AI-Ready Data & Platform

Transforming satellite imagery into crop analytics (Saudi Arabia)

Built satellite-to-analytics platform on Azure that segments imagery, classifies crops, estimates yields, securing multiple government contracts

Client: Orion Earth
Industry: Agriculture / GeoSpatial

Client Context & Problem

Orion Earth needed to turn satellite images into actionable insights—identifying crop types by region and estimating yields. They required a system that could ingest raw imagery, segment land, and output analytics for government agencies.

Pain Points

  • Huge satellite data volumes
  • Noise and clouds in imagery
  • Regional variance in crop patterns
  • Need for high-performance inferencing
  • Strict data privacy requirements
  • Unknown model generalisation across climates

Key Challenges

Data volume

Processing huge satellite data volumes with noise and cloud cover

Regional variance

Models needed to generalize across different climates and crop patterns

Performance

High-performance inferencing required for thousands of images per day

Data privacy

Strict data privacy requirements for government contracts

Project Goal

Build a Satellite-to-Analytics platform on Azure that segments images, classifies crop types, estimates yields, and presents results in dashboards—leading to faster, more accurate agricultural decisions and multiple government contracts.

Success Metrics

  • Process thousands of images per day
  • Segment fields and classify crop types accurately
  • Estimate yields for government planning
  • Secure multiple government contracts

Solution & Platform Architecture

Using Azure ML Studio and open source models fine-tuned on regional data, we created a pipeline: ingest satellite imagery; pre-process and filter noise; segment via U-Net and Mask R-CNN models; classify crops; estimate yields using regression models and weather data; store results in a data warehouse; and visualise them through custom dashboards. Auto-scaling clusters ensured high performance during inference.

Architecture

Transforming satellite imagery into crop analytics (Saudi Arabia) Architecture Diagram

Azure-based pipeline with ML Studio, U-Net/Mask R-CNN for segmentation, regression models for yield estimation, and BI dashboards

Key Components

  • Azure Event Grid for satellite image ingestion
  • Pre-processing pipeline for noise filtering and stitching
  • U-Net and Mask R-CNN models for field segmentation
  • Classification models for crop type identification
  • Regression models for yield estimation
  • Azure Data Warehouse for analytics storage
  • Custom dashboards for visualization
  • API endpoints for third-party integration

Workflow

1

Ingestion

Satellite images arrive via Azure Event Grid

2

Pre-processing

Pre-processing cleans and stitches images

3

Segmentation

Segmentation models delineate fields

4

Classification

Classification models identify crop type

5

Yield estimation

Regression models estimate yield per parcel

6

Storage & visualization

Analytics layer stores aggregated results and feeds interactive dashboards

7

API access

API endpoints expose data to third-party systems

User Experience

Before

Manual analysis of satellite images took weeks; government decisions delayed; limited visibility into crop patterns

  • Manual download and analysis of satellite imagery
  • Weeks to identify crop types and estimate yields
  • Limited coverage and accuracy
  • Delayed government planning decisions

After

Government users access maps with coloured overlays for crop types and yield estimates; they can drill down into individual parcels, export reports, and plan resource allocation.

  • Automated image processing at scale
  • Real-time dashboards with crop type overlays
  • Yield estimates for individual parcels
  • Drill-down and export capabilities
  • Decisions made in hours instead of weeks
  • Thousands of images processed per day

Impact & Results

Processing Time

Before
Weeks
After
Hours
95%+ reduction

Government Contracts

Before
None
After
Multiple contracts
New revenue streams

Image Volume

Before
Limited
After
Thousands per day
Massive scale increase

Decision Speed

Before
Weeks to decide
After
Hours to decide
Faster government planning

Business Outcomes

  • Client secured multiple government contracts
  • Decisions that once took weeks are now made in hours
  • System scales to thousands of images per day
  • Raw images turned into actionable insights that win contracts

Why C4Scale

Azure expertise

Deep expertise orchestrating multimodal pipelines on Azure

Remote sensing knowledge

Understanding of satellite data, segmentation, and agricultural analytics

End-to-end delivery

From data ingestion to ML to BI dashboards, we handle it all

Government-ready

Built platform meeting strict data privacy and security requirements

Ready to transform your operations?

Let's discuss how C4Scale can help you achieve similar results