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

• URL: http://arxiv.org/abs/2602.13350v1
• Date: Thu, 12 Feb 2026 23:47:31 GMT
• 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,
• Abstract summary: Brick kilns are a major source of air pollution and forced labor in South Asia.<n>We study brick kiln detection at scale using high-resolution satellite imagery.<n>We propose a region-adaptive graph-based model that captures spatial and directional structure in kiln layouts.
• Score: 5.0398829018389275
• License: http://creativecommons.org/licenses/by/4.0/
• 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major source of air pollution and forced labor in South Asia, yet large-scale monitoring remains limited by sparse and outdated ground data.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.

Related papers

• AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis [57.249817395828174]
  We propose a scalable framework combining pseudo-synthetic renderings from 3D city-wide meshes with real, ground-level crowd-sourced images.<n>The pseudo-synthetic data simulates a wide range of aerial viewpoints, while the real, crowd-sourced images help improve visual fidelity for ground-level images.<n>Using this hybrid dataset, we fine-tune several state-of-the-art algorithms and achieve significant improvements on real-world, zero-shot aerial-ground tasks.
  arXiv  Detail & Related papers  (2025-04-17T17:57:05Z)
• Reconstructing Satellites in 3D from Amateur Telescope Images [44.20773507571372]
  We propose a novel computational imaging framework that overcomes obstacles by integrating a hybrid image pre-processing pipeline.<n>We validate our approach on both synthetic satellite datasets and on-sky observations of China's Tiangong Space Station and the International Space Station.<n>Our framework enables high-fidelity 3D satellite monitoring from Earth, offering a cost-effective alternative for space situational awareness.
  arXiv  Detail & Related papers  (2024-04-29T03:13:09Z)
• Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators from High-Resolution Orthographic Imagery and Hybrid Learning [1.8369448205408005]
  Overhead images can help fill in the gaps where community information is sparse. Recent advancements in machine learning and computer vision have made it possible to quickly extract features from and detect patterns in image data. In this work, we explore how well two approaches, a supervised convolutional neural network and semi-supervised clustering can estimate population density, median household income, and educational attainment.
  arXiv  Detail & Related papers  (2023-09-28T19:30:26Z)
• A Large Scale Homography Benchmark [52.55694707744518]
  We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset. We also present HEB, a large-scale homography estimation benchmark leveraging Pi3D.
  arXiv  Detail & Related papers  (2023-02-20T14:18:09Z)
• Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery [42.70832378336697]
  High-resolution satellite images can provide abundant, detailed spatial information for land cover classification. Few studies have applied high-resolution images to land cover mapping in detailed categories at large scale. We present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images.
  arXiv  Detail & Related papers  (2022-09-01T21:00:23Z)
• Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image [91.29546868637911]
  This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. The key idea is to formulate the task as pose estimation and solve it by neural-net based optimization. Experiments on standard autonomous vehicle localization datasets have confirmed the superiority of the proposed method.
  arXiv  Detail & Related papers  (2022-04-10T19:16:58Z)
• Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region [0.28675177318965034]
  We develop an ML pipeline that relies on Sentinel-2 satellite images time series. We generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps.
  arXiv  Detail & Related papers  (2022-03-28T12:08:06Z)
• Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching [102.39635336450262]
  We address the problem of ground-to-satellite image geo-localization by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image.
  arXiv  Detail & Related papers  (2022-03-26T20:10:38Z)
• Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification [61.44538721707377]
  We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery. We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method. We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
  arXiv  Detail & Related papers  (2022-03-11T16:14:14Z)
• IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling [90.97859312029615]
  We propose an approach to estimate object count statistics over large geographies through sampling. We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U.S.
  arXiv  Detail & Related papers  (2021-12-16T18:59:29Z)
• Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions [0.0]
  This research aims to allow automated monitoring for largescale infrastructure projects, such as railways, to determine reliable metrics that define and predict the direction construction initiatives could take. By utilizing photogrammetric techniques on available satellite data to create 3D Meshes and Digital Surface Models (DSM) we hope to effectively predict transport routes.
  arXiv  Detail & Related papers  (2020-09-01T02:11:14Z)
• Flood Extent Mapping based on High Resolution Aerial Imagery and DEM: A Hidden Markov Tree Approach [10.72081512622396]
  This paper evaluates the proposed geographical hidden Markov tree model through case studies on high-resolution aerial imagery. Three scenes are selected in heavily vegetated floodplains near the cities of Grimesland and Kinston in North Carolina during Hurricane Matthew floods in 2016. Results show that the proposed hidden Markov tree model outperforms several state of the art machine learning algorithms.
  arXiv  Detail & Related papers  (2020-08-25T18:35:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.