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.
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.
Key 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.
Will contact relevant individuals for the project details soon.