The Data Masking Technologies Software market is experiencing robust growth, driven by increasing concerns about data privacy regulations like GDPR and CCPA, and the rising need for secure data sharing within and outside organizations. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $4.2 billion by 2033. This expansion is fueled by the surging adoption of cloud-based solutions, offering scalability and cost-effectiveness compared to on-premises deployments. Large enterprises are currently the largest segment, but growth is expected to be particularly strong within the small and medium-sized enterprise (SME) sectors as they increasingly adopt data masking to comply with regulations and protect sensitive customer information. Key trends shaping the market include the integration of artificial intelligence (AI) and machine learning (ML) for improved data masking accuracy and automation, and the increasing demand for solutions supporting diverse data formats and deployment models. However, challenges remain, including the complexity of implementing and managing data masking solutions, as well as potential performance impacts on data access and retrieval. The competitive landscape is characterized by a mix of established players like Microsoft, IBM, and Oracle, alongside specialized vendors focused on niche functionalities and specific industry needs. Geographic expansion is expected across all regions, with North America maintaining a significant market share, followed by Europe and Asia Pacific, driven by increasing digitalization and data-driven business strategies. The segment breakdown reveals a diverse market. Large enterprises lead in adoption, driven by stringent regulatory requirements and extensive internal data volumes. The SME segment presents significant growth potential, though challenges like budgetary constraints and limited in-house expertise may require tailored solutions and flexible pricing models. Cloud-based solutions dominate owing to their inherent flexibility and scalability, and the ability to manage growing data sets without extensive infrastructure investment. The preference for specific deployment models and solution types differs geographically; North America and Europe may show a greater preference for cloud-based solutions, while Asia Pacific might witness a slightly higher adoption rate for on-premises systems due to varying levels of internet penetration and security concerns. Ongoing technological innovation in data masking, including advanced techniques for synthetic data generation and enhanced data anonymization, promise to further accelerate market expansion in the coming years.

The Data Creation Tool market, currently valued at $7.233 billion (2025), is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 18.2% from 2025 to 2033. This significant expansion is driven by the increasing need for high-quality synthetic data across various sectors, including software development, machine learning, and data analytics. Businesses are increasingly adopting these tools to accelerate development cycles, improve data testing and validation processes, and enhance the training and performance of AI models. The rising demand for data privacy and regulatory compliance further fuels this growth, as synthetic data offers a viable alternative to real-world data while preserving sensitive information. Key players like Informatica, Broadcom (with its EDMS solutions), and Delphix are leveraging their established positions in data management to capture significant market share. Emerging players like Keymakr and Mostly AI are also contributing to innovation with specialized solutions focusing on specific aspects of data creation, such as realistic data generation and streamlined workflows. The market segmentation, while not explicitly provided, can be logically inferred. We can anticipate segments based on deployment (cloud, on-premise), data type (structured, unstructured), industry vertical (financial services, healthcare, retail), and functionality (data generation, data masking, data anonymization). Competitive dynamics are shaping the market with established players facing pressure from innovative startups. The forecast period of 2025-2033 indicates a substantial market expansion opportunity, influenced by factors like advancements in AI/ML technologies that demand massive datasets, and the growing adoption of Agile and DevOps methodologies in software development, both of which rely heavily on efficient data creation tools. Understanding specific regional breakdowns and further market segmentation is crucial for developing targeted business strategies and accurately assessing investment potential.

The Test Data Generation Tools market is experiencing robust growth, driven by the increasing demand for high-quality software and the rising adoption of agile and DevOps methodologies. The market's expansion is fueled by several factors, including the need for realistic and representative test data to ensure thorough software testing, the growing complexity of applications, and the increasing pressure to accelerate software delivery cycles. The market is segmented by type (Random, Pathwise, Goal, Intelligent) and application (Large Enterprises, SMEs), each demonstrating unique growth trajectories. Intelligent test data generation, offering advanced capabilities like data masking and synthetic data creation, is gaining significant traction, while large enterprises are leading the adoption due to their higher testing volumes and budgets. Geographically, North America and Europe currently hold the largest market shares, but the Asia-Pacific region is expected to witness significant growth due to rapid digitalization and increasing software development activities. Competitive intensity is high, with a mix of established players like IBM and Informatica and emerging innovative companies continuously introducing advanced features and functionalities. The market's growth is, however, constrained by challenges such as the complexity of implementing and managing test data generation tools and the need for specialized expertise. Overall, the market is projected to maintain a healthy growth rate throughout the forecast period (2025-2033), driven by continuous technological advancements and evolving software testing requirements. While the precise CAGR isn't provided, assuming a conservative yet realistic CAGR of 15% based on industry trends and the factors mentioned above, the market is poised for significant expansion. This growth will be fueled by the increasing adoption of cloud-based solutions, improved data masking techniques for enhanced security and privacy, and the rise of AI-powered test data generation tools that automatically create comprehensive and realistic datasets. The competitive landscape will continue to evolve, with mergers and acquisitions likely shaping the market structure. Furthermore, the focus on data privacy regulations will influence the development and adoption of advanced data anonymization and synthetic data generation techniques. The market will see further segmentation as specialized tools catering to specific industry needs (e.g., financial services, healthcare) emerge. The long-term outlook for the Test Data Generation Tools market remains positive, driven by the relentless demand for higher software quality and faster development cycles.

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Synthetic Data Generation Market Size 2025-2029

The synthetic data generation market size is forecast to increase by USD 4.39 billion, at a CAGR of 61.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the escalating demand for data privacy protection. With increasing concerns over data security and the potential risks associated with using real data, synthetic data is gaining traction as a viable alternative. Furthermore, the deployment of large language models is fueling market expansion, as these models can generate vast amounts of realistic and diverse data, reducing the reliance on real-world data sources. However, high costs associated with high-end generative models pose a challenge for market participants. These models require substantial computational resources and expertise to develop and implement effectively. Companies seeking to capitalize on market opportunities must navigate these challenges by investing in research and development to create more cost-effective solutions or partnering with specialists in the field. Overall, the market presents significant potential for innovation and growth, particularly in industries where data privacy is a priority and large language models can be effectively utilized.

What will be the Size of the Synthetic Data Generation 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 market continues to evolve, driven by the increasing demand for data-driven insights across various sectors. Data processing is a crucial aspect of this market, with a focus on ensuring data integrity, privacy, and security. Data privacy-preserving techniques, such as data masking and anonymization, are essential in maintaining confidentiality while enabling data sharing. Real-time data processing and data simulation are key applications of synthetic data, enabling predictive modeling and data consistency. Data management and workflow automation are integral components of synthetic data platforms, with cloud computing and model deployment facilitating scalability and flexibility. Data governance frameworks and compliance regulations play a significant role in ensuring data quality and security. Deep learning models, variational autoencoders (VAEs), and neural networks are essential tools for model training and optimization, while API integration and batch data processing streamline the data pipeline. Machine learning models and data visualization provide valuable insights, while edge computing enables data processing at the source. Data augmentation and data transformation are essential techniques for enhancing the quality and quantity of synthetic data. Data warehousing and data analytics provide a centralized platform for managing and deriving insights from large datasets. Synthetic data generation continues to unfold, with ongoing research and development in areas such as federated learning, homomorphic encryption, statistical modeling, and software development. The market's dynamic nature reflects the evolving needs of businesses and the continuous advancements in data technology.

How is this Synthetic Data Generation Industry segmented?

The synthetic data generation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHealthcare and life sciencesRetail and e-commerceTransportation and logisticsIT and telecommunicationBFSI and othersTypeAgent-based modellingDirect modellingApplicationAI and ML Model TrainingData privacySimulation and testingOthersProductTabular dataText dataImage and video dataOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period.In the rapidly evolving data landscape, the market is gaining significant traction, particularly in the healthcare and life sciences sector. With a growing emphasis on data-driven decision-making and stringent data privacy regulations, synthetic data has emerged as a viable alternative to real data for various applications. This includes data processing, data preprocessing, data cleaning, data labeling, data augmentation, and predictive modeling, among others. Medical imaging data, such as MRI scans and X-rays, are essential for diagnosis and treatment planning. However, sharing real patient data for research purposes or training machine learning algorithms can pose significant privacy risks. Synthetic data generation addresses this challenge by producing realistic medical imaging data, ensuring data privacy while enabling research and development. Moreover

The market for data masking software is growing steadily, with a global market size of USD 380.5 million in 2025 and a projected CAGR of 7.2% over the 2025-2033 forecast period. This growth is being driven by the increasing need to protect sensitive data from cyberattacks and data breaches, as well as the growing adoption of cloud-based data storage and processing. Major market drivers include the increasing volume of sensitive data being generated and stored, the growing number of data breaches, and the strict regulations governing the protection of personal and financial data. Trends in the market include the adoption of cloud-based data masking solutions, the use of artificial intelligence and machine learning to improve data masking accuracy, and the development of new data masking techniques. Restraints to market growth include the high cost of data masking software and the lack of skilled professionals to implement and manage data masking solutions. Market Research Reports

360-degree insight into the global Data Masking Software market.

Test Data Generation Tools Market Outlook

The global market size for Test Data Generation Tools was valued at USD 800 million in 2023 and is projected to reach USD 2.2 billion by 2032, growing at a CAGR of 12.1% during the forecast period. The surge in the adoption of agile and DevOps practices, along with the increasing complexity of software applications, is driving the growth of this market.

One of the primary growth factors for the Test Data Generation Tools market is the increasing need for high-quality test data in software development. As businesses shift towards more agile and DevOps methodologies, the demand for automated and efficient test data generation solutions has surged. These tools help in reducing the time required for test data creation, thereby accelerating the overall software development lifecycle. Additionally, the rise in digital transformation across various industries has necessitated the need for robust testing frameworks, further propelling the market growth.

The proliferation of big data and the growing emphasis on data privacy and security are also significant contributors to market expansion. With the introduction of stringent regulations like GDPR and CCPA, organizations are compelled to ensure that their test data is compliant with these laws. Test Data Generation Tools that offer features like data masking and data subsetting are increasingly being adopted to address these compliance requirements. Furthermore, the increasing instances of data breaches have underscored the importance of using synthetic data for testing purposes, thereby driving the demand for these tools.

Another critical growth factor is the technological advancements in artificial intelligence and machine learning. These technologies have revolutionized the field of test data generation by enabling the creation of more realistic and comprehensive test data sets. Machine learning algorithms can analyze large datasets to generate synthetic data that closely mimics real-world data, thus enhancing the effectiveness of software testing. This aspect has made AI and ML-powered test data generation tools highly sought after in the market.

Regional outlook for the Test Data Generation Tools market shows promising growth across various regions. North America is expected to hold the largest market share due to the early adoption of advanced technologies and the presence of major software companies. Europe is also anticipated to witness significant growth owing to strict regulatory requirements and increased focus on data security. The Asia Pacific region is projected to grow at the highest CAGR, driven by rapid industrialization and the growing IT sector in countries like India and China.

Synthetic Data Generation has emerged as a pivotal component in the realm of test data generation tools. This process involves creating artificial data that closely resembles real-world data, without compromising on privacy or security. The ability to generate synthetic data is particularly beneficial in scenarios where access to real data is restricted due to privacy concerns or regulatory constraints. By leveraging synthetic data, organizations can perform comprehensive testing without the risk of exposing sensitive information. This not only ensures compliance with data protection regulations but also enhances the overall quality and reliability of software applications. As the demand for privacy-compliant testing solutions grows, synthetic data generation is becoming an indispensable tool in the software development lifecycle.

Component Analysis

The Test Data Generation Tools market is segmented into software and services. The software segment is expected to dominate the market throughout the forecast period. This dominance can be attributed to the increasing adoption of automated testing tools and the growing need for robust test data management solutions. Software tools offer a wide range of functionalities, including data profiling, data masking, and data subsetting, which are essential for effective software testing. The continuous advancements in software capabilities also contribute to the growth of this segment.

In contrast, the services segment, although smaller in market share, is expected to grow at a substantial rate. Services include consulting, implementation, and support services, which are crucial for the successful deployment and management of test data generation tools. The increasing complexity of IT inf

Test Data Management Market size was valued at USD 1.54 Billion in 2024 and is projected to reach USD 2.97 Billion by 2032, growing at a CAGR of 11.19% from 2026 to 2032.

Test Data Management Market Drivers

Increasing Data Volumes: The exponential growth in data generated by businesses necessitates efficient management of test data. Effective TDM solutions help organizations handle large volumes of data, ensuring accurate and reliable testing processes.

Need for Regulatory Compliance: Stringent data privacy regulations, such as GDPR, HIPAA, and CCPA, require organizations to protect sensitive data. TDM solutions help ensure compliance by masking or anonymizing sensitive data used in testing environments.

We introduce a synthetic dataset for evaluating no-rigid 3D reconstruction using a moving RGB-D camera. The dataset consist of two subjects captured with four different camera trajectories. For each case we provide frame-by-frame ground truth geometry of the scene, the camera trajectory and foreground mask. This synthetic data was a part of paper "Non-rigid reconstruction with a single moving RGB-D camera" published at ICPR 2018. If you are using this dataset please cite the paper and this collection. More information can be found at the supporting documents.

Purpose and Features World's largest open source privacy dataset. The purpose of the dataset is to train models to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The example texts have 54 PII classes (types of sensitive data), targeting 229 discussion subjects / use cases split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...). Key facts: - Size: 13.6m text tokens in ~209k examples with 649k PII tokens (see summary.json) - 4 languages, more to come! - English - French - German - Italian - Synthetic data generated using proprietary algorithms - No privacy violations! - Human-in-the-loop validated high quality dataset # Getting started Option 1: Python terminal pip install datasets python from datasets import load_dataset dataset = load_dataset("ai4privacy/pii-masking-200k", data_files=["*.jsonl"]) # Token distribution across PII classes We have taken steps to balance the token distribution across PII classes covered by the dataset. This graph shows the distribution of observations across the different PII classes in this release: There is 1 class that is still overrepresented in the dataset: firstname. We will further improve the balance with future dataset releases. This is the token distribution excluding the FIRSTNAME class: # Compatible Machine Learning Tasks: - Tokenclassification. Check out a HuggingFace's guide on token classification. - ALBERT, BERT, BigBird, BioGpt, BLOOM, BROS, CamemBERT, CANINE, ConvBERT, Data2VecText, DeBERTa, DeBERTa-v2, DistilBERT, ELECTRA, ERNIE, ErnieM, ESM, Falcon, FlauBERT, FNet, Funnel Transformer, GPT-Sw3, OpenAI GPT-2, GPTBigCode, GPT Neo, GPT NeoX, I-BERT, LayoutLM, LayoutLMv2, LayoutLMv3, LiLT, Longformer, LUKE, MarkupLM, MEGA, Megatron-BERT, MobileBERT, MPNet, MPT, MRA, Nezha, Nyströmformer, QDQBert, RemBERT,...

Gretel Synthetic Domain-Specific Documents Dataset (English)

This dataset is a synthetically generated collection of documents enriched with Personally Identifiable Information (PII) and Protected Health Information (PHI) entities spanning multiple domains. Created using Gretel Navigator with mistral-nemo-2407 as the backend model, it is specifically designed for fine-tuning Gliner models. The dataset contains document passages featuring PII/PHI entities from a wide range of… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/gretel-pii-masking-en-v1.

This dataset contains all recorded and hand-annotated as well as all synthetically generated data as well as representative trained networks used for semantic and instance segmentation experiments in the replicAnt - generating annotated images of animals in complex environments using Unreal Engine manuscript. Unless stated otherwise, all 3D animal models used in the synthetically generated data have been generated with the open-source photgrammetry platform scAnt peerj.com/articles/11155/. All synthetic data has been generated with the associated replicAnt project available from https://github.com/evo-biomech/replicAnt.

Abstract:

Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.

Benchmark data

Semantic and instance segmentation is used only rarely in non-human animals, partially due to the laborious process of curating sufficiently large annotated datasets. replicAnt can produce pixel-perfect segmentation maps with minimal manual effort. In order to assess the quality of the segmentations inferred by networks trained with these maps, semi-quantitative verification was conducted using a set of macro-photographs of Leptoglossus zonatus (Dallas, 1852) and Leptoglossus phyllopus (Linnaeus, 1767), provided by Prof. Christine Miller (University of Florida), and Royal Tyler (Bugwood.org. For further qualitative assessment of instance segmentation, we used laboratory footage, and field photographs of Atta vollenweideri provided by Prof. Flavio Roces. More extensive quantitative validation was infeasible, due to the considerable effort involved in hand-annotating larger datasets on a per-pixel basis.

Synthetic data

We generated two synthetic datasets from a single 3D scanned Leptoglossus zonatus (Dallas, 1852) specimen: one using the default pipeline, and one with additional plant assets, spawned by three dedicated scatterers. The plant assets were taken from the Quixel library and include 20 grass and 11 fern and shrub assets. Two dedicated grass scatterers were configured to spawn between 10,000 and 100,000 instances; the fern and shrub scatterer spawned between 500 to 10,000 instances. A total of 10,000 samples were generated for each sub dataset, leading to a combined dataset comprising 20,000 image render and ID passes. The addition of plant assets was necessary, as many of the macro-photographs also contained truncated plant stems or similar fragments, which networks trained on the default data struggled to distinguish from insect body segments. The ability to simply supplement the asset library underlines one of the main strengths of replicAnt: training data can be tailored to specific use cases with minimal effort.

Funding

This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

About

The dataset contains synthetically generated images of people wearing glasses (regular eyeglasses + sunglasses) and glasses masks (full + frames + shadows). It can primarily be used for eyeglasses/sunglasses classification and segmentation.

This dataset is an augmented version of the synthetic dataset introduced in Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data and can be accessed here. The augmentation adds overlays on top of eyeglass frames to create images of people wearing sunglasses and corresponding masks.

Structure

There are 73 people identities in total, each with 400 different expressions or lightning effects, thus making a total of 29,000 samples. Each sample is a group of 8 images of the form sample-name-[suffix].png where [suffix] can be one of the following: * all - regular eyeglasses (i.e., frames) and their shadows * sunglasses - occluded glasses (i.e., sunglasses) and their frame shadows * glass - regular eyeglasses but no shadows * shadows - frame shadows but no eyeglasses * face - plain face: no glasses and no shadows * seg - mask for regular eyeglasses * sgseg - mask for sunglasses * shaseg - mask for frame shadows

10 identities were used for test data and 10 identities for validation, which corresponds to roughly 14% each, leaving around 72% of the data for training (which is 21,200 samples).

Collection

The data was generated in the following process: 1. The original dataset was downloaded from the link in the official Github repository 2. Glasses Detector was used to create full glasses segmentation masks which were used to generate various color and transparency (mainly dark) glasses 3. The generated glasses were overlaid on top of the original images with frames to create new images with sunglasses and corresponding masks 4. The 73 identities were shuffled and split into 3 parts (train, val, test) which were used to group all the 400 variations of each identity.

You can see the full process of glass overlay generation and data splitting in this gist.

Note: a type of noise (e.g., random, single spot) was added to roughly 15% of the images with sunglasses. Also, some of the generated glasses do not fill the entire frame, however, masks capture that.

Licence

This dataset is marked under CC BY-NC 4.0, meaning you can share and modify the data for non-commercial reuse as long as you provide a copyright notice.

Citation

Please use the original authors, i.e., the following citation:

@misc{glasses-segmentation-synthetic,
  author = {Junfeng Lyu, Zhibo Wang, Feng Xu},
  title = {Glasses Segmentation Synthetic Dataset},
  year = {2023},
  publisher = {Kaggle},
  journal = {Kaggle datasets},
  howpublished = {\url{https://www.kaggle.com/datasets/mantasu/glasses-segmentation-synthetic-dataset}}
}

Clinical letters contain sensitive information, limiting their use in model training, medical research, and education. This study aims to generate reliable, diverse, and de-identified synthetic clinical letters to support these tasks. We investigated multiple pre-trained language models for text masking and generation, focusing on Bio_ClinicalBERT, and applied different masking strategies. Evaluation included qualitative and quantitative assessments, downstream named entity recognition (NER) tasks, and clinically focused evaluations using BioGPT and GPT-3.5-turbo. The experiments show: (1) encoder-only models perform better than encoder–decoder models; (2) models trained on general corpora perform comparably to clinical-domain models if clinical entities are preserved; (3) preserving clinical entities and document structure aligns with the task objectives; (4) Masking strategies have a noticeable impact on the quality of synthetic clinical letters: masking stopwords has a positive impact, while masking nouns or verbs has a negative effect; (5) The BERTScore should be the primary quantitative evaluation metric, with other metrics serving as supplementary references; (6) Contextual information has only a limited effect on the models' understanding, suggesting that synthetic letters can effectively substitute real ones in downstream NER tasks; (7) Although the model occasionally generates hallucinated content, it appears to have little effect on overall clinical performance. Unlike previous research, which primarily focuses on reconstructing original letters by training language models, this paper provides a foundational framework for generating diverse, de-identified clinical letters. It offers a direction for utilizing the model to process real-world clinical letters, thereby helping to expand datasets in the clinical domain. Our codes and trained models are available at https://github.com/HECTA-UoM/Synthetic4Health.

The size of the Big Data Security Market was valued at USD 24.13 billion in 2023 and is projected to reach USD 73.73 billion by 2032, with an expected CAGR of 17.3 % during the forecast period.The Big Data Security Market is witnessing significant growth due to the increasing adoption of big data analytics across various industries and the rising threat of cyberattacks. Organizations are generating vast amounts of data, which necessitates robust security solutions to protect sensitive information from unauthorized access and breaches. Key drivers for market expansion include the growing reliance on cloud-based services, regulatory compliance requirements, and the proliferation of connected devices. Technologies such as encryption, data masking, and intrusion detection systems are gaining traction in this market. Additionally, sectors like banking, healthcare, and retail are investing heavily in big data security to safeguard customer information and maintain operational integrity. Challenges such as high implementation costs and complexities in integrating security solutions with existing systems persist, but advancements in artificial intelligence and machine learning are paving the way for innovative solutions. Geographically, North America dominates the market due to its technological advancements, while Asia-Pacific is emerging as a lucrative region due to increasing digital transformation and cybersecurity awareness. The Big Data Security Market is poised for substantial growth, driven by technological innovations and evolving security needs.Increased data breaches and cyberattacksStringent regulatory compliance requirementsAdvancements in data analytics and artificial intelligence (AI)Investments in data security infrastructure and solutions Key drivers for this market are: Market Segmentation: Component: Software, Services Deployment: On-premises, Cloud Enterprise Size: Large Enterprises, SMEs End-use: BFSI, Utilities, IT, Healthcare, Retail, Telecom Regional Insights: North America (35% Market Share) Europe (25% Market Share) Asia-Pacific (20% Market Share) Latin America (10% Market Share) Middle East & Africa (5% Market Share). Potential restraints include: Data Complexity and Volume: Managing and securing massive and diverse data sets poses challenges. Lack of Skilled Security Professionals: Shortages of skilled professionals impede effective security implementation. Budget Constraints: SMEs may face financial limitations in adopting advanced security solutions.. Notable trends are: Shift to Cloud-Based Security: Enterprises embrace cloud solutions for enhanced agility and cost efficiency. Emergence of AI and ML for Data Protection: Leveraging AI to detect and respond to security threats. Growing Adoption of Zero Trust Security: Implementing stringent security measures to minimize trust and reduce vulnerabilities. Focus on Data Privacy Regulations: Compliance with GDPR, CCPA, and other regulations drive data security investments..

Paper Accept by WACV 2024

[paper, supp] [arXiv]

Overview

Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages.

Description

This dataset has been designed for land cover mapping and building change detection tasks.

File Structure and Content:

1. 1024.zip:

   • Contains images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.
   • images and ss_mask folders: Used for the land cover mapping task.
   • images folder: Post-event images for building change detection.
   • small-pre-images: Images with a minor off-nadir angle difference compared to post-event images.
   • big-pre-images: Images with a large off-nadir angle difference compared to post-event images.
   • cd_mask: Ground truth for the building change detection task.

2. 512-1.zip, 512-2.zip, 512-3.zip:

   • Contains images of size 512x512 with a GSD of 0.3-0.6m.
   • images and ss_mask folders: Used for the land cover mapping task.
   • images folder: Post-event images for building change detection.
   • pre-event folder: Images for the pre-event phase.
   • cd-mask: Ground truth for building change detection.

Land Cover Mapping Class Grep Map:

class_grey = { "Bareland": 1, "Rangeland": 2, "Developed Space": 3, "Road": 4, "Tree": 5, "Water": 6, "Agriculture land": 7, "Building": 8, }

Reference

@misc{song2023syntheworld, title={SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection}, author={Jian Song and Hongruixuan Chen and Naoto Yokoya}, year={2023}, eprint={2309.01907}, archivePrefix={arXiv}, primaryClass={cs.CV} }

In recent years, the exploitation of three-dimensional (3D) data in deep learning has gained momentum despite its inherent challenges. The necessity of 3D approaches arises from the limitations of two-dimensional (2D) techniques when applied to 3D data due to the lack of global context. A critical task in medical and microscopy 3D image analysis is instance segmentation, which is inherently complex due to the need for accurately identifying and segmenting multiple object instances in an image. Here, we introduce a 3D adaptation of the Mask R-CNN, a powerful end-to-end network designed for instance segmentation. Our implementation adapts a widely used 2D TensorFlow Mask R-CNN by developing custom TensorFlow operations for 3D Non-Max Suppression and 3D Crop And Resize, facilitating efficient training and inference on 3D data. We validate our 3D Mask R-CNN on two experiences. The first experience uses a controlled environment of synthetic data with instances exhibiting a wide range of anisotropy and noise. Our model achieves good results while illustrating the limit of the 3D Mask R-CNN for the noisiest objects. Second, applying it to real-world data involving cell instance segmentation during the morphogenesis of the ascidian embryo Phallusia mammillata, we show that our 3D Mask R-CNN outperforms the state-of-the-art method, achieving high recall and precision scores. The model preserves cell connectivity, which is crucial for applications in quantitative study. Our implementation is open source, ensuring reproducibility and facilitating further research in 3D deep learning.

Welcome to the the CSAW-M dataset homepageThis page includes the files and metadata related to the CSAW-M, a curated dataset of mammograms with expert assessments of the masking of cancer. CSAW-M is collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image density as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We trained deep learning models on CSAW-M to estimate the masking level, and showed that the estimated masking is significantly more predictive of screening participants diagnosed with interval and large invasive cancers — without being explicitly trained for these tasks — than its breast density counterparts. Please find the paper corresponding to our work here and the GitHub repo here.CSAW-M Research Use LicensePlease read carefully all the terms and conditions of the CSAW-M Research Use License. How to access the dataset:If you want to get access to the data, please use the "Request access to files" option above (currently, non-Swedish researchers need to have a general figshare account to be able to to request access). We will ask you to agree to our terms of conditions and provide us with some information about what you will use the data for. We will then receive the request and process it, after which you would be able to download all the files.If you use this Work, please cite our paper:@article{sorkhei2021csaw, title={CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer}, author={Sorkhei, Moein and Liu, Yue and Azizpour, Hossein and Azavedo, Edward and Dembrower, Karin and Ntoula, Dimitra and Zouzos, Athanasios and Strand, Fredrik and Smith, Kevin}, year={2021} }

The “FruitSeg30_Segmentation Dataset & Mask Annotations” is a comprehensive collection of high-resolution images of various fruits, accompanied by precise segmentation masks. We structured this dataset into 30 distinct classes, which containing 1969 images and their corresponding masks, with each measuring 512×512 pixels. Each class folder contains two subfolders: “Images” with high-quality JPG images captured under diverse conditions and “Mask” with PNG files representing the segmentation masks. We meticulously collected the dataset from various locations in Malaysia, Bangladesh, and Australia, ensuring a robust and diverse collection suitable for training and evaluating image segmentation models like U-Net. This resource is ideal for automated fruit recognition and classification applications, agricultural quality control, and computer vision and image processing research. By providing precise annotations and a wide range of fruit types, this dataset serves as a valuable asset for advancing research and development in these fields.

Dataset Description for Chest X-rays (Montgomery and Shenzhen)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F7162438%2F8ca0d9e4c8d1853a14b9e20e6f600b97%2F_results_11_2.png?generation=1732001019088722&alt=media" alt=""> This dataset consists of 704 chest X-ray images that have been curated from two sources: the Montgomery County Chest X-ray Database (USA) and the Shenzhen Chest X-ray Database (China). The images are used for training and evaluating machine learning models for tuberculosis (TB) detection.

The dataset contains both tuberculosis-positive and normal chest X-rays, along with demographic details such as gender, age, and county of origin. The images are accompanied by lung segmentation masks and clinical metadata, which makes the dataset highly suitable for deep learning applications in medical imaging.

Dataset Overview

Data Sources

• Montgomery County Chest X-ray Database
• Shenzhen Chest X-ray Database

Dataset Stats

1. Total Images: 704 chest X-rays (from both Montgomery and Shenzhen).
2. County Distribution: - Shenzhen: 80% (563 images). - Montgomery: 20% (141 images).

3. PTB (Tuberculosis Cases): - PTB=1 (Tuberculosis Positive): 345 images. - PTB=0 (Normal): 359 images.

Clinical Data Breakdown:

The clinical metadata file includes the following columns: - id: Unique identifier for each image. - gender: Gender of the patient. - age: Age of the patient. - county: County of origin (Shenzhen or Montgomery). - ptb: Label indicating if the image shows tuberculosis (PTB=1) or is normal (PTB=0). - remarks: Additional clinical notes about the patient’s condition (e.g., "secondary PTB", "normal").

Organized Data Structure:

/datasets/
  /image/      # X-ray images
  /mask/      # Lung segmentation masks
  /MetaData.csv   # Clinical metadata

Next Steps and Recommendations

To improve the dataset and address some of the challenges: - Balance the dataset by using oversampling/undersampling techniques or generate synthetic data for underrepresented categories. - Increase data diversity: Consider adding more datasets from different regions and with different demographic distributions. - Use transfer learning for model training, leveraging models pretrained on larger datasets (e.g., ImageNet) to overcome the small dataset size.

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