Semantic Segmentation, Instance & Panoptic, 3D Cuboid, LiDAR, Landmarks & More
End-to-End Computer Vision Data Services
Comprehensive Computer Vision Services for Images, Videos, and LiDAR — Bounding Boxes, Polygons, and More
Digital Divide Data (DDD) provides industry-leading computer vision solutions, specializing in data annotation services and utilizing advanced techniques such as bounding boxes, polygons, semantic and image segmentation, keypoints, and more. With our human-in-the-loop approach and advanced AI annotation tools, we ensure precise, high-quality data for training machine learning models across various industries.
Whether you're looking for customized ML solutions or scalability, our team is dedicated to helping businesses achieve success in their AI initiatives. As one of the top data annotation companies, DDD specializes in providing accurate image annotation services and video annotation services designed to meet the unique needs of AI projects.
Using traditional providers and platforms
can cost you 60% of your time.
Computer Vision Annotation Types
We support a full complement of CV annotation types
Bounding Box
Cuboid
Polygon
Keypoint
Instance
Segmentation
Polyline
Semantic Segmentation
Object Tracking
Object tracking requires not only detecting objects but also accurately distinguishing and following objects across video frames. Object tracking use cases include shopper behavior, surveillance, sports analytics, traffic control, autonomous vehicles, and others.
Object Detection
Numerous Computer Vision AI use cases depend on Object Detection to locate and identify pertinent objects in images or videos, including robotics, autonomous vehicles, food harvesting, surveillance, inventory control, and others.
Image Classification
Image Classification is a CV task that involves interpretation, ascertaining whether an image or video meets particular criteria. Classification has broad applications, including visual search, product recommendations, telemedicine, and law enforcement.
Semantic Segmentation
Semantic Segmentation assigns every pixel in an image or video to any of several salient classes (e.g., vehicle, pedestrian, traffic sign, road, building, sky). Semantic Segmentation features prominently in models involving autonomous vehicles, aerial surveying, emissions regulation, and others.
Instance Segmentation
A pixel-level identification of each instance of an object in an image or video. Use cases where Instance Segmentation is beneficial include surveillance, robotic navigation, law enforcement, livestock management, and others.
Bounding Box Annotation
Bounding box annotation is one of the most widely used techniques in computer vision for labeling objects in images or videos. It involves drawing rectangular boxes around objects to identify their location and size. Applications include autonomous driving, retail inventory management, object detection in robotics, and wildlife monitoring.
Polygon Annotation
Polygon annotation offers precise object labeling by outlining exact shapes and boundaries rather than relying on simple rectangles. This technique is beneficial for irregularly shaped objects and complex environments, supporting use cases in aerial imagery analysis, medical imaging, agriculture, and autonomous navigation.
Keypoint Annotation
Keypoint annotation marks specific points of interest on objects, such as human facial landmarks, joints for pose estimation, or mechanical parts in machinery. It is widely used in sports analytics, motion tracking, augmented reality, and quality inspection in manufacturing.
LiDAR Annotation
LiDAR annotation enables the accurate labeling of 3D spatial data captured by LiDAR sensors, essential for applications like autonomous vehicles, drone surveying, and infrastructure mapping. This technique is used to detect and classify road elements, pedestrians, vehicles, and environmental features with high precision.
3D Point Cloud Annotation
3D point cloud annotation labels objects in three-dimensional datasets made up of millions of points, often generated by LiDAR or depth sensors. It plays a crucial role in advanced mapping, construction site monitoring, robotics navigation, and environmental analysis.
Panoptic Segmentation
Panoptic segmentation combines semantic and instance segmentation to provide a complete scene understanding, assigning every pixel to either a specific object instance or a general class. It is essential in urban planning, autonomous driving, agricultural monitoring, and aerial surveillance for its holistic view of complex environments.
Learn how we can help with your Computer Vision AI programs
Training Data Considerations
Expert Training Data Pipeline for Computer Vision and Natural Language Processing
INDUSTRIES
Autonomous Vehicles
DDD empowers the autonomous vehicle industry with high-quality annotations such as bounding boxes, polygons, and semantic segmentation, helping you build safer and more accurate autonomous driving systems.
Government
We support government AI initiatives by delivering annotated imagery for traffic management, infrastructure monitoring, and public safety systems, ensuring efficient decision-making and improved resource allocation.
Agriculture
We specialize in agricultural robotics, providing precise annotations for crop detection, pest identification, and yield estimation, enabling smarter farming solutions and improved productivity.
Healthcare
Our team delivers precise medical image annotation, including segmenting organs, identifying anomalies, and training AI models to enhance diagnostics, streamline workflows, and enhance patient care.
Finance
DDD enhances financial AI models with visual data labeling for tasks such as fraud detection, document analysis, and identity verification, enabling greater efficiency and compliance.
Social Media
Our annotations power AI for social media platforms by identifying inappropriate content, detecting trends, and improving user engagement through better content moderation and recommendation systems.
Geospatial Intelligence
We provide geospatial annotation services, including satellite image segmentation and object detection, enabling applications in urban planning, disaster management, and defense intelligence.
Insurance
We assist insurers by annotating imagery and videos for claims analysis, risk assessment, and damage evaluation, helping accelerate claim processing and fraud detection with AI-powered insights.
Legal
We offer visual annotation services for legal applications such as document verification, evidence analysis, and courtroom visualizations, ensuring AI-driven accuracy and efficiency in legal workflows.
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The DDD Difference
Strategic
We are more than a data labeling service. We bring industry-tested SMEs, provide training data strategy, and understand the data security and training requirements needed to deliver better client outcomes.
Reliable
Our global workforce allows us to deliver high quality work, 365 days a year, across 100’s and 1000’s of data labelers across multiple countries and time zones. With 24/7 coverage, we are agile in responding to changing project needs.
Consistent
We are lifetime project partners. Your assigned team will stay with you - no rotation. And as your team becomes experts over time, they train more labelers. That's how we achieve scale.
Flexible
We are platform agnostic. We don't force you to use our tools, we integrate with the technology stack that works best for your project.
Secured, for ease of mind
Data and information security is a mission critical business function at DDD. Our clients depend on us to keep their valuable and confidential information secure, and we take this responsibility seriously.
