Key Skills

Software

CVAT

SuperAnnotate

Top Subject Matter

Satellite image classification

Self-driving car imagery

Net-Zero Methodology Compliance

Top Data Types

Geospatial Tiled Imagery

Image

Text

Top Task Types

Bounding Box

Evaluation Rating

Land Cover Classification

Text Generation

Text Summarization

Freelancer Overview

My experience in data management and analysis, gained through roles at Aetlas Labs and BioCarbonGrow, is highly applicable to data labelling and AI training data initiatives. In these positions, I managed large datasets crucial for sustainability projects, ensuring data accuracy and integrity—skills directly transferable to AI data labelling. My technical proficiency with data analysis tools and project management expertise equips me to handle the complexities of AI training data projects effectively. My background in sustainability provides a unique perspective, ensuring that data labelling efforts align with broader environmental and industry-specific goals. Moreover, my experience in the EV logistics space, particularly through roles at Paack and ShipBob, involved optimizing logistics solutions and integrating sustainable practices. This required precise data management and analysis to improve operational efficiencies and reduce emissions, skills that are directly applicable to data labelling tasks. Combining my expertise in sustainability, e-commerce logistics, and EV logistics, I am well-positioned to contribute to data labelling and AI training data initiatives, ensuring high-quality, accurate datasets for machine learning models.

Labeling Experience

Satellite Image Classification

CVATGeospatial Tiled ImageryBounding BoxPolygon

The primary objective of this project was to quantify changes in biomass within tree canopies and identifying deforestation risk features such as roads and local deforestation patterns. The project involved detailed labelling of satellite imagery to train the AI model. - Annotating tree canopies for biomass changes using polygon and semantic segmentation. - Identifying roads and deforestation risks with line annotation. - Marking deforestation areas through polygon annotation and semantic segmentation. Project Size: - The project processed a large dataset of high-resolution satellite images, involving thousands of images and a team of skilled annotators and GIS specialists, using advanced labelling tools. Quality & Data accuracy through: - Regular inter-annotator agreement assessments. - Multiple rounds of review and validation. - Comprehensive guidelines and training for annotators.

The primary objective of this project was to quantify changes in biomass within tree canopies and identifying deforestation risk features such as roads and local deforestation patterns. The project involved detailed labelling of satellite imagery to train the AI model. - Annotating tree canopies for biomass changes using polygon and semantic segmentation. - Identifying roads and deforestation risks with line annotation. - Marking deforestation areas through polygon annotation and semantic segmentation. Project Size: - The project processed a large dataset of high-resolution satellite images, involving thousands of images and a team of skilled annotators and GIS specialists, using advanced labelling tools. Quality & Data accuracy through: - Regular inter-annotator agreement assessments. - Multiple rounds of review and validation. - Comprehensive guidelines and training for annotators.

2023

Review of Project Documentation for Methodology Compliance

SuperannotateTextQuestion AnsweringText Generation

The project focused on reviewing written project documentation to ensure compliance with established methodology standards, aiming to improve the consistency and quality of project outputs. The data labelling tasks included: - Categorizing Sections: Labelling different sections of the documentation to identify their purpose (e.g., introduction, methodology, results). - Compliance Tags: Annotating sections with tags indicating compliance or non-compliance with methodology standards. - Highlighting Deviations: Marking specific areas where the documentation deviated from the required standards. - Feedback Annotations: Adding comments and recommendations directly within the documents to guide necessary revisions.

The project focused on reviewing written project documentation to ensure compliance with established methodology standards, aiming to improve the consistency and quality of project outputs. The data labelling tasks included: - Categorizing Sections: Labelling different sections of the documentation to identify their purpose (e.g., introduction, methodology, results). - Compliance Tags: Annotating sections with tags indicating compliance or non-compliance with methodology standards. - Highlighting Deviations: Marking specific areas where the documentation deviated from the required standards. - Feedback Annotations: Adding comments and recommendations directly within the documents to guide necessary revisions.

2023 - 2023

Education

Work History