Detecting brick kiln infrastructure at scale: graph, foundation, and remote sensing models for satellite imagery data

Conference item

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major source of air pollution and forced labor in South Asia, yet large-scale monitoring remains limited by sparse and outdated ground data.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 resolution (0.149 m pixel⁻¹ ) dataset comprising over 1.3 million image tiles across five regions in South and Central Asia. We propose ClimateGraph, a region-adaptive graphbased 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 sensing approaches, providing practical guidance for scalable brick kiln monitoring from satellite imagery.

Authors: Nazir, U; Chen, X; Abu Bakar, H; Abubakar, HM; Arbaz, R

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: PMLR
• Series: Proceedings of Machine Learning Research
• Acceptance date: 2026-01-28
• Event title: 43rd International Conference on Machine Learning (ICML 2026)
• Event location: Seoul, South Korea
• Event website: https://icml.cc/Conferences/2026
• Event start date: 2026-07-06
• Event end date: 2026-07-11
• Language: English