Francisco Bison

Key Skills

Software

Scale AI

Remotasks

Top Subject Matter

Sensor 3D

Image Annotation

LiDAR Tecnology

Top Data Types

3D Sensor

Image

Top Label Types

Polygon

Cuboid

Classification

Polyline

Bounding Box

Freelancer Overview

I have approximately one and a half years of experience in AI training focused on data annotation using LiDAR technology, working with both 3D environments and hybrid 2D/3D systems. I participated in projects related to autonomous driving, performing accurate identification and classification of pedestrians and various types of vehicles, including cars, trucks, cargo trucks, motorcycles, and bicycles, ensuring consistency, quality, and compliance with precision metrics. Additionally, I worked on labeling road infrastructure and urban environments, including road markings, lane lines, highways, drivable and non-drivable areas, and traffic signs. I developed strong skills in point cloud interpretation, spatial segmentation, attention to detail, and adherence to strict technical guidelines, contributing to the improvement of computer vision models applied to intelligent mobility systems.

Labeling Experience

Lime-JR-ltd-full-v2025

ImageBounding Box

I worked on a Remotasks project focused on 2D data annotation, labeling vehicles, pedestrians, animals, and traffic signs in images. The task involved accurately drawing bounding boxes and performing object tracking across multiple frames to maintain consistent identification of moving objects. This role required strong attention to detail and precision while following strict annotation guidelines, ensuring the dataset remained accurate and reliable for training and improving computer vision systems.

I worked on a Remotasks project focused on 2D data annotation, labeling vehicles, pedestrians, animals, and traffic signs in images. The task involved accurately drawing bounding boxes and performing object tracking across multiple frames to maintain consistent identification of moving objects. This role required strong attention to detail and precision while following strict annotation guidelines, ensuring the dataset remained accurate and reliable for training and improving computer vision systems.

2025 - 2025

jr-ltd-map-V2-FULL

Scale AI3D SensorPolyline

I worked on a Remotasks project involving hybrid 2D and 3D data annotation, where the goal was to label road infrastructure elements for autonomous driving datasets. The tasks included annotating road symbols, traffic signs, and defining drivable and non-drivable areas. Using polylines and other annotation tools, I carefully marked road boundaries and zones while following detailed project guidelines to ensure accuracy and consistency in the dataset. This work required attention to detail, consistency in labeling, and the ability to follow annotation guidelines to produce high-quality training data for computer vision models.

I worked on a Remotasks project involving hybrid 2D and 3D data annotation, where the goal was to label road infrastructure elements for autonomous driving datasets. The tasks included annotating road symbols, traffic signs, and defining drivable and non-drivable areas. Using polylines and other annotation tools, I carefully marked road boundaries and zones while following detailed project guidelines to ensure accuracy and consistency in the dataset. This work required attention to detail, consistency in labeling, and the ability to follow annotation guidelines to produce high-quality training data for computer vision models.

2025 - 2025

Anteater 3 D OD 5 FWP Group

Scale AI3D SensorCuboid

Project developed at Remotask focused on 3D data annotation using LiDAR technology. The role involved accurately identifying and classifying various types of vehicles — including cars, trucks, motorcycles, bicycles, and cargo trucks — as well as pedestrians within point cloud environments. The work required strong attention to detail, spatial awareness in 3D environments, and strict adherence to quality guidelines to support the training of autonomous driving and computer vision AI models.

Project developed at Remotask focused on 3D data annotation using LiDAR technology. The role involved accurately identifying and classifying various types of vehicles — including cars, trucks, motorcycles, bicycles, and cargo trucks — as well as pedestrians within point cloud environments. The work required strong attention to detail, spatial awareness in 3D environments, and strict adherence to quality guidelines to support the training of autonomous driving and computer vision AI models.

2024 - 2025

Jack-Rabbit

3D SensorCuboid

I worked on a 3D data annotation project using LiDAR technology, focused on labeling pedestrians and a wide range of vehicles. The task involved identifying and classifying different vehicle types with detailed categories, ensuring each object was accurately annotated in the 3D point cloud. Objects were tracked frame by frame, maintaining consistent IDs while assigning specific attributes according to the project requirements. All tasks were completed by following detailed guidelines and meeting strict deadlines for each annotation assignment.

I worked on a 3D data annotation project using LiDAR technology, focused on labeling pedestrians and a wide range of vehicles. The task involved identifying and classifying different vehicle types with detailed categories, ensuring each object was accurately annotated in the 3D point cloud. Objects were tracked frame by frame, maintaining consistent IDs while assigning specific attributes according to the project requirements. All tasks were completed by following detailed guidelines and meeting strict deadlines for each annotation assignment.

2023 - 2024

Education

Work History