What is Data Annotation?
Data annotation is the essential process of labelling raw data – images, videos, text, or audio – to transform it into structured, machine-readable information. Adding meaningful tags and labels ensures that AI models are trained to understand and interpret data accurately, driving precise predictions and smarter decisions. High-quality data annotation is the key to unleashing the full potential of AI.
Companies that trust us
Why Use Aya for Your Data Annotation Project
-
-
Exceptional Customer EXPERIENCE
- 1. Clear, timely project communication
- 2. Value-focused annotation engagement
- 3. Flexible timeframes for efficient delivery
- 4. Transparent pricing and policies
- 5. Dedicated annotation project support team
-
-
-
Unwavering QUALITY
- 1. Custom annotation quality control measures
- 2. Innovative solutions for complex data
- 3. Swift issue resolution processes
- 4. Follow data labelling ethical best practices
- 5. ISO, GDPR, SOC2 compliant standards
-
-
-
Unparalleled EXPERTISE
- 1. Specialised annotation domain knowledge
- 2.Broad external expert partnership network
- 3.Cutting-edge annotation technology implementation
- 4. Continuous annotator skill development programs
- 5.Proven track record of annotation success
-
Data Annotation Services
Industries We Serve
Aya Data provides high-quality data annotation services across
a range of industries and use cases.
- Aerial/geospatial
- Agriculture
- Automotive
- Finance/Insurance
- HealthCare
- Robotics
- Security and Survelliance
- Telecommunications
- Utilities and Infrastructure
...and many more
Data Annotation Process
What Our Clients
Say About Us
Data Annotation Case Study
How Drones and AI boosted Reforestation Efficiency and Reduced Risk
Oko Environmental partnered with Aya Data to enhance reforestation projects using advanced technology. We combined drone imagery, custom AI models, and an interactive dashboard to track tree growth, germination rates, and environmental risks. Germination rates increased from 72% to 94%, resource allocation improved, and data processing became more efficient. Learn more about how Aya Data's AI model significantly boosted germination rates, and increased efficiency and decision making.
Detecting Environmental Changes with High-Resolution Satellite Images
A U.S. company partnered with Aya Data to develop a model for detecting environmental changes using satellite imagery. We classified land use in 2,000 high-resolution images of South America spanning years. Our team meticulously labeled diverse land features, enabling a custom computer vision model that accurately detects land-use changes, contributing to sustainable farming research. Learn more about how Aya's team enabled accurate land-use change detection.
Precision Annotation on Road and Infrastructure Damage
A leading utility company partnered with Aya Data to revolutionise infrastructure maintenance using computer vision. We annotated 250,000 road images to detect and classify cracks accurately. Facing challenges of increased accuracy demands and large data volumes, Aya implemented enhanced quality control processes and scaled our annotation platform. This resulted in a highly accurate model that improved road safety and maintenance efficiency. Learn more about how Aya's solution increased efficiency and road safety.
Preparing Self-Driving Cars for the Rise of E-Scooters
As e-scooters transform urban transportation, self-driving car systems face new challenges. A leading AI company partnered with Aya Data to address the lack of comprehensive e-scooter data in existing datasets. Aya's experts meticulously labeled 10,000 diverse images of e-scooters, providing crucial training data for autonomous vehicle models. This collaboration resulted in a significant improvement in real-time e-scooter detection accuracy, reaching 95%. Learn how Aya enabled a client's AV models to accurately detect and classify e-scooters.
Real-Time Shoplifting Detection with Advanced CCTV Analytics
Aya Data partnered with a leading CCTV analytics provider to combat shoplifting. We labeled 5,000 hours of challenging footage, identifying individuals and theft events. Overcoming poor data quality, our team of 100+ annotators processed complex scenarios, enabling the client to train a custom computer vision model for real-time shoplifting detection. Learn more about Aya’s real-time system for detecting theft and warning behaviours.
Real-Time Transcription of American Police Radio Communications
A tech firm developed a real-time crime alert app needing accurate police radio transcription. Facing challenges with audio quality and colloquial language, they partnered with Aya Data. Our Ghanaian team, fluent in English, outperformed other vendors by 18 times. Over 18 months, Aya's experts accurately transcribed and classified police calls, enabling the app's successful deployment. Learn more about how Aya's team transcribed 500,000+ calls in 18 months.
Securely Procuring and Annotating Large Volumes of 3D DICOM Medical Data
A European MedTech company partnered with Aya Data to develop AI models for studying blood vessels affected by invasive surgery. Facing data collection challenges, Aya collaborated with the University of Ghana Medical Centre to ethically procure anonymized 3D vascular scans. Our medical specialists provided high-quality annotations, matching or exceeding European standards at a lower cost. Learn more about how Aya ethically procured scans.
Simplifying Vehicle Damage Verification for Faster Insurance Claims
An African insurance company aimed to revolutionize claims processing with technology. They needed 85,000 labeled images of damaged vehicles for a computer vision model. Aya Data's teams in Ghana and Sierra Leone collected and annotated the images. Using a custom pre-labeling model, Aya accelerated the process, enabling early project completion. Learn how Aya's computer vision model facilitated rapid detection and classification.
Turning Soil Data into Sales Success
Demeter, a West African organic fertilizer supplier, partnered with Aya Data to leverage soil acidity data for enhancing sales strategies. We developed a user-friendly dashboard integrating soil, customer, and sales data. This interactive tool visualised relationships between soil conditions and product performance, resulting in a 48% sales increase in one year. It improved customer engagement and decision-making, optimising Demeter's approach to serving small farmers with organic fertilizers. Learn more about how Aya's custom dashboard optimised sales, creating significant growth.
Data Annotation Blog Post
What Is Audio Transcription and How Does It Relate to Data Labeling?
Audio transcription is the process of converting unstructured audio data, such as recordings of human speech, into structured data. Artificial intelligence (AI) and machine learning (ML) algorithms require structured data to perform various tasks involving human speech, including speech recognition, sentiment analysis, and speaker identification. In short, audio transcription is fundamental for teaching computers to understand spoken language.
Bounding Boxes in Computer Vision: Uses, Best Practices for Labeling, and More
Bounding boxes are rectangular region labels used for computer vision (CV) tasks. In supervised machine learning (ML), an object detection model uses bounding box labels to learn about the contents of an image. The bounding box labels objects or features of interest to the model, whether a person, traffic sign, vehicle, or virtually anything else.
What Is Polygon Annotation and How Does it Work?
Polygon annotation is an essential labeling technique for supervised computer vision (CV). Objects are labeled with polygon annotations to create a dataset, which is fed into a supervised CV model. The model learns from the annotations, enabling it to predict and classify objects when exposed to new, unseen data. The physical environment primarily consists of complex shapes with non-linear edges – polygon annotation is considerably more effective at labeling them when compared to bounding boxes, which include a lot of useless information.
What is Video Annotation for AI?
Computer vision (CV) was formerly focused on identifying and classifying information from still images but has now evolved to respond to complex video data. Video annotation has emerged as a critical component for developing AI applications that understand and respond to visual data in motion. In this article, we will explore the concept of video annotation, its applications, and the challenges of this complex form of data annotation.
The Most Important Natural Language Processing (NLP) Techniques Explained
Do you struggle to optimize your website’s keywords for search engine ranking? Are you tired of manually analyzing and selecting keywords for your content? Natural Language Processing (NLP) techniques may be your solution. NLP is a branch of artificial intelligence that deals with the interaction between computers and human language. It has been increasingly applied to search engine optimization (SEO) in recent years- from keyword extraction to topic modeling.
Transforming Tree Counting for Large-Scale Farming
The large-scale oil palm plantation and the orderly rows of banana trees might look serene, but their management presents complex challenges. From overseeing the hectares of plantation to keeping an eye on the dying plants and restoring the health of wilted ones to manually keeping a count of the trees, numerous problems can arise. Manual labor and traditional methods not only waste resources but also require more time. Plus, there is always room for inaccuracy.
Guide to Video Annotation for Computer Vision
As a subcategory of data annotation, video annotation is used in training AI models and improving their accuracy. But what exactly is video annotation and how does it work? In this comprehensive guide, we will dive into the world of video annotation, exploring its importance, methods, and best practices. Whether you're a beginner or an experienced professional, this guide should help you get a deeper understanding on the subject.
Your In-Depth Guide to Data Labeling
Data labeling, a pivotal aspect of machine learning models and artificial intelligence, involves the systematic process of adding descriptive metadata or labels to raw data. These labels serve as the foundation for training AI algorithms to recognize patterns, objects, and entities within the data. In this comprehensive guide, we will explore the intricacies of data labeling, its significance, methods, best practices, and more. Join us on this journey to unlock the power of data labeling in the world of AI.
Ready to bring your AI project to life?
Do you need help with data acquisition, data annotation or building a custom model, Aya Data is ready to partner with you. Talk to us today!
