The global healthcare data annotation tools market size reached USD 204.6 Million in 2024. Looking forward, IMARC Group expects the market to reach USD 1,308.5 Million by 2033, exhibiting a growth rate (CAGR) of 22.9% during 2025-2033. The increasing adoption of artificial intelligence (AI) and machine learning (ML) in healthcare, the rise in generating vast amounts of data, significant advancement in medical imaging technologies, and the increasing demand for telemedicine are some of the major factors propelling the market.

IMARC Group provides an analysis of the key trends in each segment of the global healthcare data annotation tools market report, along with forecasts at the global, regional, and country levels for 2025-2033. Our report has categorized the market based on type, technology, application, and end user.

The size and share of the market is categorized based on product (Image Ai-assisted Annotation Tools, Text Ai-assisted Annotation Tools, Video Ai-assisted Annotation Tools) and Application (Machine Learning, Computer Vision, Artificial Intelligence, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in machine learning and artificial intelligence applications. The market's expansion is fueled by several factors: the rising adoption of AI across various sectors (including IT, automotive, healthcare, and finance), the need for cost-effective data annotation solutions, and the inherent flexibility and customization offered by open-source tools. While cloud-based solutions currently dominate the market due to scalability and accessibility, on-premise deployments remain significant, particularly for organizations with stringent data security requirements. The market's growth is further propelled by advancements in automation and semi-supervised learning techniques within data labeling, leading to increased efficiency and reduced annotation costs. Geographic distribution shows a strong concentration in North America and Europe, reflecting the higher adoption of AI technologies in these regions; however, Asia-Pacific is emerging as a rapidly growing market due to increasing investment in AI and the availability of a large workforce for data annotation. Despite the promising outlook, certain challenges restrain market growth. The complexity of implementing and maintaining open-source tools, along with the need for specialized technical expertise, can pose barriers to entry for smaller organizations. Furthermore, the quality control and data governance aspects of open-source annotation require careful consideration. The potential for data bias and the need for robust validation processes necessitate a strategic approach to ensure data accuracy and reliability. Competition is intensifying with both established and emerging players vying for market share, forcing companies to focus on differentiation through innovation and specialized functionalities within their tools. The market is anticipated to maintain a healthy growth trajectory in the coming years, with increasing adoption across diverse sectors and geographical regions. The continued advancements in automation and the growing emphasis on data quality will be key drivers of future market expansion.

The data collection and labeling market is experiencing robust growth, fueled by the escalating demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033), reaching approximately $75 billion by 2033. This expansion is primarily driven by the increasing adoption of AI across diverse sectors, including healthcare (medical image analysis, drug discovery), automotive (autonomous driving systems), finance (fraud detection, risk assessment), and retail (personalized recommendations, inventory management). The rising complexity of AI models and the need for more diverse and nuanced datasets are significant contributing factors to this growth. Furthermore, advancements in data annotation tools and techniques, such as active learning and synthetic data generation, are streamlining the data labeling process and making it more cost-effective. However, challenges remain. Data privacy concerns and regulations like GDPR necessitate robust data security measures, adding to the cost and complexity of data collection and labeling. The shortage of skilled data annotators also hinders market growth, necessitating investments in training and upskilling programs. Despite these restraints, the market’s inherent potential, coupled with ongoing technological advancements and increased industry investments, ensures sustained expansion in the coming years. Geographic distribution shows strong concentration in North America and Europe initially, but Asia-Pacific is poised for rapid growth due to increasing AI adoption and the availability of a large workforce. This makes strategic partnerships and global expansion crucial for market players aiming for long-term success.

The Asia Pacific data annotation tools market is projected to exhibit a robust CAGR of 28.05% during the forecast period of 2025-2033. This growth is primarily driven by the surging demand for high-quality annotated data for training and developing artificial intelligence (AI) and machine learning (ML) algorithms. The increasing adoption of AI and ML across various industry verticals, such as healthcare, retail, and financial services, is fueling the need for accurate and reliable data annotation. Key trends influencing the market growth include the rise of self-supervised annotation techniques, advancements in natural language processing (NLP), and the proliferation of cloud-based annotation platforms. Additionally, the growing awareness of the importance of data privacy and security is driving the adoption of annotation tools that comply with industry regulations. The competitive landscape features a mix of established players and emerging startups offering a wide range of annotation tools. The Asia Pacific data annotation tools market is projected to grow from USD 2.4 billion in 2022 to USD 10.5 billion by 2027, at a CAGR of 35.4% during the forecast period. The growth of the market is attributed to the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies, which require large amounts of annotated data for training and development.

Market Analysis for Text Annotation Tool The global market for text annotation tools is projected to grow significantly, reaching XXX million USD by 2033, exhibiting a CAGR of XX% from 2025 to 2033. Key drivers behind this growth include the increasing demand for accurate data labeling for machine learning and natural language processing applications, the rise of cloud computing and AI-driven automation, and the expanding need for data annotation in various sectors such as healthcare, finance, and research. The market is segmented by application (commercial use, personal use), type (text annotation tool, image annotation tool, others), company (CloudApp, iMerit, Playment, Trilldata Technologies, Amazon Web Services, and others), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). North America currently holds the largest market share, followed by Europe and Asia Pacific. The increasing adoption of text annotation tools by enterprises and government agencies is expected to drive growth in the commercial use segment, while the demand for personal annotation tools for research and academic purposes is expected to fuel growth in the personal use segment.

Video Annotation Services Market Analysis The global video annotation services market size was valued at USD 475.6 million in 2025 and is projected to reach USD 843.2 million by 2033, exhibiting a compound annual growth rate (CAGR) of 7.4% over the forecast period. The increasing demand for video data in various industries such as healthcare, transportation, retail, and entertainment, coupled with the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies, is driving the market growth. Moreover, the emergence of new annotation techniques and the increasing adoption of cloud-based annotation solutions are further contributing to the market expansion. Key market trends include the integration of AI and ML capabilities to enhance annotation accuracy and efficiency, the increasing adoption of remote and hybrid work models leading to the demand for automated video annotation tools, and the focus on ethical and responsible data annotation practices to ensure data privacy and protection. Major companies operating in the market include Acclivis, Ai-workspace, GTS, HabileData, iMerit, Keymakr, LXT, Mindy Support, Sama, Shaip, SunTec, TaskUs, Tasq, and Triyock. North America holds a dominant share in the market, followed by Europe and Asia Pacific.

The global data annotation and collection services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $75 billion by 2033. This significant expansion is fueled by several key factors. The burgeoning autonomous driving industry necessitates vast amounts of annotated data for training self-driving systems, significantly contributing to market growth. Similarly, the healthcare sector's increasing reliance on AI for diagnostics and personalized medicine creates a substantial demand for high-quality annotated medical images and data. Other key application areas like smart security (surveillance, facial recognition), financial risk control (fraud detection), and social media (content moderation) are also driving substantial demand. The market is segmented by annotation type (image, text, voice, video) and application, with image annotation currently holding the largest market share due to its wide applicability across various sectors. However, the growing importance of natural language processing and speech recognition is expected to fuel significant growth in text and voice annotation segments in the coming years. While data privacy concerns and the need for high-quality data annotation present certain restraints, the overall market outlook remains extremely positive. The competitive landscape is characterized by a mix of large established players like Appen, Amazon (through AWS), and Google (through Google Cloud), along with numerous smaller, specialized companies. These companies are constantly innovating to improve the accuracy, efficiency, and scalability of their annotation services. Geographic distribution shows a strong concentration in North America and Europe, reflecting the high adoption of AI in these regions. However, Asia-Pacific, particularly China and India, are witnessing rapid growth, driven by increasing investment in AI and the availability of large datasets. The future of the market will likely be shaped by advancements in automation technologies, the development of more sophisticated annotation tools, and the increasing focus on data quality and ethical considerations. The continued expansion of AI across various industries ensures the long-term viability and growth trajectory of the data annotation and collection services market.

The India Data Annotation Tools Market is positioned for significant growth, currently valued at USD 85 million. This market expansion is largely driven by the rise in artificial intelligence (AI) and machine learning (ML) applications across various industries, such as healthcare, automotive, and retail, where large volumes of labeled data are essential.

The global data annotation platform market is experiencing robust growth, driven by the increasing demand for high-quality training data across diverse sectors. The market's expansion is fueled by the proliferation of artificial intelligence (AI) and machine learning (ML) applications in autonomous driving, smart healthcare, and financial risk control. Autonomous vehicles, for instance, require vast amounts of annotated data for object recognition and navigation, significantly boosting demand. Similarly, the healthcare sector leverages data annotation for medical image analysis, leading to advancements in diagnostics and treatment. The market is segmented by application (Autonomous Driving, Smart Healthcare, Smart Security, Financial Risk Control, Social Media, Others) and annotation type (Image, Text, Voice, Video, Others). The prevalent use of cloud-based platforms, coupled with the rising adoption of AI across various industries, presents significant opportunities for market expansion. While the market faces challenges such as high annotation costs and data privacy concerns, the overall growth trajectory remains positive, with a projected compound annual growth rate (CAGR) suggesting substantial market expansion over the forecast period (2025-2033). Competition among established players like Appen, Amazon, and Google, alongside emerging players focusing on specialized annotation needs, is expected to intensify. The regional distribution of the market reflects the concentration of AI and technology development in specific geographical regions. North America and Europe currently hold a significant market share due to their robust technological infrastructure and early adoption of AI technologies. However, the Asia-Pacific region, particularly China and India, is demonstrating rapid growth potential due to the burgeoning AI industry and expanding digital economy. This signifies a shift in market dynamics, as the demand for data annotation services increases globally, leading to a more geographically diverse market landscape. Continuous advancements in annotation techniques, including the use of automated tools and crowdsourcing, are expected to reduce costs and improve efficiency, further fueling market growth.

Overview

This dataset contains 8,992 images of Uno cards and 26,976 labeled examples on various textured backgrounds.

This dataset was collected, processed, and released by Roboflow user Adam Crawshaw, released with a modified MIT license: https://firstdonoharm.dev/

https://i.imgur.com/P8jIKjb.jpg" alt="Image example">

Use Cases

Adam used this dataset to create an auto-scoring Uno application:

Getting Started

Fork or download this dataset and follow our How to train state of the art object detector YOLOv4 for more.

Annotation Guide

See here for how to use the CVAT annotation tool.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

1. Overview This dataset is a collection of 5,000+ images of vehicle number plate position that are ready to use for optimizing the accuracy of computer vision models. All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organisations to enable their creative and machine learning projects.

2. Use case The 5,000+ images of vehicle number plate position could be used for various AI & Computer Vision models: Number Plate Recognition, Parking System, Surveillance Camera,... Each data set is supported by both AI and human review process to ensure labelling consistency and accuracy. Contact us for more custom datasets.

3. Annotation Annotation is available for this dataset on demand, including:

4. Bounding box

5. Classification

6. Segmentation ...

7. About PIXTA PIXTASTOCK is the largest Asian-featured stock platform providing data, contents, tools and services since 2005. PIXTA experiences 15 years of integrating advanced AI technology in managing, curating, processing over 100M visual materials and serving global leading brands for their creative and data demands. Visit us at https://www.pixta.ai/ or contact via our email contact@pixta.ai.

The coral reef benthic community data described here result from the automated annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). SCUBA divers conducted benthic photoquadrat surveys in coral reef habitats according to protocols established by ESD and NCRMP during the ESD-led NCRMP mission to the islands and atolls of the Pacific Remote Island Areas (PRIA) and American Samoa from June 8 to August 11, 2018. Still photographs were collected with a high-resolution digital camera mounted on a pole to document the benthic community composition at predetermined points along transects at stratified random sites surveyed only once as part of Rapid Ecological Assessment (REA) surveys for corals and fish and permanent sites established by ESD and resurveyed every ~3 years for climate change monitoring. Overall, 30 photoquadrat images were collected at each survey site. The benthic habitat images were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015). Ten points were randomly overlaid on each image and the machine-learning algorithm "robot" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). These benthic data can ultimately be used to produce estimates of community composition, relative abundance (percentage of benthic cover), and frequency of occurrence.

Overview

This dataset contains 74 images of aerial maritime photographs taken with via a Mavic Air 2 drone and 1,151 bounding boxes, consisting of docks, boats, lifts, jetskis, and cars. This is a multi class problem. This is an aerial object detection dataset. This is a maritime object detection dataset.

The drone was flown at 400 ft. No drones were harmed in the making of this dataset.

This dataset was collected and annotated by the Roboflow team, released with MIT license.

https://i.imgur.com/9ZYLQSO.jpg" alt="Image example">

Use Cases

• Identify number of boats on the water over a lake via quadcopter.
• Boat object detection dataset
• Aerial Object Detection proof of concept
• Identify if boat lifts have been taken out via a drone
• Identify cars with a UAV drone
• Find which lakes are inhabited and to which degree.
• Identify if visitors are visiting the lake house via quad copter.
• Proof of concept for UAV imagery project
• Proof of concept for maritime project
• Etc.

This dataset is a great starter dataset for building an aerial object detection model with your drone.

Getting Started

Fork or download this dataset and follow our How to train state of the art object detector YOLOv4 for more. Stay tuned for particular tutorials on how to teach your UAV drone how to see and comprable airplane imagery and airplane footage.

Annotation Guide

See here for how to use the CVAT annotation tool that was used to create this dataset.

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless. :fa-spacer: Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Training dataset

BWILD is a dataset tailored to train Artificial Intelligence applications to automate beach seagrass wrack detection in RGB images. It includes oblique RGB images captured by SIRENA beach video-monitoring systems, along with corresponding annotations, auxiliary data and a README file. BWILD encompasses data from two microtidal sandy beaches in the Balearic Islands, Spain. The dataset consists of images with varying fields of view (9 cameras), beach wrack abundance, degrees of occupation, and diverse meteoceanic and lighting conditions. The annotations categorise image pixels into five classes: i) Landwards, ii) Seawards, iii) Diffuse wrack, iv) Intermediate wrack, and v) Dense wrack.

Technical details

The BWILD version 1.1.0 is packaged in a compressed file (BWILD_v1.1.0.zip). A total of 3286 RGB images are shared in PNG format, corresponding annotations and masks in various formats (PNG, XML, JSON,TXT), and the README file in PDF format.

Data preprocessing

The BWILD dataset utilizes snapshot images from two SIRENA beach video-monitoring systems. To facilitate annotation while maintaining a diverse range of scenarios, the original 1280x960 pixel images were cropped to smaller regions, with a uniform resolution of 640x480 pixels. A subset of images was carefully curated to minimize annotation workload while ensuring representation of various time periods, distances to camera, and environmental conditions. Image selection involved filtering for quality, clustering for diversity, and prioritizing scenes containing beach seagrass wracks. Further details are available in the README file.

Data splitting

Data splitting requirements may vary depending on the chosen Artificial Intelligence approach (e.g., splitting by entire images or by image patches). Researchers should use a consistent method and document the approach and splits used in publications, enabling reproducible results and facilitating comparisons between studies.

Classes, labels and annotations

The BWILD dataset has been labelled manually using the 'Computer Vision Annotation Tool' (CVAT), categorising pixels into five labels of interest using polygon annotations.

Annotations were exported from CVAT in four different formats: (i) CVAT for images (XML); (ii) Segmentation Mask 1.0 (PNG); (iii) COCO (JSON); (iv) Ultralytics YOLO Segmentation 1.0 (TXT). These diverse annotation formats can be used for various applications including object detection and segmentation, and simplify the interaction with the dataset, making it more user-friendly. Further details are available in the README file.

Parameters

RGB values or any transformation in the colour space can be used as parameters.

Data sources

A SIRENA system consists of a set of RGB cameras mounted at the top of buildings on the beachfront. These cameras take oblique pictures of the beach, with overlapping sights, at 7.5 FPS during the first 10 minutes of each hour in daylight hours. From these pictures, different products are generated, including snapshots, which correspond to the frame of the video at the 5th minute. In the Balearic Islands, SIRENA stations are managed by the Balearic Islands Coastal Observing and Forecasting System (SOCIB), and are mounted at the top of hotels located in front of the coastline. The present dataset includes snapshots from the SIRENA systems operating since 2011 at Cala Millor (5 cameras) and Son Bou (4 cameras) beaches, located in Mallorca and Menorca islands (Balearic Islands, Spain), respectively. All latest and historical SIRENA images are available at the Beamon app viewer (https://apps.socib.es/beamon).

Data quality

All images included in BWILD have been supervised by the authors of the dataset. However, variable presence of beach segrass wracks across different beach segments and seasons impose a variable distribution of images across different SIRENA stations and cameras. Users of BWILD dataset must be aware of this variance. Further details are available in the README file.

Image resolution

The resolution of the images in BWILD is of 640x480 pixels.

Spatial coverage

The BWILD version 1.1.0 contains data from two SIRENA beach video-monitoring stations, encompassing two microtidal sandy beaches in the Balearic Islands, Spain. These are: Cala Millor (clm) and Son Bou (snb).

Contact information

For further technical inquiries or additional information about the annotated dataset, please contact jsoriano@socib.es.

75 TBPP CXR Annotations for Sensitive TB versus Qure.ai Binary Prediction, Crosstabulation Summary Statistics.

Methods

Cotton plants were grown in a well-controlled greenhouse in the NC State Phytotron as described previously (Pierce et al, 2019). Flowers were tagged on the day of anthesis and harvested three days post anthesis (3 DPA). The distinct fiber shapes had already formed by 2 DPA (Stiff and Haigler, 2016; Graham and Haigler, 2021), and fibers were still relatively short at 3 DPA, which facilitated the visualization of multiple fiber tips in one image.

Cotton fiber sample preparation, digital image collection, and image analysis:

Ovules with attached fiber were fixed in the greenhouse. The fixative previously used (Histochoice) (Stiff and Haigler, 2016; Pierce et al., 2019; Graham and Haigler, 2021) is obsolete, which led to testing and validation of another low-toxicity, formalin-free fixative (#A5472; Sigma-Aldrich, St. Louis, MO; Fig. S1). The boll wall was removed without damaging the ovules. (Using a razor blade, cut away the top 3 mm of the boll. Make about 1 mm deep longitudinal incisions between the locule walls, and finally cut around the base of the boll.) All of the ovules with attached fiber were lifted out of the locules and fixed (1 h, RT, 1:10 tissue:fixative ratio) prior to optional storage at 4°C. Immediately before imaging, ovules were examined under a stereo microscope (incident light, black background, 31X) to select three vigorous ovules from each boll while avoiding drying. Ovules were rinsed (3 x 5 min) in buffer [0.05 M PIPES, 12 mM EGTA. 5 mM EDTA and 0.1% (w/v) Tween 80, pH 6.8], which had lower osmolarity than a microtubule-stabilizing buffer used previously for aldehyde-fixed fibers (Seagull, 1990; Graham and Haigler, 2021). While steadying an ovule with forceps, one to three small pieces of its chalazal end with attached fibers were dissected away using a small knife (#10055-12; Fine Science Tools, Foster City, CA). Each ovule piece was placed in a single well of a 24-well slide (#63430-04; Electron Microscopy Sciences, Hatfield, PA) containing a single drop of buffer prior to applying and sealing a 24 x 60 mm coverslip with vaseline.

Samples were imaged with brightfield optics and default settings for the 2.83 mega-pixel, color, CCD camera of the Keyence BZ-X810 imaging system (www.keyence.com; housed in the Cellular and Molecular Imaging Facility of NC State). The location of each sample in the 24-well slides was identified visually using a 2X objective and mapped using the navigation function of the integrated Keyence software. Using the 10X objective lens (plan-apochromatic; NA 0.45) and 60% closed condenser aperture setting, a region with many fiber apices was selected for imaging using the multi-point and z-stack capture functions. The precise location was recorded by the software prior to visual setting of the limits of the z-plane range (1.2 µm step size). Typically, three 24-sample slides (representing three accessions) were set up in parallel prior to automatic image capture. The captured z-stacks for each sample were processed into one two-dimensional image using the full-focus function of the software. (Occasional samples contained too much debris for computer vision to be effective, and these were reimaged.)

• Resource Title: Deltapine 90 - Manually Annotated Training Set.

  File Name: GH3 DP90 Keyence 1_45 JPEG.zip

  Resource Description: These images were manually annotated in Labelbox.




• Resource Title: Deltapine 90 - AI-Assisted Annotated Training Set.

  File Name: GH3 DP90 Keyence 46_101 JPEG.zip

  Resource Description: These images were AI-labeled in RoboFlow and then manually reviewed in RoboFlow.




• Resource Title: Deltapine 90 - Manually Annotated Training-Validation Set.

  File Name: GH3 DP90 Keyence 102_125 JPEG.zip

  Resource Description: These images were manually labeled in LabelBox, and then used for training-validation for the machine learning model.




• Resource Title: Phytogen 800 - Evaluation Test Images.

  File Name: Gb cv Phytogen 800.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Pima 3-79 - Evaluation Test Images.

  File Name: Gb cv Pima 379.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Pima S-7 - Evaluation Test Images.

  File Name: Gb cv Pima S7.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Coker 312 - Evaluation Test Images.

  File Name: Gh cv Coker 312.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Deltapine 90 - Evaluation Test Images.

  File Name: Gh cv Deltapine 90.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Half and Half - Evaluation Test Images.

  File Name: Gh cv Half and Half.zip

  Resource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.




• Resource Title: Fiber Tip Annotations - Manual.

  File Name: manual_annotations.coco_.json

  Resource Description: Annotations in COCO.json format for fibers. Manually annotated in Labelbox.




• Resource Title: Fiber Tip Annotations - AI-Assisted.

  File Name: ai_assisted_annotations.coco_.json

  Resource Description: Annotations in COCO.json format for fibers. AI annotated with human review in Roboflow.




• Resource Title: Model Weights (iteration 600).

  File Name: model_weights.zip

  Resource Description: The final model, provided as a zipped Pytorch .pth file. It was chosen at training iteration 600. The model weights can be imported for use of the fiber tip type detection neural network in Python.

  Resource Software Recommended: Google Colab,url: https://research.google.com/colaboratory/

“Mobile mapping data” or “geospatial videos”, as a technology that combines GPS data with videos, were collected from the windshield of vehicles with Android Smartphones. Nearly 7,000 videos with an average length of 70 seconds were recorded in 2019. The smartphones collected sensor data (longitude and latitude, accuracy, speed and bearing) approximately every second during the video recording. Based on the geospatial videos, we manually identified and labeled about 10,000 parking violations in data with the help of an annotation tool. For this purpose, we defined six categorical variables (see PDF). Besides parking violations, we included street features like street category, type of bicycle infrastructure, and direction of parking spaces. An example for a street category is the collector street, which is an access street with primary residential use as well as individual shops and community facilities. Obviously, the labeling is a step that can (partly) be done automatically with image recognition in the future if the labeled data is used as a training dataset for a machine learning model. https://www.bmvi.de/SharedDocs/DE/Artikel/DG/mfund-projekte/parkright.html https://parkright.bliq.ai