Image & Visual Data Services

Image & Visual Data Services provide labeled and structured visual datasets for training AI in image recognition, facial analysis, and computer vision applications. These services are fundamental for autonomous systems, security applications, and digital media enhancement.

3D Point Cloud Annotation

Involves labeling 3D spatial data (e.g., from LiDAR) to identify objects, distances, or boundaries. Workers tag points for AI to interpret environments, often for autonomous vehicles or robotics.

Aerial & Satellite Image Annotation

Focuses on annotating overhead imagery (e.g., buildings, roads, vegetation) to train AI in geospatial analysis. Workers label features for applications like mapping or urban planning.

Facial Recognition & Emotion Labeling

Workers identify faces in images and tag emotional states (e.g., happy, sad) or attributes (e.g., age, gender), enabling AI to process human expressions or authenticate identities.

Image Annotation & Labeling

Entails tagging images with metadata (e.g., object types, colors) to train AI in visual recognition. Workers provide detailed labels for broad or specific use cases.

Image Captioning

Workers generate descriptive text for images (e.g., “dog chasing ball”), creating datasets to train AI in understanding and describing visual content.

Image Data Augmentation

Involves altering images (e.g., rotating, cropping, adjusting brightness) to expand datasets. Workers create and validate variations, enhancing AI robustness across conditions.

Medical Image Annotation

Workers label medical visuals (e.g., X-rays, MRIs) to identify anomalies (e.g., tumors, fractures). This trains AI for diagnostic support, requiring precision and domain knowledge.

Optical Character Recognition (OCR) Data Labeling

Entails tagging text within images (e.g., signs, documents) for AI to extract and interpret. Workers transcribe and annotate, ensuring accuracy in varied fonts or distortions.

Scene & Object Recognition Tagging

Workers label entire scenes (e.g., “beach”) or specific objects (e.g., “chair”) in images, training AI to categorize environments or detect items.

Synthetic Image Generation

Involves creating artificial images (e.g., simulated landscapes) to supplement real data. Workers design and validate synthetic visuals, boosting dataset diversity.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are crucial in orchestrating the development and enhancement of Image & Visual Data AI systems.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to curate the visual data that drives these systems.

Training and Onboarding

PMs design and implement training programs to ensure workers understand visual standards, annotation guidelines, and project objectives. For example, in medical image annotation, PMs might train workers to recognize subtle anomalies, using sample scans and reference materials. Onboarding includes hands-on labeling tasks, feedback loops, and calibration sessions to align worker outputs with AI needs. PMs also establish workflows, such as multi-tier reviews for complex 3D point clouds.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., labeling 15,000 satellite images) and set metrics like precision, consistency, or coverage. They monitor progress via dashboards, resolve inefficiencies, and update guidelines based on worker insights or client priorities.

Collaboration with AI Teams

PMs connect visual data curators with machine learning engineers, translating technical requirements (e.g., pixel-level accuracy) into actionable tasks. They also manage timelines to sync data delivery with AI training cycles.

We Manage the Tasks Performed by Workers

The annotators, labelers, or creators perform the detailed work of preparing high-quality visual datasets. Their efforts are meticulous and visually focused, requiring precision and contextual awareness.

Common tasks include:

Labeling and Tagging

For scene recognition, we might tag an image as “forest” or “urban.” In facial recognition, they label expressions like “surprised” or “neutral.”

Contextual Analysis

For image captioning, our team crafts descriptions like “child playing soccer.” In 3D point cloud annotation, they analyze spatial relationships to tag “car” or “tree.”

Flagging Violations

In medical annotation, our employees and subcontractors flag unclear scans (e.g., blurry regions), ensuring reliable data. In OCR labeling, they mark illegible text.

Edge Case Resolution

We address complex cases—like overlapping objects or ambiguous emotions—often requiring discussion or escalation to visual experts.

We can quickly adapt to and operate within our clients’ annotation platforms, such as proprietary visual tools or industry-standard systems, efficiently processing batches of images ranging from dozens to thousands per shift, depending on task complexity.

Data Volumes Needed to Improve AI

The volume of curated visual data required to train and refine Image & Visual Data AI systems is substantial, driven by the diversity of visuals and use cases. While specifics vary by task and model, general benchmarks include:

Baseline Training

A functional model might require 10,000–50,000 labeled images per category (e.g., 50,000 object-tagged photos). For specialized tasks like medical annotation, this could rise to 100,000.

Iterative Refinement

To improve accuracy (e.g., from 85% to 95%), an additional 5,000–20,000 samples per issue (e.g., misidentified objects) are often needed. For example, refining facial recognition might demand 10,000 new faces.

Scale for Robustness

Large-scale systems (e.g., autonomous driving) require datasets in the millions to cover edge cases, lighting conditions, or perspectives. A satellite imagery model might start with 200,000 annotated images, expanding by 50,000 annually.

Active Learning

Advanced systems use active learning, where AI flags uncertain visuals for review. This reduces volume but requires ongoing curation—perhaps 1,000–5,000 images weekly—to sustain performance.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and quality.

Multilingual & Multicultural Image & Visual Data Services

We can assist you with your image and visual data needs across diverse linguistic and cultural contexts.

Our team is equipped to annotate and process visual data for global applications, ensuring culturally relevant and accurate datasets tailored to your goals.

We work in the following languages: