Outdoor Autonomous Driving Image AI Training Data Labeling & Dataset Construction Project

Autonomous Driving · Image Data Labeling Case Study

Outdoor Autonomous Driving Image AI Training Data Labeling & Dataset Construction Project

This case study highlights precision annotation that combines Semantic Segmentation and Object Detection for outdoor road environment images.

Project Overview

Project Name

Outdoor Road Object Recognition Annotation Dataset for Autonomous Driving

Industry

Mobility · Autonomous Driving

Data Type

Outdoor road images · Semantic Segmentation & Object Detection

This project built an AI training dataset using outdoor road environment images to support object detection for autonomous vehicles.
We performed data labeling on real-world images featuring diverse traffic scenarios such as intersections, crosswalks, sidewalks, and vehicles.
Background and area-level elements (road, crosswalk, sidewalk) were annotated via Semantic Segmentation, while traffic signal objects were processed via Object Detection in parallel.
Occluded objects were labeled only for the visible portions, avoiding inference and ensuring high-confidence labeling quality.
Outputs were delivered in structured JSON format containing class and location information, enabling immediate integration into model training pipelines.
Total volume: approximately thousands to tens of thousands of images, supporting production-grade autonomous driving perception model development.

Key Work Scope

For the client-provided outdoor road images, we performed annotation without additional preprocessing and organized deliverables in a structure optimized for autonomous driving perception models.

TASK	Description
Requirements Analysis	Defined the class taxonomy and annotation granularity based on model objectives (e.g., road/pedestrian area separation, traffic signal detection).
Class & Guideline Design	Structured Semantic Segmentation classes (road, crosswalk, sidewalk, bicycle lane, pedestrian, curb, etc.) and Object Detection classes for pedestrian/vehicle traffic signals.
Segmentation Annotation	Performed pixel-level segmentation of driving and pedestrian environments, applying precise labeling rules to minimize overlap between classes.
Object Detection Annotation	Annotated pedestrian and vehicle traffic signals with bounding boxes. For occluded signals, recorded only the visibly confirmed regions to ensure accurate localization.
Quality Review & Correction	Verified class confusion, missing regions, and duplicate labels through a QC process. Non-compliant annotations were reworked and corrected.
JSON Schema Design & Delivery	Structured image-level class, coordinate, and mask information in JSON format and delivered using a schema compatible with the client’s AI training pipeline.

Project Workflow

1 Requirements Definition

2 Class & Guideline Design

3 Segmentation & Detection Labeling

4 First Review & Feedback Integration

5 Second Precision Review

6 Final JSON Delivery

Results & Key Takeaways

Dataset Summary

Volume: Thousands to tens of thousands of outdoor road environment images
Type: High-resolution road images captured from the front and surrounding views of autonomous vehicles
Labeling Scope: Semantic Segmentation for road, crosswalk, sidewalk, bicycle lane, pedestrian, curb, etc. + Object Detection for pedestrian/vehicle traffic signals
Delivery Format: JSON-structured AI training dataset including segmentation and detection outputs

Through this project, we built high-precision annotation data specialized for outdoor autonomous driving environments, providing a strong foundation for improving road object recognition model performance.

Gendive’s Dataset Construction Capabilities

Quality: We manage labeling quality by designing annotation strategies optimized for combined detection and segmentation—clearly separating class boundaries and labeling only confirmed visible geometry.
Control: We establish project-specific QC standards and staged review processes to maintain guideline consistency across labelers and reviewers, while controlling rework systematically.
Scalability: With experience across public and private AI dataset projects, we propose formats and structures that can be readily extended to other cities, environments, and sensors (video, images, etc.).

Gendive Partner Services

We provide end-to-end support across computer vision projects—including autonomous driving and mobility—from post-collection labeling and review to format/schema design.

Why Work with Gendive

We go beyond simple labeling—designing annotation strategies aligned with model objectives to connect dataset quality directly to real service performance.
We maintain consistent labeling quality at scale through more detailed guidelines and multi-stage review systems than typical labeling vendors.
We support multiple formats such as JSON, COCO, and YOLO—delivering results ready for AI training pipelines without additional conversion work.

If you need data labeling and AI training dataset construction for autonomous driving, mobility, or vision recognition, we recommend consulting from the early planning stage.

If you already have an ongoing labeling project, we can also support you with quality audits and review process improvements.

Request a Data Labeling Project Consultation

Contact: Gendive Data Team (Project consultation · Quotation · Partner collaboration inquiries)

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