Eyeclimate expands air quality intelligence pilot in New Delhi
A cross-platform AI system mapping pollution sources in real time.

Earth observation is moving from occasional snapshots to operational intelligence. The challenge is no longer collecting data—it is turning large, noisy datasets into decisions that teams can act on quickly and confidently.
This update brings our research pipeline into a real deployment context, combining rigorous validation with practical workflows for operators, researchers, and conservation teams.
Why New Delhi
New Delhi’s air-quality challenge is shaped by many overlapping sources: traffic, construction, industry, seasonal burning, and regional transport. The pilot brings satellite observations, street-level measurements, and emissions modelling into one operational view.
“The goal is not another dashboard. It is trustworthy evidence that shortens the distance between observation and response.”
How the system works
- 01Ingest and normalize multi-source observations
- 02Detect candidate events with product-specific models
- 03Validate confidence against known environmental conditions
- 04Package results for review, reporting, and follow-up action

Early results
3×
Faster review
91%
Validated precision
2 min
Typical processing
What’s next
We are expanding validation with partners and incorporating feedback directly into the next product release. Future updates will share new field results, model benchmarks, and the operational lessons learned along the way.
Continue reading
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