Afrisal Arzaqi

Key Skills

Software

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Geospatial Tiled Imagery

Image

Video

Top Task Types

Bounding Box

Classification

Object Detection

Point Key Point

Segmentation

Freelancer Overview

With a strong foundation in AI and Machine Learning, I have significant hands-on experience in preparing and managing high-quality training data, the cornerstone of any successful AI model. My expertise covers the full data lifecycle, including data collection, cleaning, feature engineering, and structuring diverse datasets for various machine learning applications. I excel at analyzing complex data to ensure its integrity and quality, transforming raw information into refined, model-ready assets that drive accurate and reliable outcomes. My practical experience is highlighted by projects requiring meticulous data handling. In developing Computer Vision systems, I processed and prepared large-scale image datasets for training object detection models like YOLO and CNN, a process involving rigorous data annotation and quality control. For an AI-powered sales forecasting tool, I was responsible for cleaning and structuring complex time-series data to ensure predictive accuracy. These projects have honed my proficiency in using Python and Pandas to systematically prepare and label data, directly contributing to the performance and success of the final AI models.

Labeling Experience

Earthquake Satellite Imagery

RoboflowImageSegmentation

As a specialist in geospatial data analysis for crisis response, I executed a critical instance segmentation project to identify and delineate infrastructure impacted by natural disasters and conflict. Leveraging the Roboflow platform, I meticulously annotated high-resolution satellite imagery, creating precise segmentation masks for buildings damaged by earthquakes, landslides, floods, tsunamis, and weapon strikes. This highly specialized task required nuanced interpretation of post-event imagery to ensure data accuracy for training computer vision models. The resulting dataset is instrumental for developing automated systems for rapid damage assessment, directly supporting and accelerating humanitarian relief and recovery operations.

As a specialist in geospatial data analysis for crisis response, I executed a critical instance segmentation project to identify and delineate infrastructure impacted by natural disasters and conflict. Leveraging the Roboflow platform, I meticulously annotated high-resolution satellite imagery, creating precise segmentation masks for buildings damaged by earthquakes, landslides, floods, tsunamis, and weapon strikes. This highly specialized task required nuanced interpretation of post-event imagery to ensure data accuracy for training computer vision models. The resulting dataset is instrumental for developing automated systems for rapid damage assessment, directly supporting and accelerating humanitarian relief and recovery operations.

2025

ANPR System

RoboflowVideoBounding Box

As a Data Annotator, I was responsible for developing a high-quality image dataset for an Automatic Number Plate Recognition (ANPR) system in Indonesia. Utilizing the Roboflow platform, I meticulously labeled thousands of vehicle images with precise bounding boxes to accurately identify license plates. My role included implementing data augmentation techniques to enhance dataset variety and ensuring strict quality control for labeling consistency. The resulting, curated dataset was exported in a machine learning-ready format, serving as the foundational asset for training a robust and accurate object detection model.

As a Data Annotator, I was responsible for developing a high-quality image dataset for an Automatic Number Plate Recognition (ANPR) system in Indonesia. Utilizing the Roboflow platform, I meticulously labeled thousands of vehicle images with precise bounding boxes to accurately identify license plates. My role included implementing data augmentation techniques to enhance dataset variety and ensuring strict quality control for labeling consistency. The resulting, curated dataset was exported in a machine learning-ready format, serving as the foundational asset for training a robust and accurate object detection model.

2025 - 2025

Education

Work History