Michael-sam Vidza

Key Skills

Software

CrowdSource

Data Annotation Tech

Dataloop

Labelbox

Other

LabelImg

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Geospatial Tiled Imagery

Image

Top Task Types

Classification

Computer Programming/Coding

Data Collection

Evaluation/Rating

Land Cover Classification

Freelancer Overview

I have strong experience working with data preparation, cleaning, and validation to support AI model development and analytical projects. My background combines technical expertise in computer code/programming, data science, machine learning, and automation, where I have managed large datasets and contributed to projects requiring structured labeling and accurate annotation for predictive modelling. I am highly skilled in using tools such as Python, SQL, and Power BI to maintain data quality and consistency across diverse analytical workflows. I also bring experience with geospatial and environmental data annotation using platforms like ArcGIS and QGIS, as well as preparing structured data for health and social research.

Labeling Experience

Visual Data annotation for computer vision

LabelimgImageClassificationObject Detection

reated annotated image datasets for student projects on object detection and machine learning, supporting supervised model training exercises.

reated annotated image datasets for student projects on object detection and machine learning, supporting supervised model training exercises.

2022 - 2024

Environmental and geospatial image annotation for land used classification

LabelboxGeospatial Tiled ImageryBounding BoxPolygon

Contributed to the preparation and labelling of geospatial datasets used for developing machine learning models aimed at automating land use and land cover classification. This involved annotating satellite images to identify urban areas, vegetation, water bodies, and infrastructure. Data quality assurance checks were performed to ensure precision and consistency across training datasets. The labelled data was later integrated into supervised learning workflows to evaluate model accuracy and improve environmental change detection systems.

Contributed to the preparation and labelling of geospatial datasets used for developing machine learning models aimed at automating land use and land cover classification. This involved annotating satellite images to identify urban areas, vegetation, water bodies, and infrastructure. Data quality assurance checks were performed to ensure precision and consistency across training datasets. The labelled data was later integrated into supervised learning workflows to evaluate model accuracy and improve environmental change detection systems.

2020 - 2020

Geospatial data annotation for market analysis

OtherGeospatial Tiled ImageryBounding BoxPolygon

nnotated and labelled satellite imagery to map customer distribution and retail network coverage, supporting predictive spatial models for market expansion.

nnotated and labelled satellite imagery to map customer distribution and retail network coverage, supporting predictive spatial models for market expansion.

2015 - 2019

Education

Work History