![[2602.13350] Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13350] Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data
Summary
This paper presents a novel approach to detecting brick kiln infrastructure using high-resolution satellite imagery, focusing on a new model called ClimateGraph that enhances monitoring capabilities across South and Central Asia.
Why It Matters
Brick kilns significantly contribute to air pollution and labor exploitation in South Asia. This research addresses the challenge of monitoring these kilns at scale, providing a solution that leverages advanced satellite imagery and machine learning techniques, which can inform policy and environmental management.
Key Takeaways
- Introduces ClimateGraph, a graph-based model for detecting brick kilns.
- Utilizes a dataset of over 1.3 million satellite image tiles for analysis.
- Compares the effectiveness of graph, foundation, and remote sensing models.
- Highlights the importance of scalable monitoring solutions for environmental issues.
- Provides practical guidance for implementing satellite-based detection methods.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13350 (cs) [Submitted on 12 Feb 2026] Title:Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data Authors:Usman Nazir, Xidong Chen, Hafiz Muhammad Abubakar, Hadia Abu Bakar, Raahim Arbaz, Fezan Rasool, Bin Chen, Sara Khalid View a PDF of the paper titled Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data, by Usman Nazir and Xidong Chen and Hafiz Muhammad Abubakar and Hadia Abu Bakar and Raahim Arbaz and Fezan Rasool and Bin Chen and Sara Khalid View PDF HTML (experimental) Abstract:Brick kilns are a major source of air pollution and forced labor in South Asia, yet large-scale monitoring remains limited by sparse and outdated ground data. We study brick kiln detection at scale using high-resolution satellite imagery and curate a multi city zoom-20 (0.149 meters per pixel) resolution dataset comprising over 1.3 million image tiles across five regions in South and Central Asia. We propose ClimateGraph, a region-adaptive graph-based model that captures spatial and directional structure in kiln layouts, and evaluate it against established graph learning baselines. In parallel, we assess a remote sensing based detection pipeline and benchmark it against recent foundation models for satellite imagery. Our results highlight complementary strengths across graph, foundation, and remote...
