X.

- SURF
We used the OpenSURF (http://www.chrisevansdev.com/computer-vision-opensurf.html) toolkit to extract this descriptor. The open source code is available in C++, C#, Java and many more languages.

- TOP-SURF
We used the TOP-SURF (http://press.liacs.nl/researchdownloads/topsurf) toolkit to extract this descriptor, which represents images with SURF-based bag-of-words. The website provides codebooks of several different sizes that were created using a combination of images from the MIR-FLICKR collection and from the internet. The toolkit also offers the ability to create custom codebooks from your own image collection. The code is open source, written in C++ and available for Windows, Linux and Mac OS X.

- GIST
We used the LabelMe (http://labelme.csail.mit.edu) toolkit to extract this descriptor. The MATLAB-based library offers a comprehensive set of tools for annotating images.

For the interest point-based features above we used a Fast Hessian-based technique to detect the interest points in each image. This detector is built into the OpenSURF library. In comparison with the Hessian-Laplace technique built into the ColorDescriptors toolkit it detects fewer points, resulting in a considerably reduced memory footprint. We therefore also provide you with the interest point locations in each image that the Fast Hessian-based technique detected, so when you would like to recalculate some features you can use them as a starting point for the extraction. The ColorDescriptors toolkit for instance accepts these locations as a separate parameter. Please go to http://www.imageclef.org/2012/photo-flickr/descriptors for more information on the file format of the visual features and how you can extract them yourself if you want to change the default settings.

CONCEPT FEATURES
We have solicited the help of workers on the Amazon Mechanical Turk platform to perform the concept annotation for us. To ensure a high standard of annotation we used the CrowdFlower platform that acts as a quality control layer by removing the judgments of workers that fail to annotate properly. We reused several concepts of last year's task and for most of these we annotated the remaining photos of the MIRFLICKR-25K collection that had not yet been used before in the previous task; for some concepts we reannotated all 25,000 images to boost their quality. For the new concepts we naturally had to annotate all of the images.

- Concepts
For each concept we indicate in which images it is present. The 'raw' concepts contain the judgments of all annotators for each image, where a '1' means an annotator indicated the concept was present whereas a '0' means the concept was not present, while the 'clean' concepts only contain the images for which the majority of annotators indicated the concept was present. Some images in the raw data for which we reused last year's annotations only have one judgment for a concept, whereas the other images have between three and five judgments; the single judgment does not mean only one annotator looked at it, as it is the result of a majority vote amongst last year's annotators.

- Annotations
For each image we indicate which concepts are present, so this is the reverse version of the data above. The 'raw' annotations contain the average agreement of the annotators on the presence of each concept, while the 'clean' annotations only include those for which there was a majority agreement amongst the annotators.

You will notice that the annotations are not perfect. Especially when the concepts are more subjective or abstract, the annotators tend to disagree more with each other. The raw versions of the concept annotations should help you get an understanding of the exact judgments given by the annotators.

Annotation Management Platforms Market Outlook

According to our latest research, the global Annotation Management Platforms market size reached USD 1.27 billion in 2024, reflecting robust expansion driven by the accelerating adoption of artificial intelligence and machine learning across industries. The market is expected to grow at a CAGR of 18.6% during the forecast period from 2025 to 2033, reaching an estimated value of USD 6.15 billion by 2033. This impressive growth trajectory is powered by the increasing need for high-quality annotated datasets to train advanced AI models, the proliferation of data-driven applications, and the rising complexity of digital content management requirements across sectors.

The primary growth driver for the annotation management platforms market is the exponential surge in the deployment of AI and machine learning solutions across diverse industries such as healthcare, automotive, BFSI, and retail. As organizations intensify their focus on digital transformation, the demand for precise, scalable, and efficient data annotation tools has soared. Annotation management platforms streamline the process of labeling large volumes of unstructured data, enabling businesses to extract actionable insights and enhance the accuracy of AI algorithms. Moreover, the widespread integration of computer vision and natural language processing technologies in enterprise workflows further amplifies the need for comprehensive annotation solutions. The ongoing evolution of AI-powered applications, from autonomous vehicles to intelligent virtual assistants, underscores the critical role of annotation management in ensuring data quality and model reliability.

Another significant factor fueling market growth is the increasing emphasis on data privacy, security, and regulatory compliance. As organizations handle sensitive information, particularly in sectors like healthcare and finance, annotation management platforms offer robust governance features, audit trails, and customizable access controls. These capabilities are essential for meeting stringent regulatory standards such as GDPR, HIPAA, and CCPA. Furthermore, the rise of remote work and geographically dispersed teams has accelerated the adoption of cloud-based annotation solutions, allowing for seamless collaboration, scalability, and centralized management of annotation projects. The ability to integrate annotation platforms with existing enterprise systems, coupled with support for diverse data types—including text, image, audio, and video—enhances their value proposition and drives widespread adoption.

The market is also witnessing a surge in innovation, with vendors introducing advanced features such as automated annotation, AI-assisted labeling, and real-time quality assurance. These technological advancements not only reduce manual effort and operational costs but also improve annotation accuracy and consistency. The growing ecosystem of third-party integrations, APIs, and open-source frameworks is fostering greater flexibility and interoperability, enabling organizations to tailor annotation workflows to their specific requirements. Additionally, strategic partnerships between annotation platform providers and AI technology companies are facilitating the development of end-to-end data management solutions, further propelling market growth.

From a regional perspective, North America continues to dominate the annotation management platforms market, accounting for over 38% of global revenue in 2024. This leadership is attributed to the strong presence of technology giants, a mature digital infrastructure, and early adoption of AI-driven solutions. Asia Pacific, however, is emerging as the fastest-growing region, with a projected CAGR of 21.2% through 2033, driven by rapid digitalization, expanding IT investments, and the proliferation of start-ups in countries such as China, India, and Japan. Europe maintains a significant market share, supported by robust regulatory frameworks and the rising focus on data sovereignty. Latin America and the Middle East & Africa are also experiencing steady growth, fueled by increasing investments in digital transformation initiatives and the adoption of AI across key verticals.

Thousands of per- and polyfluoroalkyl substances (PFAS) exist in the environment and pose a potential health hazard. Suspect and nontarget screening with liquid chromatography (LC)–high-resolution tandem mass spectrometry (HRMS/MS) can be used for comprehensive characterization of PFAS. To date, no automated open source PFAS data analysis software exists to mine these extensive data sets. We introduce FluoroMatch, which automates file conversion, chromatographic peak picking, blank feature filtering, PFAS annotation based on precursor and fragment masses, and annotation ranking. The software library currently contains ∼7 000 PFAS fragmentation patterns based on rules derived from standards and literature, and the software automates a process for users to add additional compounds. The use of intelligent data-acquisition methods (iterative exclusion) nearly doubled the number of annotations. The software application is demonstrated by characterizing PFAS in landfill leachate as well as in leachate foam generated to concentrate the compounds for remediation purposes. FluoroMatch had wide coverage, returning 27 PFAS annotations for landfill leachate samples, explaining 71% of the all-ion fragmentation (CF2)n related fragments. By improving the throughput and coverage of PFAS annotation, FluoroMatch will accelerate the discovery of PFAS posing significant human risk.