Wen L.

Key Skills

Software

Other

Top Subject Matter

Autonomous driving perception (road/obstacle/traffic) using sensor data

Autonomous driving navigation data quality and preparation

Urban Digital Twin spatial data processing (geometric/multi-modal)

Top Data Types

Image

Video

Geospatial Tiled Imagery

Top Task Types

Mapping

Classification

Entity (NER) Classification

Freelancer Overview

Data Annotation Specialist | Data Processing Center, China Academy of Aerospace (2020–2021). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor’s Degree, Yanshan University (2002). AI-training focus includes data types such as Geospatial, Tiled Imagery, and 3D Sensor and labeling workflows including Segmentation, Evaluation, and Rating.

Labeling Experience

Technical Department Manager | Aerospace Geoinformatics Technology Co., Ltd. (2022–2024)

3D Sensor

Managed the technical department overseeing end-to-end operational pipelines for massive Urban Digital Twin datasets supporting complex simulation environments. Supervised quality control frameworks to improve overall data accuracy and process efficiency across data processing stages. Coordinated cross-functional teams to align raw spatial data into standardized formats for reliable data ingestion into technical deployments. • Led geometric and multi-modal data structuring for digital twin/simulation workflows. • Audited operational pipelines and applied strict quality control to raise accuracy. • Standardized spatial data formats to ensure consistent ingestion and downstream use. • Improved processing efficiency through pipeline optimization and team coordination.

2022 - 2024

Data Processing Team Lead | Data Processing Center, China Academy of Aerospace (2021–2022)

3D Sensor

Led a data processing team responsible for managing and refining high-precision autonomous driving navigation data collected by Tencent. Implemented data auditing and validation workflows to eliminate noise and ensure clean, high-fidelity inputs for vehicle AI models. Provided technical guidance and structured task allocation to improve weekly processing throughput. • Conducted rigorous audits and validation of navigation/drive data quality. • Eliminated noisy samples to support perception and downstream AI training. • Guided team members with standardized task allocation and processing methods. • Improved processing throughput while maintaining data reliability.

2021 - 2022

Data Annotation Specialist | Data Processing Center, China Academy of Aerospace (2020–2021)

Segmentation

Created pixel-level and semantic annotations for complex road, obstacle, and traffic datasets used for Tencent Autonomous Driving vehicles. Ensured annotations followed provided guidelines and correctly mapped raw sensor outputs to defined ground-truth targets. Performed QA-focused review to achieve top-tier accuracy for perception model training and iterative evaluation. • Annotated scenes at pixel/semantic granularity for navigation and driving-relevant elements. • Interpreted labeling guidelines with ML engineers to maintain consistency with targets. • Validated annotation quality through rigorous QA checks to reduce noise. • Enabled high-fidelity dataset inputs for training and evaluation cycles.

2020 - 2021

Education

Work History