James R.

Key Skills

Software

Appen

CVAT

Roboflow

Other

Top Subject Matter

Urban mapping and autonomous vehicle training (street-level imagery)

LiDAR point cloud refinement and 3D perception

Architectural floor plan vectorization for spatial AI

Top Data Types

Text

Geospatial Tiled Imagery

Image

Document

3D Sensor

Top Task Types

Classification

Object Detection

Text Generation

Evaluation/Rating

Segmentation

Polygon

Cuboid

Freelancer Overview

Data Annotation Intern — IntelliScan Africa. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include CVAT, Other, and AutoCAD. Education includes Bachelor of Engineering, University of Uyo (2022) and Diploma, TS Academy. AI-training focus includes data types such as Geospatial, Tiled Imagery, and 3D Sensor and labeling workflows including Segmentation, Cuboid, and Polygon.

Labeling Experience

Engineering Intern (SIWES) — Eauxwell Nigeria Limited (blueprint analysis)

Classification

You analyzed technical blueprints to ensure installation matched engineering schematics, supporting accurate downstream data interpretation. You reviewed design documentation to align physical execution with specified plans and technical requirements. While primarily engineering-focused, your work involved blueprint understanding that supports structured spatial data handling. • Blueprint and schematic interpretation for alignment • Ensuring compliance with engineering documentation • Supporting structured understanding of technical plans • Technical analysis tied to installation planning

2025 - 2025

Data Annotation Intern

Other3D SensorSegmentationPolygon

As a Data Annotation Intern at IntelliScan Africa, I performed precise 2D panoptic segmentation on street-level urban images. I refined 3D LiDAR point clouds and executed vectorization tasks for architectural floor plans to support AI model training. Rigorous self-QA and manual correction were integral to ensuring high-quality data deliverables. • Applied 100% pixel-accurate segmentation techniques for autonomous vehicle datasets. • Improved LiDAR-based spatial awareness through cuboid placement and point cloud refinement. • Managed vectorization of complex architectural floor plans, defining spatial boundaries and attributes. • Conducted manual quality assurance on auto-annotated datasets, correcting mask bleeding and misclassifications.

2024 - 2024

Education

Work History