6 Best Data Labeling Platforms for Smart Cities AI [2025]

AI is transforming smart cities, from real-time traffic management and environmental sensing to public safety, infrastructure monitoring, and citizen services. But even the most advanced urban AI systems depend on accurately labeled data.

High-quality labeled training data is essential in smart city deployments where stakes are high, and data is multimodal: video, satellite,  aerial imagery, LiDAR, sensor data, etc.

In smart cities, AI relies on:

• Video from traffic cameras, CCTV, and public safety monitoring
• LiDAR, radar and 3D point clouds for mapping infrastructure, autonomous transit, obstacle detection
• IoT sensor and telemetry data from water, energy, air quality, sound, waste etc.
• GIS / geospatial / satellite/aerial imagery for planning, zoning, land use, flood risk
• Anomaly detection for utilities, environment, structural health, etc.

Choosing the right data labeling platform is critical. Here are the top 6 platforms for smart city AI in 2025, including some newer names.

1. Encord – End-to-End Multimodal Platform

Encord remains strong for enterprise-scale, multimodal workflows. It supports video, LiDAR, sensors, images, etc., making it suitable for traffic flows, infrastructure monitoring, and public safety applications in the city.

Key features:

• Annotate video, LiDAR, 3D, images + text/audio. The platform is also video-native, making it ideal for computer vision applications
• Automation to help with repetitive frame interpolation, motion tracking and SAM 2 integration for faster labeling
• Workflow management: QA dashboards, role-based access, collaboration, monitoring for enhanced visibility and observability across enterprise-grade AI teams
• Strong compliance: GDPR, SOC2 etc., which is critical for public sector data

Best for: Municipal governments, integrators, smart city service providers needing a full data ops stack.

2. Sama – Scale, Quality & Multimodal Depth

Sama has expanded its features recently, adding better LiDAR / 3D support, plus enhanced tooling for annotating large scale, complex multimodal data.

Key features:

• Very high throughput that allows for hundreds of millions of frames or annotation shapes per month
• Strong quality assurance and human-in-the-loop processes for catching edge cases, sensor errors etc. 
• Multimodal support (video, images, LiDAR), with capabilities to handle sensor fusion tasks
• Reporting / dashboards for monitoring quality and progress

Best for: Cities and agencies needing reliable, large-scale annotation for multimodal systems, especially safety-critical ones.

3. iMerit – Geospatial & Public Sector Ready

iMerit is a newer consolidated platform that brings together automation, annotation tools, and analytics. Strong credentials in geospatial work, public sector, and domains where data privacy and domain expertise matter. 

Key features:

• Handles image, video, LiDAR, sensor fusion, and geospatial / remote sensing imagery.
• Workflow automation + analytics, with QA and reporting.
• Strong public sector / government credentials (secure offices, compliance) and experience.
• Domain specialists / subject matter experts in geospatial imagery and mapping.

Best for: Urban planning agencies, mapping departments, municipalities needing precision in GIS / aerial imagery, and secure public data handling.

4. Labelbox – Flexible & Multimodal with Strong Ecosystem Integration

Labelbox has matured a lot, and is widely used across industries including those relevant to smart city applications. Its feature set supports many modalities and use cases. 

Key features:

• Supports image, video, audio, geospatial tiled imagery, documents etc. 
• Good tooling for segmentation, object detection, classification; well‐designed annotation interfaces and integrations.
• Strong for teams that want a mix of managed service + self-serve tooling.
  Good presence / support in Europe (useful for GDPR, local compliance).

Best for: Municipal AI/ML teams wanting a flexible platform that can adapt as their needs grow, especially where different data types are involved.

5. Annotell – Safety / Perception Analytics Specialty

Annotell is more specialized, but its strengths map well onto smart city perception problems (e.g. pedestrian detection, traffic, autonomous transit). 

Key features:

• Strong video annotation, perception analytics; tools that help measure dataset quality and object detectability under different conditions. 
• Keyframe interpolation, support for temporal coherence (useful for moving objects in traffic scenes etc.) 
• Ability to connect sensor data to ground truth for safety/perception evaluation.

Best for: Smart mobility, autonomous transit, traffic analysis, intersections, any application where moving objects and detection under variable conditions matter.

6. Snorkel Flow (Snorkel AI) – Programmatic Labeling & SME Collaboration

For many smart city problems (e.g. anomaly detection, sensor fusion, monitoring), labeling can get expensive; programmatic or semi-automated labeling helps. Snorkel’s “Flow” product is strong here. 

Key features:

• Labeling functions for automating parts of the labeling task (e.g. pattern matching, embeddings, keyword or feature-based rules) instead of doing everything manually. 
• Tools for collaboration between domain experts / SMEs + data scientists. Good for ensuring labels capture domain nuance (e.g. what is “road obstruction”, “public safety breach”, “infrastructure damage”).
• Error / conflict detection & analysis; helps in iterating and improving label quality.

Best for: Budget-sensitive city projects, R&D / pilot projects, or where you have SMEs who can encode domain knowledge, but need to scale labeling more efficiently.

Choosing the Right Data Labeling Platform

Why Encord Still Leads (Even Among New Players)

While these new players bring compelling strengths, Encord continues to be a top contender in smart city AI because it provides:

• A truly multimodal stack: video, images, LiDAR, sensors, GIS etc.
• Strong tools for automation of repetitive tasks (frame interpolation, pre-labeling) + edge case detection.
• Workflow & dataset management with quality tracking, dashboards, role separation.
• Enterprise-grade governance and compliance capabilities (important for public sector, privacy, safety).