Overview
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.
Role
About the role: Job title: Rater for Crop Classification in Satellite and Street View Images
Hourly commitment: 4-5 hours per day
The project aims to accurately classify crop types from satellite imagery, leveraging high-quality crop labels derived from street-view images of fields. This data will serve as a scalable ground-truth reference to improve model accuracy in agricultural mapping and analysis.
Responsibilities

Classify crop types or identify uncultivated areas based on satellite and street-view imagery.
Apply workflow protocols to ensure efficient and consistent annotations.
Assess agricultural field presence and visibility within each image.
Determine crop type or mark as uncultivated when fields are partially occluded but still identifiable.
Accurately label crop types when fields are clearly visible and identifiable.

Qualifications

Education: Bachelor’s degree or higher in Agriculture, Agronomy, Crop Science, Agricultural Engineering, Horticulture, or related fields.
Relevant background: Academic or professional exposure to Geography, Remote Sensing, Environmental Science, or GIS with a focus on crop identification.
Agricultural expertise: Experience in crop identification, agricultural surveys, or prior work with crop-related image annotation.
Visual skills: Strong ability to distinguish crop types from both partial and full visual perspectives.
Preferred skills: Exceptional attention to detail and familiarity with diverse crop varieties; prior experience using image annotation tools; ability to work with precision and maintain consistency across large datasets.

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