Experience on data annotation and labelling
The project involved multi-type data labeling and evaluation tasks designed to support AI model training and validation across computer vision, natural language processing, and geospatial intelligence use cases. Work was completed in a structured, guideline-driven environment with emphasis on accuracy, consistency, and quality assurance. The datasets supported machine learning models used for action recognition, location intelligence, content evaluation, speech processing, and data enrichment. Specific Data Labeling Tasks Performed Action Recognition Annotation Labeled and classified human actions and activities from image and video datasets Identified action categories based on predefined taxonomies such as walking, running, interaction, and object usage Ensured temporal and contextual accuracy when tagging actions across frames Applied consistent labels to support supervised learning for computer vision models Geocoding and Location Data Labeling Verified and labeled geographic in