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Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data

Usman Nazir, Xidong Chen, Hafiz Muhammad Abubakar, Hadia Abu Bakar, Raahim Arbaz, Fezan Rasool, Bin Chen, Sara Khalid

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major source of air pollution and forced labor in South Asia, yet large-scale monitoring remains limited by sparse and outdated ground data.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 sensing approaches, providing practical guidance for scalable brick kiln monitoring from satellite imagery.

Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data

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major source of air pollution and forced labor in South Asia, yet large-scale monitoring remains limited by sparse and outdated ground data.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 sensing approaches, providing practical guidance for scalable brick kiln monitoring from satellite imagery.
Paper Structure (44 sections, 16 equations, 5 figures, 3 tables)

This paper contains 44 sections, 16 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Graph Neural Networks
  4. Foundation Models
  5. Remote Sensing Methods
  6. Dataset and Preprocessing
  7. Study Regions
  8. Imagery
  9. Experimental Setup
  10. Evaluation Regimes
  11. Evaluation Metrics
  12. Baselines
  13. Compute and Implementation.
  14. Methods
  15. ClimateGraph
  16. ...and 29 more sections

Figures (5)

  • Figure 1: Example of graph construction for ClimateGraph. Nodes represent POIs enriched with multi-modal environmental features from raster datasets. Edges connect each node to its eight nearest neighbors using great-circle distance, with each edge annotated by a bearing angle for directional message passing. This structure enables the model to capture both spatial proximity and orientation, critical for detecting emission sources in heterogeneous landscapes.
  • Figure 2: ClimateGraph architecture. The model processes geospatial graphs through stacked anisotropic attention layers that integrate directional kernels with geometry-aware weights. The pipeline transitions from G-equivariant feature extraction, preserving spatial orientation, to G-invariant classification, enabling robust emission source detection across varied geographic contexts.
  • Figure 3: RemoteCLIP zero-shot classification pipeline.
  • Figure 4: Rex-Omni zero-shot detection pipeline.
  • Figure 5: Remote sensing baseline pipeline for brick kiln detection.