Job Title: Rater – Crop Classification in Satellite and Street View Images
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
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.