The Challenge
Analyzing multi-spectral satellite imagery covering 500,000+ acres required specialized annotation of crop types, growth stages, disease patterns, and environmental stress indicators. Annotations needed to account for seasonal variations, regional differences, and spectral band interpretations.
Our Solution
Trained agronomy specialists in remote sensing annotation techniques. Developed custom annotation tools supporting multi-spectral band visualization. Created reference library of crop health patterns validated by agricultural scientists. Implemented temporal tracking to monitor crop progression.
Project Specifications
Data Volume
100,000+ satellite images, 50K drone images
Team Size
25 agronomy specialists
Duration
8 months
Accuracy
96.2%
Annotation Types
Tools & Technologies
Deliverables
Sample Annotations
Crop Health Mapping
Color-coded health indicators using NDVI and other vegetation indices across field boundaries
Disease Pattern Recognition
Segmentation masks for fungal infections, pest damage, and nutrient deficiencies
Field Boundary Detection
Precise polygon annotations defining field boundaries for crop rotation tracking
Related Projects
Autonomous Vehicle Perception System
Large-scale annotation project for training autonomous driving perception models, including pedestrian detection, vehicle tracking, lane marking, and traffic sign recognition across diverse driving conditions.
Medical Image Diagnosis Dataset
Comprehensive annotation of chest X-rays, CT scans, and MRI images for training diagnostic AI models to detect pneumonia, tumors, fractures, and other abnormalities with radiologist-level accuracy.
E-Commerce Product Catalog Enrichment
Multi-modal annotation project enriching product catalog with detailed attributes, visual features, and semantic relationships to power advanced search, recommendations, and virtual try-on experiences.