Pith. sign in

REVIEW 4 major objections 5 minor 85 references

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

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper shows that free, moderate-resolution satellite imagery, together with object detection and hand validation, can produce a 30,638-kiln inventory across the Indo-Gangetic Plain, matching independent surveys and supporting…

desk verdict Useful, hard-won dataset with honest caveats; the recall gap outside UP is real and should be addressed before the policy numbers are quoted as point estimates. read the letter →

arxiv 2412.04065 v3 pith:JMI2JMRM submitted 2024-12-05 cs.LG

Zeel B Patel , Rishabh Mondal , Shataxi Dubey , Suraj Jaiswal , Sarath Guttikunda , Nipun Batra This is my paper · ORCID
classification cs.LG
keywords objectdetectionsatelliteimagerybrickkilnsairqualityIndo-GangeticPlaincompliancemonitoringemissioninventoryUNSDGs
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is trying to establish that a scalable pipeline can replace manual brick-kiln surveys with free satellite data and machine learning, and that the resulting inventory is accurate enough to drive government compliance monitoring. The authors train an object detector on 1,621 hand-labeled kilns in four regions, fine-tune it on hand-validated detections in Uttar Pradesh, and apply it to five states covering 520,000 square kilometers, ending with 30,638 hand-validated kilns classified into three kiln technologies. They test this against an independent state survey and report a district-level correlation of 0.94 in Uttar Pradesh, plus 70% of all kilns violating at least one siting rule, a computed 8% contribution to Delhi PM2.5, and 30.66 million people living within 800 meters of kilns. If true, this matters because it converts a slow, expensive, and quickly outdated monitoring process into a repeatable, essentially free pipeline that can be rerun every season and tied directly to policy rules.

What carries the argument

The load-bearing mechanism is the oriented bounding box (OBB): a rotated rectangle that hugs a kiln's footprint rather than using an axis-aligned box. Labels are drawn on high-resolution basemap imagery as geo-referenced OBBs and then transferred onto free 4.77-meter-per-pixel mosaics; a single-shot deep detector (YOLO11m-obb) is trained on 1,621 such boxes, applied to Uttar Pradesh, hand-validated to add 15,627 kilns, fine-tuned, and run over the five states. The same OBB geometry is what lets the pipeline go beyond counting: it supports kiln-level spatial extent, feeds technology classifications into emission factors, and anchors the temporal backtracking of technology change by binary search through historical imagery. Distance-based compliance checks run by computing point-to-feature distances from open map data and hospital locations against the siting thresholds in central and state rules.

What would settle it

Conduct an independent stratified re-scan of a random sample of grid cells across the four non-UP states using high-resolution imagery and count how many kilns the published 30,638 inventory omits. If the omission rate is comparable to the 0.12–0.45 out-of-region recall, the true total is substantially higher and the reported compliance, emissions, and 30.66-million exposure figures are understated.

Watch

Extended reading notes

Core claim

The paper's central claim is that brick kilns can be detected, geo-located, and classified by technology at regional scale using free moderate-resolution satellite imagery, not costly high-resolution imagery, and that the results agree with on-ground surveys. The authors report 30,638 hand-validated kilns across Uttar Pradesh, Bihar, West Bengal, Haryana, and Punjab; district counts in Uttar Pradesh correlate at r = 0.94 with an independent state survey, and counts in Delhi-NCR, Bihar, and West Bengal align with court-affidavit and field-survey reports. On this inventory they build an automated compliance system that finds 21,402 of 30,638 kilns, about 70%, violating at least one central or state siting rule, identifies a sharp technology shift from fixed-chimney kilns to zigzag kilns in the Delhi airshed but not in Lucknow, estimates total emissions of about 226 tonnes per day of PM2.5, attributes about 8% of Delhi's PM2.5 to brick kilns for March and April 2024, and counts 30.66 million people living within 800 meters of kilns, a non-compliant distance under central rules.

Load-bearing premise

Everything downstream assumes that the hand-validated detections in Bihar, West Bengal, Haryana, and Punjab miss few kilns; the paper measures precision there, not recall, and its own out-of-region experiment shows recall between 0.12 and 0.45 when the model faces unfamiliar regions.

Editorial extensions

If this is right

  • District-level compliance tables can be generated automatically for any state that publishes siting rules, turning rule enforcement from a survey task into a data product.
  • Tracking kiln technology over time with historical imagery gives a direct way to measure whether the 2026 central deadline for Zigzag conversion is being met, and to see how quickly a new technology diffuses.
  • The Delhi-Lucknow comparison indicates that enforcement intensity, not just regulation, drives technology adoption; the same method can be used to evaluate other non-attainment city airsheds.
  • The inventory's emission and source-apportionment numbers provide a concrete, spatially explicit input for air-quality models, replacing uniform-distribution assumptions.
  • The exposure estimate, 30.66 million people within 800 meters of a kiln, puts a population-scale cost on siting-rule violations and gives relocation efforts a target group.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • My inference: because recall is not measured in four of the five states and the paper's own out-of-region experiment shows recall between 0.12 and 0.45, the 30,638-kiln total is best read as a lower bound, which would bias the compliance, emission, and exposure figures in the same direction.
  • My inference: re-running the pipeline on quarterly satellite mosaics would turn a static inventory into a change-detection system that dates kiln establishment and closure, giving a direct, scalable read on whether the 2026 Zigzag conversion deadline is being met.
  • My inference: the oriented bounding boxes encode kiln footprint, so combining footprint and trench width with known production relationships could yield kiln-level production capacity and emissions without any new survey, refining the state totals.
  • My inference: the same transfer-annotation recipe, labels on high-resolution imagery and training on free medium-resolution imagery, could be applied to other small, dispersed industrial sources where free imagery plus policy rules can drive compliance monitoring.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper presents an end-to-end pipeline that uses free, moderate-resolution Planet Labs imagery and YOLO-OBB object detection to locate and technology-classify brick kilns across five Indian states (Uttar Pradesh, Bihar, West Bengal, Haryana, Punjab), yielding a hand-validated set of 30,638 kilns. The authors validate district counts against independent surveys (UPPCB in UP, CPCB in Delhi-NCR, WBSPCB/UNDP in West Bengal and Bihar), finding a Pearson correlation of 0.94 with the UP survey. On top of the inventory, the paper performs automated distance-based compliance checks against state and central siting rules, a manual temporal analysis of kiln technology transitions in the Delhi and Lucknow airsheds, state-level emission estimates, a WRF-CAMx source apportionment for Delhi, and a population-exposure analysis. The central limitation, acknowledged in the paper but not quantitatively addressed, is that recall of the final model is not measured in the four non-UP states, so the completeness of the 30,638-kiln inventory and all downstream quantities is not directly supported.

Significance. If the central inventory claim is accepted, this is a substantial advance for brick kiln emission inventories and policy compliance monitoring in the Indo-Gangetic Plain. The paper's strengths are its scale (520,000 km², five states, 30,638 hand-validated kilns), its external grounding through independent survey counts (notably the UPPCB district-level correlation of 0.94), its use of free research-licensed imagery, and its reproducible project page. The automatic compliance-monitoring framework, the Delhi/Lucknow technology transition analysis, and the public release of geo-located, technology-tagged data are all genuinely useful contributions. However, the significance of the downstream claims—70% non-compliance, 30.66 million people within 800 m, 8% PM2.5 contribution—is conditional on the inventory being nearly complete, and that completeness is only validated in UP; the evidence for the other states is weaker.

major comments (4)
  1. [Section 4.4 (Table 4) and Section 4.5] The final model's recall is never measured in Bihar, West Bengal, Haryana, or Punjab. The Leave-One-Region-Out experiment (Table 4) reports held-out recall of only 0.12–0.45, and the subsequent iterative fine-tuning with UP data is evaluated only by hand-validation, which measures precision (82%, 76%, 66%, 72%, 71%) and cannot recover kilns the model never proposed. This is load-bearing for the abstract claim of having 'detected and classified 30,638 brick kilns' as a near-comprehensive inventory: if recall in the non-UP states is as low as the out-of-region results suggest, the counts for those states are lower bounds, and downstream compliance, emission, exposure, and source-apportionment numbers are biased to an unknown degree. The paper should either measure final-model recall against independent point-level ground truth in at least one or two non-UP districts (e.g., from the UNDP/Bihar or WBSPCB surveys), or explicitly reframe all non-UP results as 'detected kilns' and add a sensitivity analysis for the downstream quantities.
  2. [Tables 7 and 8] The paper's own comparison with independent surveys shows that its counts are lower than survey counts in every reported Bihar district (1,260 vs. 1,680) and in most West Bengal districts (2,785 vs. 3,895), with correlations of 0.84 for West Bengal and no correlation reported for Bihar. The text attributes the shortfall to 'exclusion errors' and possible permanent kiln closures between the survey period and Q1 2024, but no evidence is provided to separate these two mechanisms. Since the reader can verify that the shortfall is consistent with materially low recall, the manuscript must quantify the plausible range of exclusion errors (e.g., by re-surveying selected districts or by bounding recall from the survey data) rather than leaving both explanations as unquantified possibilities.
  3. [Section 6.1 (Table 12) and Section 6.2] The emission estimates and the 8% PM2.5 source-apportionment contribution assume that the detected kiln set is representative of the true kiln population and that each detected kiln produces bricks at the same rate. This is an explicit modeling assumption: state-level annual production is taken from the literature and allocated to technologies according to the detected kiln fractions. If recall varies by state, technology, or operating status, the per-state emission totals in Table 12 and the Delhi source-apportionment result are not robust. The paper should state this assumption prominently and provide a sensitivity analysis (e.g., varying the missing-kiln fraction from 0% to 30–50% in the under-surveyed states) to show how the headline numbers change.
  4. [Section 5 (Table 10)] Table 10 reports that 70% of brick kilns violate at least one siting rule, but this percentage is computed on the detected set. If missed kilns are systematically different from detected ones (e.g., smaller kilns, older technologies, or kilns in different settlement patterns), the violation rate is biased. At minimum, the manuscript should state that all compliance statistics are conditional on the set of detected kilns; more usefully, it should report the sensitivity of the 'non-compliant' fraction to plausible lower-bound recall values, especially for Bihar and West Bengal where the detected counts are already below survey counts.
minor comments (5)
  1. [Running header (all pages)] The running header on pages after the first reads 'Trovato et al.', which is unrelated to the author list and should be corrected to the actual authors or removed.
  2. [First page footer and ACM Reference Format] The manuscript footer and ACM reference format block state '© 2018' and a placeholder conference acronym; these are leftover template artifacts and should be updated or removed before submission.
  3. [Section 4.6] The phrase 'In another case involving West Bengal Pollution Control Board...' appears in the middle of describing the Bihar UNDP comparison (Section 4.6, near Figures 8 and 9); the paragraph structure makes the source of the Bihar survey count ambiguous. Please separate the Bihar and West Bengal validation descriptions and state the survey source for each clearly.
  4. [Section 9 (Limitation 3)] The limitation text says the method has 'a reasonably accurate precision and practically useful recall of our studies,' but no recall value is reported for the final model anywhere in the paper. Either add the measured recall from a suitable evaluation or rephrase the statement to reflect that recall is unmeasured outside the initial annotated regions.
  5. [Section 7.4] The text states that Uttar Pradesh has 'around 20,000 brick kilns' in 2025, while Table 5 reports 17,335 for Q1 2024 and the UPPCB survey reports 19,671 for 2022. The source of the 20,000 figure should be cited or the statement should be aligned with the numbers used in the paper.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: detection is validated against independent surveys and trained on manual annotations, not on predicted counts.

full rationale

The central claim—30,638 hand-validated brick kilns across five states—rests on an object-detection model trained on 1,621 manually annotated kilns and iteratively fine-tuned on 15,627 hand-validated Uttar Pradesh detections. This training data is independent of the external validation targets: district counts from the UPPCB (Pearson r = 0.94), the CPCB Delhi-NCR affidavit, the UNDP Bihar report, and the WBSPCB West Bengal survey. The downstream compliance, emission, and population-exposure results are applications of the detected inventory to external geodata (OpenStreetMap, LandScan, literature emission factors) and do not feed back into the detection. Self-citations such as the Guttikunda et al. airshed definitions and Mondal et al. prior deployment are contextual and not load-bearing for the main detection claim. The acknowledged inability to measure recall outside Uttar Pradesh (Leave-One-Region-Out recalls of 0.12–0.45; Section 4.5 reports precision but not recall per state) is a genuine uncertainty in inventory completeness and in the magnitude of downstream estimates, but it is a validation gap rather than a circular derivation.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central detection claim rests on assumptions about image resolution, label transfer, and the unquantified recall of the final model in states beyond UP. The compliance and emission claims additionally assume completeness of OSM/government data and equal production per kiln. No new physical entities are postulated.

free parameters (5)
  • Detection confidence threshold = 0.25
    Chosen in Section 4.5 to 'gain more recall at the expense of precision'; the final 30,638-kiln count depends on which candidate detections are presented to human validators.
  • NMS IoU threshold = 0.33
    Chosen for Non-Maximum Suppression to balance duplicate suppression against retaining accurate detections (Section 4.5); affects candidate counts and thus the final inventory size.
  • Initial annotation regions and extents = Delhi airshed, Lucknow airshed, WB small, Ahmedabad buffer
    Strategic selection of four training regions (Section 4.1) shapes the model's generalization; the out-of-region experiment quantifies the gap but the final model's coverage in new states remains dependent on this choice.
  • Annual operating season length = 180 days
    Assumed six-month operating season based on expert inputs (Section 6.1); used to convert annual production into daily emission rates.
  • Per-kiln production weight = 1
    Emissions allocate state-level production proportionally to detected kiln counts (Section 6.1), implicitly assuming equal brick output per kiln regardless of size or operating status; acknowledged as a limitation in Section 7.2.
assumptions (6)
  • domain assumption Planet 4.77 m imagery, with labels transferred from Esri high-resolution OBBs, is sufficient to detect kilns and classify their technology.
    Invoked in Sections 3.1.1 and 4.2; the paper relies on label transfer across resolutions and projections without independent verification of technology labels at 4.77 m.
  • domain assumption OSM and the Indian government hospital dataset are sufficiently complete for distance-based compliance checking.
    Invoked in Section 5.1; incompleteness of OSM residential polygons or hospital points would bias violation counts, and this is not quantified.
  • domain assumption District-level survey counts (UPPCB, CPCB, WBSPCB, UNDP reports) are accurate and temporally comparable to Q1 2024 detections.
    Used in Section 4.6 for external validation; surveys are from 2022-2023, addresses are not reverse-geocoded, and validation is at aggregated district level only.
  • ad hoc to paper Each detected kiln produces bricks at the same rate, so state-level production can be allocated by kiln counts.
    Used in Section 6.1 emission calculations; production capacity varies with kiln size and operating status, which the paper acknowledges in Section 7.2.
  • domain assumption The WRF-CAMx run (80x80 grid, March-April 2024, expert-guided setup) yields reliable source-apportionment contributions without comparison to observed PM2.5.
    Section 6.2 reports the 8% brick kiln contribution; no model validation or sensitivity analysis is presented.
  • domain assumption Technology transitions since 2010 can be determined by manual binary-search inspection of Esri historical imagery.
    Section 5.2; the authors note the manual method misses kilns that existed earlier and closed, and no inter-annotator agreement is reported.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Space to Policy: Scalable Brick Kiln Detection and Automatic Compliance Monitoring with Geospatial Data." pith.science (2026). https://pith.science/paper/JMI2JMRM

@misc{pith2026241204065,
  author       = {Pith},
  title        = {Pith review of: Space to Policy: Scalable Brick Kiln Detection and Automatic Compliance Monitoring with Geospatial Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JMI2JMRM}},
  note         = {Machine review of arXiv:2412.04065}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2412.04065 by the authors.

Figure 1
Figure 1. Satellite view of brick kilns with bounding boxes. CFCBK is Circular Fixed Chimney Bull’s Trench Kiln, and FCBK is Fixed [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Visualization of geographic regions for initial data annotation. The blue boundaries are the regions for annotation. The green [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Illustration of the labeling process. We first create a grid over the labeling region with 1 km [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: A few samples of predicted brick kilns of each brick kiln category from our model. The first, second, and third rows show [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Brick kiln locations across five states of the Indo-Gangetic Plain, India. CFCBKs, which use a relatively old technology, are [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: District-wise brick kiln counts in Uttar Pradesh as per (a) UPPCB 2023 survey (19671 kilns) [ [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: District-wise brick kiln counts in Delhi-NCR as per (a) CPCB 2022 survey and (b) our hand-validated data. White colored [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: District-wise brick kiln counts in Bihar as per (a) UNDP GeoAI field survey [ [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: District-wise brick kiln counts in West Bengal as per (a) WBSPCB 2022 survey [ [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: (a) Compliance with hospitals; (b) Compliance with inter kiln distance policy; (c) Compliance with railway tracks. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Brick kiln technology evolution over 12 years (2010-2022) for two NCAP non-attainment city airsheds: (a) Delhi Airshed [ [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

85 extracted references · 75 canonical work pages

  1. [1]

    [n. d.]. Environment (Protection) Amendment Rules, 2022. https://cpcb.nic.in/uploads/Industry-Specific-Standards/Effluent/74-brick_kiln.pdf. [Accessed 09-11-2024]

    2022

  2. [2]

    [n. d.]. Esri Wayback Imagery — esri.com. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/imagery/wayback-server-connection-in- pro/. [Accessed 10-11-2024]

    2024

  3. [3]

    [n. d.]. Goal 8 | Department of Economic and Social Affairs — sdgs.un.org. https://sdgs.un.org/goals/goal8#targets_and_indicators. [Accessed 08-11-2024]

    2024

  4. [4]

    [n. d.]. NCAP — prana.cpcb.gov.in. https://prana.cpcb.gov.in. [Accessed 09-11-2024]

    2024

  5. [5]

    [n. d.]. NIN health facilities geo code and additional parameters. https://www.data.gov.in/resource/nin-health-faclities-geo-code-and-additional- parameters-updated-till-last-month. [Accessed 26-11-2024]

    2024

  6. [6]

    [n. d.]. Planet API Reference. https://developers.planet.com/docs/basemaps/reference/. (Accessed on 11/08/2024)

    work page 2024

  7. [7]

    Dhruv Agarwal, Srinivasan Iyengar, and Pankaj Kumar. 2024. PollutionMapper: Identifying Global Air Pollution Sources. ACM J. Comput. Sustain. Soc. 2, 1, Article 7 (Jan. 2024), 23 pages. https://doi.org/10.1145/3617129

    work page doi:10.1145/3617129 2024

  8. [8]

    European Space Agency. 2017. Sentinel-5P. https://sentinel.esa.int/web/sentinel/missions/sentinel-5p Accessed: 2025-04-01

    work page 2017

Show all 85 references
  1. [9]

    Muhammad Nauman Ahmad, Leon JL van den Berg, Hamid Ullah Shah, Tariq Masood, Patrick Büker, Lisa Emberson, and Mike Ashmore. 2012. Hydrogen fluoride damage to vegetation from peri-urban brick kilns in Asia: a growing but unrecognised problem? Environmental pollution 162 (2012)...

    2012

  2. [10]

    Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, and Stefano Ermon. 2020. Generating interpretable poverty maps using object detection in satellite images. arXiv preprint arXiv:2002.01612 (2020)

    2020 arXiv

  3. [11]

    Central Pollution Control Board. 2019. Central Pollution Control Board. https://cpcb.nic.in/National-Air-Quality-Index/. [Online; accessed 02-December-2024]

    2019

  4. [12]

    Doreen S Boyd, Bethany Jackson, Jessica Wardlaw, Giles M Foody, Stuart Marsh, and Kevin Bales. 2018. Slavery from space: Demonstrating the role for satellite remote sensing to inform evidence-based action related to UN SDG number 8. ISPRS journal of photogrammetry and remote s...

    2018

  5. [13]

    Doreen S Boyd, Bertrand Perrat, Xiaodong Li, Bethany Jackson, Todd Landman, Feng Ling, Kevin Bales, Austin Choi-Fitzpatrick, James Goulding, Stuart Marsh, et al. 2021. Informing action for United Nations SDG target 8.7 and interdependent SDGs: Examining modern slavery from spa...

    2021

  6. [14]

    Marshall Burke, Anne Driscoll, David B Lobell, and Stefano Ermon. 2021. Using satellite imagery to understand and promote sustainable development. Science 371, 6535 (2021), eabe8628

    2021

  7. [15]

    camx. 2019. camx. https://www.camx.com/. [Online; accessed 02-December-2024]

    2019

  8. [16]

    Weiyu Cheng, Yanyan Shen, Yanmin Zhu, and Linpeng Huang. 2018. A neural attention model for urban air quality inference: Learning the weights of monitoring stations. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32

    2018

  9. [17]

    Jessica D DeWitt, Peter G Chirico, Marissa A Alessi, and Kathleen M Boston. 2021. Remote sensing inventory and geospatial analysis of brick kilns and clay quarrying in Kabul, Afghanistan. Minerals 11, 3 (2021), 296

    2021

  10. [18]

    Energy Policy Institute at the University of Chicago (EPIC). 2024. About the Air Quality Life Index (AQLI). https://aqli.epic.uchicago.edu/about/ Accessed: 2024-11-26

    2024

  11. [19]

    Esri. 2024. ArcGIS Geographic Information System. https://www.esri.com/en-us/arcgis/about-arcgis/overview Accessed: 2024-11-27

    2024

  12. [20]

    Sarath Guttikunda. 2008. Estimating health impacts of urban air pollution. In SIM-air Working Paper Series. Vol. 6. New Delhi, India, 2008

    2008

  13. [21]

    Sarath Guttikunda. 2009. Impact analysis of brick kilns on the air quality in Dhaka, Bangladesh. SIM-air working paper series (2009), 234

    2009

  14. [22]

    Sarath Guttikunda, Nishadh Ka, Tanushree Ganguly, and Puja Jawahar. 2023. Plugging the ambient air monitoring gaps in India’s national clean air programme (NCAP) airsheds. Atmospheric Environment 301 (2023), 119712. https://doi.org/10.1016/j.atmosenv.2023.119712

    2023

  15. [23]

    Sarath K Guttikunda, Bilkis A Begum, and Zia Wadud. 2013. Particulate pollution from brick kiln clusters in the Greater Dhaka region, Bangladesh. Air Quality, Atmosphere & Health 6 (2013), 357–365

    2013

  16. [24]

    Sarath K Guttikunda and Giuseppe Calori. 2013. A GIS based emissions inventory at 1 km× 1 km spatial resolution for air pollution analysis in Delhi, India. Atmospheric Environment 67 (2013), 101–111. Manuscript submitted to ACM Space to Policy 27

    2013

  17. [25]

    Sarath K Guttikunda and Rahul Goel. 2013. Health impacts of particulate pollution in a megacity—Delhi, India. Environmental Development 6 (2013), 8–20

    2013

  18. [26]

    Sarath K Guttikunda, Rahul Goel, and Pallavi Pant. 2014. Nature of air pollution, emission sources, and management in the Indian cities.Atmospheric environment 95 (2014), 501–510

    2014

  19. [27]

    Sarath K Guttikunda and Puja Jawahar. 2014. Atmospheric emissions and pollution from the coal-fired thermal power plants in India. Atmospheric Environment 92 (2014), 449–460

    2014

  20. [28]

    Sarath K Guttikunda, KA Nishadh, Sudhir Gota, Pratima Singh, Arijit Chanda, Puja Jawahar, and Jai Asundi. 2019. Air quality, emissions, and source contributions analysis for the Greater Bengaluru region of India. Atmospheric Pollution Research 10, 3 (2019), 941–953

    2019

  21. [29]

    Guttikunda, K.A

    Sarath K. Guttikunda, K.A. Nishadh, and Puja Jawahar. 2019. Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Climate 27 (2019), 124–141. https://doi.org/10.1016/j.uclim.2018.11.005

    2019 doi

  22. [30]

    Muhammad Suleman Ali Hamdani, Khizer Zakir, Neetu Kushwaha, Syeda Eman Fatima, and Hassan Aftab Sheikh. 2024. Brick Kiln Dataset for Pakistan’s IGP Region Using AI. arXiv preprint arXiv:2412.00052 (2024)

    2024 arXiv

  23. [31]

    Jiaming Han, Jian Ding, Nan Xue, and Gui-Song Xia. 2021. Redet: A rotation-equivariant detector for aerial object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2786–2795

    2021

  24. [32]

    Shama E Haque, Minhaz M Shahriar, Nazmun Nahar, and Md Sazzadul Haque. 2022. Impact of brick kiln emissions on soil quality: A case study of Ashulia brick kiln cluster, Bangladesh. Environmental Challenges 9 (2022), 100640

    2022

  25. [33]

    Junfeng Hu, Yuxuan Liang, Zhencheng Fan, Hongyang Chen, Yu Zheng, and Roger Zimmermann. 2023. Graph Neural Processes for Spatio-Temporal Extrapolation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

    2023

  26. [34]

    Syed Imaduddin, Yusuf Ahmed Khan, Khushboo Mirza, and BK Bhadra. 2023. Detection of Brick Kilns Using Multi-Spectral Bands of Sentinel-2 Imagery. In 2023 International Conference on Artificial Intelligence and Smart Communication (AISC). IEEE, 496–503

    2023

  27. [35]

    IQAir. [n. d.]. World’s Most Polluted Cities - 2024. https://www.iqair.com/in-en/world-most-polluted-cities. Accessed: 2024-11-26

    2024

  28. [36]

    Glenn Jocher, Jing Qiu, and Ayush Chaurasia. 2023. Ultralytics YOLO. Ultralytics. https://ultralytics.com

    2023

  29. [37]

    T Kanabkaew and K Buasing. 2015. Assessment of air pollution concentrations from brick kilns using an atmospheric dispersion model. WIT Trans. Ecol. Environ 198 (2015), 27–37

    2015

  30. [38]

    Planet Labs. [n. d.]. Education and Research Program. https://www.planet.com/industries/education-and-research/. [Accessed 08-11-2024]

    2024

  31. [39]

    Hoang Anh Le and Nguyen Thi Kim Oanh. 2010. Integrated assessment of brick kiln emission impacts on air quality. Environmental monitoring and assessment 171 (2010), 381–394

    2010

  32. [40]

    Lebakula, J

    V. Lebakula, J. Epting, J. Moehl, C. Stipek, D. Adams, A. Reith, J. Kaufman, J. Gonzales, B. Reynolds, S. Basford, A. Martin, W. Buck, A. Faxon, A. Cunningham, A. Roy, Z. Barbose, J. Massaro, S. Walters, C. Woody, and M. Urban. 2024. LandScan Silver Edition [Data set]. https:/...

    2024

  33. [41]

    Jihyeon Lee, Nina R Brooks, Fahim Tajwar, Marshall Burke, Stefano Ermon, David B Lobell, Debashish Biswas, and Stephen P Luby. 2021. Scalable deep learning to identify brick kilns and aid regulatory capacity. Proceedings of the National Academy of Sciences 118, 17 (2021), e2018863118

    2021

  34. [42]

    Xiaodong Li, Giles M Foody, Doreen S Boyd, and Feng Ling. 2019. Aging brick kilns in the asian brick belt using a long time series of Landsat sensor data to inform the study of modern day slavery. InIGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. I...

    2019

  35. [43]

    Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, and Roger Zimmermann. 2023. Airformer: Predicting nationwide air quality in china with transformers. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 14329–14337

    2023

  36. [44]

    Anthony Lin. 2019. Binary search algorithm. WikiJournal of Science (2019). https://api.semanticscholar.org/CorpusID:62128693

    2019

  37. [45]

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proce...

    2014

  38. [46]

    Zikun Liu, Liu Yuan, Lubin Weng, and Yiping Yang. 2017. A high resolution optical satellite image dataset for ship recognition and some new baselines. In International conference on pattern recognition applications and methods, Vol. 2. SciTePress, 324–331

    2017

  39. [47]

    Greentech Knowledge Solutions Pvt Ltd. 2012. Evaluating Energy Conservation Potential of Brick Production in India. Prepared for SAARC Energy Centre

    2012

  40. [48]

    Sandro Meier. 2023. Advancing the quantification of CO2 and NOx emissions from power plants using Sentinel-5P observations: A novel approach to address NOx chemistry in plumes

    2023

  41. [49]

    Prakhar Misra, Ryoichi Imasu, Sachiko Hayashida, Ardhi Adhary Arbain, Ram Avtar, and Wataru Takeuchi. 2020. Mapping brick kilns to support environmental impact studies around Delhi using Sentinel-2. ISPRS International Journal of Geo-Information 9, 9 (2020), 544

    2020

  42. [50]

    Rishabh Mondal, Zeel B Patel, Vannsh Jani, and Nipun Batra. 2024. Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India. arXiv preprint arXiv:2402.13796 (2024)

    2024 arXiv

  43. [51]

    Usman Nazir, Numan Khurshid, Muhammad Ahmed Bhimra, and Murtaza Taj. 2019. Tiny-Inception-ResNet-v2: Using deep learning for eliminating bonded labors of brick kilns in South Asia. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 39–43

    2019

  44. [52]

    Usman Nazir, Usman Khalid Mian, Muhammad Usman Sohail, Murtaza Taj, and Momin Uppal. 2020. Kiln-net: A gated neural network for detection of brick kilns in South Asia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020), 3251–3262. Manusc...

    2020

  45. [53]

    Laura Nicolaou, Fiona Sylvies, Isabel Veloso, Katherine Lord, Ram K Chandyo, Arun K Sharma, Laxman P Shrestha, David L Parker, Steven M Thygerson, Peter F DeCarlo, et al. 2024. Brick kiln pollution and its impact on health: A systematic review and meta-analysis. Environmental ...

    2024

  46. [54]

    OpenStreetMap contributors. 2024. OpenStreetMap. https://www.openstreetmap.org Accessed: 2024-11-27

    2024

  47. [55]

    OpenStreetMap contributors. 2024. OpenStreetMap Data Downloaded from Geofabrik. https://download.geofabrik.de/ Accessed from Geofabrik [https://download.geofabrik.de/] on [date]

    2024

  48. [56]

    and United Nations Development Programme

    International Labour Organisation. and United Nations Development Programme. 1984. Small-scale brickmaking. ILO, Geneva

    1984

  49. [57]

    Zeel B Patel, Palak Purohit, Harsh M Patel, Shivam Sahni, and Nipun Batra. 2022. Accurate and Scalable Gaussian Processes for Fine-Grained Air Quality Inference. Proceedings of the AAAI Conference on Artificial Intelligence 36, 11 (Jun. 2022), 12080–12088. https://doi.org/10.1...

    2022 doi

  50. [58]

    Arati Paul, Soumya Bandyopadhyay, and Uday Raj. 2022. Brick kiln detection in remote sensing imagery using deep neural network and change analysis. Spatial Information Research 30, 5 (2022), 607–616

    2022

  51. [59]

    United Nations Development Programme. 2023. GeoAI for Brick Kilns in Bihar: Learnings and Recommendations. https://www.undp.org/sites/g/ files/zskgke326/files/2023-12/geoai_for_brick_kilns_v8_web_pages_002_0.pdf Accessed: 31 March 2025

    2023

  52. [60]

    Uma Rajarathnam, Vasudev Athalye, Santhosh Ragavan, Sameer Maithel, Dheeraj Lalchandani, Sonal Kumar, Ellen Baum, Cheryl Weyant, and Tami Bond. 2014. Assessment of air pollutant emissions from brick kilns. Atmospheric Environment 98 (2014), 549–553. https://doi.org/10.1016/j. ...

    2014 doi

  53. [61]

    Ali Raza and Zulfiqar Ali. 2021. Impact of air pollution generated by brick kilns on the pulmonary health of workers.Journal of Health and Pollution 11, 31 (2021), 210906

    2021

  54. [62]

    Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779–788

    2016

  55. [64]

    W. C. Skamarock, J. B. Klemp, J. Dudhia, D. O. Gill, Z. Liu, J. Berner, W. Wang, J. G. Powers, M. G. Duda, D. M. Barker, and X.-Y. Huang. 2019.A Description of the Advanced Research WRF Version 4. NCAR Tech. Note NCAR/TN-556+STR. NCAR. 145 pages. https://doi.org/10.5065/1dfh-6p97

    2019 doi

  56. [65]

    BM Skinder, AK Pandit, AQ Sheikh, and BA Ganai. 2014. Brick kilns: cause of atmospheric pollution. J Pollut Eff Cont 2, 112 (2014), 3

    2014

  57. [66]

    Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander Alemi. 2017. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI conference on artificial intelligence, Vol. 31

    2017

  58. [67]

    Rosheen Tahir, Muhammad Shaaf Imran, Sidra Minhas, Nosheen Sabahat, Sardar Haider Waseem Ilyas, and Haider Raza Gadi. 2021. Brick kiln detection and localization using deep learning techniques. In 2021 International Conference on Artificial Intelligence (ICAI). IEEE, 37–43

    2021

  59. [68]

    Prakash Thangavel, Duckshin Park, and Young-Chul Lee. 2022. Recent insights into particulate matter (PM2. 5)-mediated toxicity in humans: an overview. International journal of environmental research and public health 19, 12 (2022), 7511

    2022

  60. [69]

    Kushal Tibrewal, Chandra Venkataraman, Harish Phuleria, Veena Joshi, Sameer Maithel, Anand Damle, Anurag Gupta, Pradnya Lokhande, Shahadev Rabha, Binoy K Saikia, et al. 2023. Reconciliation of energy use disparities in brick production in India.Nature Sustainability 6, 10 (202...

    2023

  61. [70]

    Plamen Trenchev, Maria Dimitrova, and Daniela Avetisyan. 2023. Huge CH4, NO2 and CO Emissions from Coal Mines in the Kuznetsk Basin (Russia) Detected by Sentinel-5P. Remote Sensing 15, 6 (2023), 1590

    2023

  62. [71]

    UNEP. 2019. Emissions Gap Report 2019. , 11 pages

    2019

  63. [72]

    Uttar Pradesh Government. 2012. The Uttar Pradesh Brick Kilns (Siting Criteria for Establishment) Rules, 2012. https://upload.indiacode.nic.in/ showfile?actid=AC_UP_88_464_00007_00007_1616154959565&type=rule&filename=final_bricklin_rules_pdf.pdf Accessed: April 1, 2025

    2012

  64. [73]

    Uttar Pradesh Pollution Control Board (UPPCB). [n. d.]. Guidelines for brick kiln Industry. http://uppcb.com/pdf/brick_280519.pdf. Accessed: 2024-11-26

    2024

  65. [74]

    Uttar Pradesh Pollution Control Board (UPPCB). 2023. UPPCB Report on Brick Kilns - March 2023. http://www.indiaenvironmentportal.org.in/files/ file/UPPCB-report-brick-kilns-March-2023.pdf Accessed: 2024-11-04

    2023

  66. [75]

    Bengal Brick Field Owners Association vs West Bengal Pollution Control Board. 2022. Bengal Brick Field Owners Association vs. West Bengal Pollution Control Board. https://indiankanoon.org/doc/104363701/ Accessed: 31 March 2025

    2022

  67. [76]

    Qiongyan Wang, Yutong Xia, Siru ZHong, Weichuang Li, Yuankai Wu, Shifen Cheng, Junbo Zhang, Yu Zheng, and Yuxuan Liang. 2025. AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks. arXiv preprint arXiv:2501.13141 (2025)

    2025 arXiv

  68. [77]

    Cheryl Weyant, Vasudev Athalye, Santhosh Ragavan, Uma Rajarathnam, Dheeraj Lalchandani, Sameer Maithel, Ellen Baum, and Tami C Bond. 2014. Emissions from South Asian brick production. Environmental science & technology 48, 11 (2014), 6477–6483

    2014

  69. [78]

    World Health Organization. n.d.. Air Pollution in India. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and- health Accessed: 2024-11-29

    2024

  70. [79]

    WorldBank. 2020. DIRTY STACKS, HIGH STAKES: An Overview of Brick Sector in South Asia

    2020

  71. [80]

    Qiusheng Wu. 2021. Leafmap: A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. Journal of Open Source Software 6, 63 (2021), 3414. https://doi.org/10.21105/joss.03414 Manuscript submitted to ACM Space to Policy 29

    2021 doi

  72. [81]

    Yanxin Xi, Yu Liu, Tong Li, Jingtao Ding, Yunke Zhang, Sasu Tarkoma, Yong Li, and Pan Hui. 2023. A satellite imagery dataset for long-term sustainable development in united states cities. Scientific data 10, 1 (2023), 866

    2023

  73. [82]

    Gui-Song Xia, Xiang Bai, Jian Ding, Zhen Zhu, Serge Belongie, Jiebo Luo, Mihai Datcu, Marcello Pelillo, and Liangpei Zhang. 2018. DOTA: A large-scale dataset for object detection in aerial images. In Proceedings of the IEEE conference on computer vision and pattern recognition...

    2018

  74. [83]

    Liuzhen Xie, Qixiang Xu, and Ruidong He. 2021. Atmospheric pollution impact assessment of brick and tile industry: a case study of Xinmi City in Zhengzhou, China. Sustainability 13, 4 (2021), 2414

    2021

  75. [84]

    Xingxing Xie, Gong Cheng, Jiabao Wang, Xiwen Yao, and Junwei Han. 2021. Oriented R-CNN for object detection. In Proceedings of the IEEE/CVF international conference on computer vision. 3520–3529

    2021

  76. [85]

    Xue Yang and Junchi Yan. 2022. On the arbitrary-oriented object detection: Classification based approaches revisited. International Journal of Computer Vision 130, 5 (2022), 1340–1365

    2022

  77. [86]

    Christopher Yeh, Anthony Perez, Anne Driscoll, George Azzari, Zhongyi Tang, David Lobell, Stefano Ermon, and Marshall Burke. 2020. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature communications 11, 1 (2020), 2583...

    2020

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.