1. The UNDP Case

UNDP describes brick kiln monitoring in India as a case of collective intelligence and climate action.
Traditional brick kilns create environmental concerns through the use of fertile topsoil and emissions from chimneys.
UNDP also notes concerns about forced labour conditions among some kiln workers.

The UNDP Accelerator Lab in India and the University of Nottingham developed a new methodology using
artificial intelligence, machine learning and geospatial analytics to map the brick kiln belt.
The approach was subsequently used by the Bihar State Pollution Control Board to better target environmental policy violations.

Read the UNDP Case Study →

2. The Monitoring Problem

Monitoring a very large number of geographically dispersed brick kilns through conventional field inspection creates a major scale challenge.

The central practical problem was:

How can environmental regulators identify and prioritize sites that require attention when the monitoring population is extremely large?

3. The GeoAI Intervention

The GeoAI approach combined several sources of intelligence rather than relying on a single technology.

UNDP describes the GeoAI digital platform as using satellite imagery and computer-vision algorithms to detect brick kilns and identify environmental monitoring hotspots.

4. Human Intelligence + Machine Intelligence

One of the particularly interesting aspects of this case is that AI was not developed in isolation.

The partners worked with citizen scientists through the Zooniverse platform to create a labelled dataset of satellite images.
The labelled data were then used to train a computer-vision algorithm to identify brick kilns.

This created a useful chain:

5. Monitoring at Scale

According to UNDP, more than 47,000 brick kilns were detected across the Indo-Gangetic plains of India and incorporated into the UNDP GeoAI open-data platform.

Source note: Figures in this table are reported by UNDP in its primary case study and refer to the different stages of the initiative. :contentReference[oaicite:1]{index=1}

6. From Detection to Prioritization

The value of the system is not simply that it can identify brick kilns.
The important operational step is that it can help make a very large monitoring population more manageable.

This illustrates a potentially important role for AI in monitoring:
helping human teams decide where limited monitoring resources should be concentrated.

7. AI Does Not Replace Field Verification

Satellite imagery and AI can identify patterns and potential hotspots, but they do not necessarily provide all the information required to understand what is happening at a specific site.

The GeoAI approach therefore connects:

• large-scale remote observation;
• AI-based detection;
• geospatial prioritization;
• field inspection; and
• regulatory action.

This is better understood as AI-assisted monitoring rather than completely autonomous monitoring.

8. The AI-Assisted Monitoring Evidence Chain

For M&E professionals, the case can be translated into an evidence chain:

The distinction between the different stages is critical.
Detecting a kiln is not the same as demonstrating an environmental outcome.

9. Monitoring Is Not the Same as Outcome Measurement

This case provides an important lesson for evaluators.
AI-generated monitoring information should not automatically be treated as evidence of impact.

EvalCommunity perspective: This distinction is an analytical framework for M&E practice, not a framework stated by UNDP in the primary case study.

10. How Should an AI Monitoring System Be Evaluated?

EvalCommunity proposes seven dimensions for evaluating AI-assisted geospatial monitoring:

1. Detection accuracy — How accurately does the system identify relevant sites?
2. Coverage — How much of the monitoring population can be assessed?
3. Prioritization accuracy — Are high-priority sites actually more likely to require intervention?
4. False positives and negatives — What does the system incorrectly flag or miss?
5. Field verification — How closely do AI-generated signals correspond with ground observations?
6. Operational efficiency — Does the system reduce time or cost?
7. Outcome contribution — Does improved monitoring contribute to measurable environmental or social change?

This seven-dimension framework is an EvalCommunity adaptation and is not presented as a UNDP framework.

11. The False-Negative Problem

One of the most important questions in AI-powered monitoring is not simply:
“How accurate is the system?”

It is also:
“What is the system systematically missing?”

If an AI system identifies a subset of high-risk sites for inspection, evaluators should examine whether important high-risk sites are being missed.

12. Equity and Differential Performance

AI monitoring systems may perform differently depending on geographic conditions, imagery quality, site characteristics and other contextual factors.

Evaluators should therefore ask:

• Are some types of sites harder for the AI to detect?
• Are some geographic areas systematically under-detected?
• Does imagery quality affect detection performance?
• Are certain populations or communities less visible in the data?
• Do AI-generated priorities create unintended distributional effects?

13. GeoAI as Collective Intelligence

UNDP presents the case as an example of collective intelligence, bringing together regulators, government agencies, civil society and volunteer groups.

The primary case study identifies the methods as:

This is an important reminder that effective AI monitoring systems are often not simply AI systems.
They are socio-technical systems combining technology, data, people and institutions.

14. Proposed M&E Indicators

These are proposed EvalCommunity indicators and are not reported by UNDP as the evaluation framework for this initiative.

15. How Could the Impact Be Evaluated?

If an organization wanted to go beyond assessing AI performance and evaluate the broader contribution of GeoAI to environmental outcomes, a possible design could compare:

Potential outcomes could include:

• environmental compliance;
• adoption of cleaner technologies;
• emissions;
• inspection efficiency;
• regulatory response time; and
• other relevant environmental or social outcomes.

EvalCommunity note: This is a proposed evaluation design, not a design reported by UNDP in the primary case study.

16. Do Not Confuse AI Performance with Development Impact

This case illustrates an important M&E principle.

The causal pathway between the technology and the ultimate outcome must be examined.

17. Lessons for M&E Professionals

18. Evidence Boundary

What UNDP documents:
a collaboration between the UNDP Accelerator Lab in India and the University of Nottingham using artificial intelligence, machine learning, geospatial analytics, satellite imagery, computer vision, citizen science and crowdsourcing to support brick kiln monitoring.

UNDP reports more than 47,000 brick kilns detected across the Indo-Gangetic plains and describes the Bihar application, including the identification of high-risk kilns, field inspections, green transitions and reported CO₂ reductions. :contentReference[oaicite:2]{index=2}

What this case study adds:
the AI-assisted monitoring evidence chain, proposed M&E indicators, quality-assurance framework and possible impact-evaluation design are EvalCommunity interpretations and adaptations. They should not be presented as UNDP’s own evaluation framework or as independently verified causal findings.

Key Takeaway

The important innovation is not simply using AI to recognize brick kilns from satellite imagery.

It is connecting large-scale AI-assisted detection with human verification, regulatory action and ongoing monitoring.

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Sources & Further Reading

Primary source:

UNDP — Brick kiln monitoring in India

UNDP India:

GeoAI for Brick Kilns in Bihar: Learnings and Recommendations

UNDP India:

Tech for Clean Air and Blue Skies

EvalCommunity Academy:

AI in Monitoring & Evaluation Certificate