6 Best Data Labeling Platforms for Logistics AI & Automated Machinery [2025]

AI is transforming logistics and automated machinery, from autonomous forklifts and drones to warehouse optimization and industrial process monitoring. But even the most advanced AI models can’t function without precisely labeled data.

High-quality labeled training data is key across every AI application, but becomes even more crucial is high-stakes deployment like machinery. Not only does it often involve multimodal data but it spans both robotics and computer vision, which hinge on video, image, and LiDAR labeling. 

In logistics, AI relies on:

• Video feeds from warehouses, ports, or factories
• LiDAR and 3D point clouds for navigation and obstacle detection
• Sensor and telemetry data from robots and conveyor systems
• RFID and barcode tracking for inventory management
• Anomaly detection for predictive maintenance

Choosing the right data labeling platform is critical. Here are the top 8 platforms for logistics and automated machinery AI in 2025.

1. Encord – End-to-End Multimodal Platform

Encord is built for enterprise-scale operations with multimodal workflows. It can handle video, LiDAR, sensor data, and more, making it ideal for autonomous warehouse vehicles and industrial machinery monitoring.

Key features:

• Annotate video, 3D, LiDAR, and images - among other modalities (text, image, audio, DICOM)
• Automation: AI-assisted labeling and frame interpolation for moving machinery
• Workflow management: dashboards, role-based tasks, QA monitoring, & agentic workflow for greater automation
• Compliance: SOC 2, GDPR, HIPAA which are key important for industrial and employee data

Best for: Logistics companies and industrial operators needing end-to-end data ops.

2. Segments.ai – 2D + 3D Fusion for Robotics & Machinery

Segments.ai shines when you need synchronized multi-view labeling, which is great for automated forklifts, drones, and conveyor monitoring.

Key features:

• Sync camera feeds with LiDAR/3D data
• Track moving objects or inventory in real time
• Semi-automated frame interpolation
• Collaborative dataset management and versioning.

Best for: Multi-sensor warehouse or factory automation projects.

3. Scale AI – High-Volume Warehouse Annotation

Scale AI excels at high-throughput labeling, supporting large-scale logistics operations like autonomous vehicles in ports or warehouses.

Key features:

• Dense video annotation and object tracking
• Bounding boxes, segmentation, and trajectory labeling for forklifts and drones
• Human-in-the-loop QA for accuracy at scale
• Integrates with ML pipelines for predictive maintenance or path optimization

Best for: Logistics operators handling massive video datasets.

4. Kili Technology – Lightweight Robotics & Video Annotation

Kili Technology is ideal for smaller teams or pilot projects. It supports motion tracking and annotation without requiring complex infrastructure.

Key features:

• Annotate video, images, and sensor data
• Automation using pre-labeling models like SAM
• Simple team collaboration and task assignment

Best for: Startups and small automation teams testing new AI workflows.

5. CVAT – Open-Source Industrial Annotation

CVAT is widely used for research and custom projects in logistics and automated machinery. It allows full flexibility for tailored annotation pipelines.

Key features:

• Bounding boxes, polygons, cuboids, and trajectory labeling
• Customizable for proprietary sensors or camera setups
• Integration with your own AI models for semi-automated labeling.

Best for: Research labs or teams needing customizable open-source solutions.

6. Label Studio – Flexible, Open-Source Annotation for Robotics & Logistics

Label Studio is one of the most flexible and customizable data labeling platforms, making it a strong choice for logistics and robotics projects that involve multiple data types and deployment constraints. Its open-source foundation and enterprise edition provide teams with the ability to adapt workflows to very specific industrial use cases.

Key features:

• Multi-modal support: For robotics projects that combine camera feeds with telemetry or machine logs.
• Custom workflows: Build specialized annotation interfaces and task pipelines tailored to your industrial environment
• Model-in-the-loop: Integrate your own ML models for pre-labeling and active learning
• Deployment flexibility: Open-source or enterprise deployment options, including on-premises setups for factories and warehouses where data can’t leave the site.

Best for: Robotics and logistics teams that need on-prem deployment for sensitive industrial or operational data.

Choosing the Right Data Labeling Platform

Why Encord Leads in Logistics & Automated Machinery AI

In logistics and industrial automation, teams need more than labeling—they need full data infrastructure. Encord delivers:

• Multimodal support (video, LiDAR, sensors, telemetry, 3D)
• Automation for repetitive labeling tasks
• End-to-end workflow management from labeling to dataset QA
• Enterprise-grade compliance for sensitive operational data

For autonomous forklifts, warehouse AI, and industrial robotics, Encord is the most complete data labeling platform in 2025.