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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 impacts 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, organizations, or financial transactions within documents.


  • Sentiment annotation: Categorizing customer or employee feedback as positive, negative, or neutral.


  • Prompt and response annotation: Evaluating and structuring prompts and responses to align LLM behaviors.

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 specialized 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 labeled 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 modeling.


  • 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

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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AI together


Tell us about your project. We'll show you
the data that gets you there.
Talk to an expert