The enterprise guide to data annotation across AI modalities: text, image, video, and LiDAR
Every type of artificial intelligence requires its own unique approach to data annotation. An LLM trained on text requires a very different annotation pipeline than an autonomous vehicle relying on LiDAR. For enterprise leaders, understanding the modalities of data annotation, including text, image, video and LiDAR, is essential for choosing the right vendor and strategy. Each modality presents different challenges, requires different skill sets and affects enterprise AI outcomes in distinct ways.
Text annotation for LLMs and NLP
Text annotation forms the backbone of large language models (LLMs) and natural language processing (NLP) applications. Common annotation tasks include:
Named entity recognition (NER): Tagging entities such as people, organisations or financial transactions within documents.
Sentiment annotation: Categorising customer or employee feedback as positive, negative or neutral.
Prompt and response annotation: Evaluating and structuring prompts and responses to align LLM behaviours.
Enterprises use these annotations to power AI applications ranging from chatbots to regulatory compliance systems, ensuring models are trained on text that is both contextually accurate and linguistically diverse.
Image annotation for computer vision
Computer vision models depend on large volumes of annotated images. Annotation can take the form of bounding boxes, polygons or pixel-level segmentation. Key enterprise applications include:
Retail: Training models for shelf monitoring to ensure inventory is tracked in real time.
Manufacturing: Detecting product defects during quality assurance processes.
Autonomous vehicles (AV): Annotating millions of pedestrian and vehicle images to train safe navigation models.
Without accurate image annotation, these AI models risk misclassification that can damage brand trust or even create safety risks.
Video annotation for temporal models
Video annotation requires annotation sequences of frames, often at millisecond intervals. This is critical for AI systems that depend on temporal context:
Warehouse robotics: Rely on annotated video to navigate efficiently and safely.
Security monitoring systems: Detect threats or anomalies in real time.
Sports analytics: Tag player movements frame by frame for deep operational insights.
The complexity and volume of video data make accurate annotation particularly challenging, requiring specialised workflow orchestration platforms to ensure both speed and precision.
LiDAR and 3D point cloud annotation
Light detection and radar (LiDAR) data annotation is at the heart of autonomous driving and robotics. LiDAR sensors generate massive 3D point clouds that must be segmented and labelled with precision. Key characteristics include:
Classifying pedestrians, vehicles and obstacles in three-dimensional space.
Powering robotics navigation, drone-based mapping and augmented reality (AR) and virtual reality (VR) spatial modelling.
Introducing depth complexity that distinguishes LiDAR from traditional 2D images.
Only a combination of automation and human-in-the-loop (HITL) workflows can deliver the accuracy enterprises require for safety-critical applications.
Summary of data annotation techniques by modality
Data modality | Use case examples | Annotation techniques | Potential challenges |
|---|---|---|---|
Text | Chatbots, NLP, regulatory compliance, sentiment analysis | Named Entity Recognition (NER), sentiment tagging, prompt/response alignment | Contextual nuances, linguistic diversity, domain specificity |
Image | Retail shelf tracking, manufacturing QA, computer vision | Bounding boxes, polygons, semantic/pixel-level segmentation | High visual variability, edge-case object detection |
Video | Warehouse robotics, security anomaly detection, sports analytics | Frame-by-frame tracking, temporal annotation, object persistence | High data volume, temporal consistency across frames |
Lidar | Autonomous vehicles, drone mapping, AR/VR spatial models | 3D point cloud segmentation, bounding cuboids, spatial tracking | Depth complexity, high accuracy needed for safety-critical environments |
Why Uber AI Solutions
Uber AI Solutions supports all annotation modalities, including text, image, video, audio and LiDAR, with tailored workflows designed for each domain. Our uLabel platform combines automation with human-in-the-loop validation, delivering both scale and accuracy. With proven expertise across industries and modalities, Uber enables enterprises to deploy AI models confidently, knowing their training data is annotated with precision.
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