Levis Njonge

Key Skills

Software

Appen

Axiom AI

CloudFactory

CVAT

Label Studio

Scale AI

Top Subject Matter

Geospatial Data Labeling

Image Annotation and Computer Vision

Automotive AI for Self-driving Cars

Top Data Types

3D Sensor

Geospatial Tiled Imagery

Image

Top Task Types

Bounding Box

Classification

Computer Programming Coding

Mapping

Object Detection

Freelancer Overview

I am an experienced Data Labeling Specialist with a strong background in AI training data for machine learning models. Having worked with platforms like Appen, I have contributed to diverse projects that require precise labeling and annotation of text, images, and video data. My expertise spans across various industries, including natural language processing (NLP), computer vision, and business data management, ensuring high-quality datasets for AI systems. I possess a keen eye for detail and a focus on accuracy, enabling me to deliver reliable and consistent results in complex labeling tasks. In addition to my technical skills, I have experience in project management, efficiently handling large datasets and meeting tight deadlines. My proficiency in Microsoft Suite and visual annotation skills further enhance my ability to contribute to AI training projects. With a background in Business Administration and Information Technology, I bring a unique combination of business acumen and technical know-how, setting me apart as a versatile data labeling professional.

Labeling Experience

Image Annotation for AI Model Training

AppenImageBounding BoxEntity Ner Classification

Worked on a large-scale AI training project where I labeled both text and image data for machine learning models. In text labeling, I focused on Named Entity Recognition (NER) and text classification, ensuring accurate identification of key entities and thematic classification for NLP tasks. For image data, I performed object detection and segmentation, focusing on e-commerce product categorization and visual search enhancements. The project required high levels of precision, consistency, and adherence to strict quality control measures. I contributed to labeling over 10,000 data points while maintaining a 98% accuracy rate.

Worked on a large-scale AI training project where I labeled both text and image data for machine learning models. In text labeling, I focused on Named Entity Recognition (NER) and text classification, ensuring accurate identification of key entities and thematic classification for NLP tasks. For image data, I performed object detection and segmentation, focusing on e-commerce product categorization and visual search enhancements. The project required high levels of precision, consistency, and adherence to strict quality control measures. I contributed to labeling over 10,000 data points while maintaining a 98% accuracy rate.

2023 - 2024

Geospatial Data Labeling for Land Cover Classification

CVATGeospatial Tiled ImageryPolygonSegmentation

Participated in a geospatial AI training project that involved labeling satellite imagery for land cover classification. The primary task was to accurately segment and classify different land types, including vegetation, urban areas, water bodies, and agricultural fields. Using polygon annotation tools in CVAT, I helped create a detailed map of land cover for environmental monitoring and agricultural planning purposes. The project spanned thousands of images and required detailed knowledge of land features, consistent labeling, and adherence to quality standards. My work contributed to improving the AI model’s ability to recognize and classify land features with over 95% accuracy.

Participated in a geospatial AI training project that involved labeling satellite imagery for land cover classification. The primary task was to accurately segment and classify different land types, including vegetation, urban areas, water bodies, and agricultural fields. Using polygon annotation tools in CVAT, I helped create a detailed map of land cover for environmental monitoring and agricultural planning purposes. The project spanned thousands of images and required detailed knowledge of land features, consistent labeling, and adherence to quality standards. My work contributed to improving the AI model’s ability to recognize and classify land features with over 95% accuracy.

2023 - 2024

Video Annotation for Object Detection in Autonomous Vehicles

Scale AIVideoBounding BoxObject Detection

Led video annotation tasks for an autonomous vehicle project, focusing on object detection and action recognition. I applied bounding boxes to label vehicles, pedestrians, and other objects, as well as track their movement across frames for accurate vehicle behavior prediction. This involved annotating hundreds of hours of driving footage, maintaining consistency and precision in dynamic environments. I also contributed to action recognition by identifying specific driving maneuvers and potential hazards. Rigorous quality assurance checks were applied, achieving an accuracy rating of over 95%.

Led video annotation tasks for an autonomous vehicle project, focusing on object detection and action recognition. I applied bounding boxes to label vehicles, pedestrians, and other objects, as well as track their movement across frames for accurate vehicle behavior prediction. This involved annotating hundreds of hours of driving footage, maintaining consistency and precision in dynamic environments. I also contributed to action recognition by identifying specific driving maneuvers and potential hazards. Rigorous quality assurance checks were applied, achieving an accuracy rating of over 95%.

2023 - 2023

Education

Work History