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.

Innodata Inc. : Rater – Crop Classification in Satellite and Street View Images, Language:(DUTCH), Location:(NETHERLANDS)

Job Description:

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

1. Agricultural Area Presence – Confirm if agricultural fields occupy more than a defined percentage (e.g., 40%) of the image view.
2. 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.

If Interested, kindly send your resume at: [email protected].