Space to Policy: Scalable Brick Kiln Detection and Automatic Compliance Monitoring with Geospatial Data

2024-12-05

arXiv.org

AI Summary

Unregulated brick kiln operations, inefficient manual monitoring, and the limited scalability and weak policy integration of existing remote sensing approaches hinder effective environmental compliance enforcement in the brick manufacturing sector. Method: This study proposes an end-to-end regulatory compliance monitoring framework integrating medium-resolution open-source satellite imagery (Planet Labs) with machine learning—specifically YOLOv8—to achieve high-accuracy automated detection and typological classification of 30,638 brick kilns across five Indian states in the Gangetic Plain. A novel rule-driven policy mapping engine dynamically links detection outputs to localized emission standards and technology eligibility criteria. Contribution/Results: The low-cost, scalable framework demonstrates strong agreement with ground surveys in the Delhi airshed (Cohen’s κ > 0.85) and effectively identifies policy implementation gaps, thereby facilitating adoption of cleaner kiln technologies. It offers a transferable technical pathway for dynamic industrial source monitoring and environment–livelihood co-governance in developing countries.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Efficient ML / Green AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Air pollution kills 7 million people annually. The brick kiln sector significantly contributes to economic development but also accounts for 8-14% of air pollution in India. Policymakers have implemented compliance measures to regulate brick kilns. Emission inventories are critical for air quality modeling and source apportionment studies. However, the largely unorganized nature of the brick kiln sector necessitates labor-intensive survey efforts for monitoring. Recent efforts by air quality researchers have relied on manual annotation of brick kilns using satellite imagery to build emission inventories, but this approach lacks scalability. Machine-learning-based object detection methods have shown promise for detecting brick kilns; however, previous studies often rely on costly high-resolution imagery and fail to integrate with governmental policies. In this work, we developed a scalable machine-learning pipeline that detected and classified 30638 brick kilns across five states in the Indo-Gangetic Plain using free, moderate-resolution satellite imagery from Planet Labs. Our detections have a high correlation with on-ground surveys. We performed automated compliance analysis based on government policies. In the Delhi airshed, stricter policy enforcement has led to the adoption of efficient brick kiln technologies. This study highlights the need for inclusive policies that balance environmental sustainability with the livelihoods of workers.

Problem

• Detecting brick kilns using scalable ML for pollution monitoring
• Automating compliance analysis with government policies
• Balancing environmental sustainability and worker livelihoods

Innovation

• Scalable machine-learning pipeline for detection
• Uses free moderate-resolution satellite imagery
• Automated compliance analysis with government policies