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.