Agriculture Specialist

Innodata Inc.; Bordeaux; Sur place; EUR 30 000 - 50 000; Plein temps

Résumé du poste

A leading data engineering company in Bordeaux is seeking a candidate to classify crop types from satellite imagery. The role involves reviewing images, applying established protocols for labeling, and ensuring accuracy across large datasets. Candidates must have a Bachelor's degree in Agriculture or a related field, along with experience in crop identification and image analysis. This position offers the opportunity to contribute to agricultural research using advanced AI technology.

Qualifications

• Bachelor’s degree (or higher) in Agriculture, Agronomy, Crop Science, or a related field.
• Knowledge in Geography, Remote Sensing, Environmental Science, or GIS.
• Previous involvement in crop identification or agricultural surveys.

Responsabilités

• Review satellite and street-view images for crop classification.
• Ensure consistent and accurate labeling following workflows.
• Maintain accuracy and consistency across volumes of images.

Connaissances

Attention to detail

Visual identification

Formation

Bachelor’s degree in Agriculture or related field

Outils

Image annotation tools

Description du poste

Innodata (NASDAQ : INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider‑of‑choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.

By combining advanced machine learning and artificial intelligence (ML / AI) technologies, a global workforce of subject matter experts, and a high‑security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy‑to‑use, high‑quality platforms.

Our global workforce includes over 5,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany.

Project Goal

The objective of this project is to classify crop types from satellite imagery by leveraging high‑quality crop labels derived from street‑view images of fields, providing a scalable ground‑truth dataset for agricultural research and AI training.

Responsibilities

• Review satellite and street‑view images to determine crop type or classify areas as uncultivated based on visual cues.
• Follow established workflows and annotation protocols to ensure consistent and accurate labeling.
• Apply domain knowledge to identify crop types even under partial occlusion.
• Maintain accuracy, attention to detail, and consistency across large volumes of images.

Workflow

• Agricultural Area Presence – Confirm if agricultural fields occupy more than a defined percentage (e.g., 40%) of the image view.
• Field Visibility Assessment – Evaluate visibility of the field.
• Partially Occluded but Identifiable – Annotate the crop type or mark as uncultivated.
• Clearly Visible and Identifiable – Proceed to assign the correct crop label.

Qualifications

• Academic Background : Bachelor’s degree (or higher) in Agriculture, Agronomy, Crop Science, Agricultural Engineering, Horticulture, or a related field.
• Hands‑on Experience : Knowledge in Geography, Remote Sensing, Environmental Science, or GIS with exposure to crop identification.
• Agriculture Experience : Previous involvement in crop identification, agricultural surveys, or image annotation.
• Visual Identification Skills : Ability to distinguish crop types from both partial and full views.

Preferred Skills

• Strong attention to detail and familiarity with diverse crop types.
• Prior experience using image annotation tools and platforms is an advantage.
• Ability to work independently and deliver results under defined timelines.