Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Brick Kiln Dataset for Pakistan's IGP Region Using AI

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A two-stage AI pipeline confirms 11,277 brick kilns across Pakistan's IGP, labeling each by type and estimated emissions.

desk verdict Useful public dataset of brick kiln locations for Pakistan's IGP, but the 11,277 count is an unvalidated lower bound and the per-kiln emissions are partly circular. read the letter →

arxiv 2412.00052 v1 pith:6WXNA7SO submitted 2024-11-24 cs.CV

Muhammad Suleman Ali Hamdani , Khizer Zakir , Neetu Kushwaha , Syeda Eman Fatima , Hassan Aftab Sheikh This is my paper · ORCID
classification cs.CV
keywords brickkilndetectionsatelliteimagerySentinel-2YOLOv8RandomForestIndo-GangeticPlainairpollutionemissioninventory
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

The paper establishes that a two-stage AI cascade can produce a public, asset-level inventory of a major unregulated pollution source at national scale. A Random Forest classifier on free Sentinel-2 imagery first screens the entire Pakistani Indo-Gangetic Plain and proposes 20,873 candidate kiln points; a YOLOv8 object detector then examines high-resolution Google Maps imagery at those points, confirming 11,277 brick kilns and labeling each as Fixed Chimney Bull's Trench Kiln or Zigzag. The dataset adds per-kiln daily and seasonal emission estimates for PM10, PM2.5, SOx, and NOx, along with proximity to schools, hospitals, and populated areas. If the inventory is accurate, it gives Pakistani regulators the first transparent, large-scale map of where brick kilns operate and how much pollution they likely emit, which is a prerequisite for enforcement and exposure studies in one of the world's most polluted regions.

What carries the argument

The load-bearing mechanism is the two-stage cascade. Stage one is a pixel-wise Random Forest classifier trained on manually annotated Sentinel-2 RGB tiles covering ten land-cover classes (25,678 brick-kiln pixels), run on 1×1 km grids and followed by a post-processing chain: binary masking, removal of isolated pixels, morphological closing, clustering into kiln centroids, elimination of redundant centroids within 20 m, and a cap of fifteen centroids per square kilometer. Stage two downloads high-resolution imagery from the Google Maps Static API for each surviving candidate point (zoom 17, scale 2, 1280×1280 pixels), runs a YOLOv8n detector trained on 375 FCBK and 295 Zigzag annotated kilns, merges detections within 12 m to avoid double-counting, and converts bounding-box centers to geographic coordinates via per-pixel latitude and longitude offsets. The design rationale is that high-resolution queries are spent only on the relatively small set of candidates (about 60,000 images total), keeping cost low while the YOLO stage both validates and classifies.

What would settle it

An independent exhaustive survey of a stratified random sample of 1×1 km grid cells across Sindh and southern Punjab—using either wall-to-wall very-high-resolution imagery or nighttime thermal anomaly detection—that finds a substantial number of brick kilns absent from the 11,277-point dataset would falsify the claim that the inventory is near-complete.

Watch

Extended reading notes

Core claim

The central discovery is that a deliberately imbalanced cascade—cheap and recall-oriented at low resolution, selective and precision-oriented at high resolution—can enumerate and type a distributed polluting asset class across a 518,000 square-kilometer study region. The authors report that the Random Forest stage alone produces 20,873 candidate points after post-processing, and the YOLOv8 stage confirms 11,277 of them: 6,706 in northern Punjab and Khyber Pakhtunkhwa, 4,271 in Sindh and southern Punjab, and 301 in areas where Sentinel-2 imagery was unavailable. Each confirmed kiln is assigned a type (FCBK or Zigzag) and an emission profile computed from standard emission factors, an assumed 3 kg per brick, and 215 operating days, yielding per-kiln daily figures such as 351.18 kg PM10 and 246.19 kg PM2.5. The paper presents this as the most comprehensive public brick kiln inventory for Pakistan to date.

Load-bearing premise

The dataset's completeness rests on the assumption that the pipeline's post-processing and deduplication steps do not discard true brick kilns, so the 11,277 confirmed detections are a near-complete national count rather than a floor; the paper explicitly notes that verification of false negatives (undetected kilns) was not studied.

Editorial extensions

If this is right

  • Regulators can overlay the 11,277 geolocated points on official kiln registries to identify unregistered operations and prioritize inspections.
  • With kiln-type labels, authorities can quantify how many kilns are still Fixed Chimney Bull's Trench Kilns versus cleaner Zigzag designs and estimate the emissions reduction from technology conversion.
  • The per-kiln emission estimates and proximity-to-sensitive-sites fields allow researchers to rank kilns by local health exposure risk for schools, hospitals, and dense populations.
  • The open-source code and model weights mean the pipeline can be re-run on updated or expanded imagery, making the dataset refreshable and extendable to other IGP regions.
  • The staged design limits high-resolution imagery queries (about 60,000 downloaded images) compared to a full high-resolution survey, keeping the update cost manageable.

Reading between the lines

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

  • Editorial inference: if the false-negative rate is non-negligible, the 11,277 count is a floor; combining the dataset with thermal anomaly imagery would test recall.
  • Editorial inference: the 15-per-square-kilometer cap and the 12–20 m merge radii imply a minimum spacing between kilns, so dense brick clusters with kilns closer than these thresholds may be undercounted, and users should treat high-density counts as conservative.
  • Editorial inference: because the emission factors are from literature and assume a uniform 3 kg per brick and 215 operating days, the per-kiln daily values are planning-level estimates rather than measured emissions; field monitoring at a handful of kilns would calibrate them.
  • Editorial inference: the same cascade could transfer to other point-source polluters or other IGP countries, but the annotation burden of about 670 kilns suggests each new geography needs its own small labeled set.
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 a two-stage AI pipeline for producing a public brick kiln inventory for Pakistan's Indo-Gangetic Plain. A Random Forest classifier on Sentinel-2 RGB imagery generates candidate kiln locations, which are post-processed (morphological closing, centroid clustering, 20 m duplicate removal, 15 per km² cap) to yield 20,873 candidates. A YOLOv8 model on Google Maps Static API imagery then validates these candidates and classifies kilns as FCBK or ZigZag, producing a final dataset of 11,277 kilns (6,706 in Northern Punjab/KP, 4,271 in Sindh/Southern Punjab, 301 in areas without Sentinel-2 coverage). The dataset includes per-kiln emission estimates for PM10, PM2.5, SOx, and NOx, computed from assumed national brick production, an assumed 65% share for the study area, an assumed 215 operational days, and the detected kiln count. The authors release code, model weights, and the dataset on Zenodo and GitHub.

Significance. If validated, this would be a valuable open resource for air-quality and regulatory research in a data-scarce region, and the two-stage low-/high-resolution pipeline is a sensible cost-saving design. The paper's strengths include detailed pipeline descriptions, region-wise reporting of intermediate results, and public release of code, weights, and data. However, the central quantitative claim—that the dataset is a near-complete inventory of 'around 11,000 kilns'—rests on an unmeasured recall. The paper explicitly states that false negatives were not studied, and the post-processing operations can only remove detections. The per-kiln emission estimates are also constructed by dividing an assumed production total by the detected kiln count, making them inversely dependent on the very number they are supposed to characterize. These issues affect the dataset's primary claims and its practical utility for regulatory enforcement.

major comments (4)
  1. [Methods, Phase Two Inference; Supplementary Section III] The completeness claim is not supported by the validation design. YOLO is run only on the 20,873 Random Forest candidate points plus regions lacking Sentinel-2 coverage; it cannot recover kilns discarded by the Random Forest threshold, the 20 m merging step, or the 15-per-km² cap. Supplementary Section III states verbatim that 'the verification of false negatives—undetected brick kilns—was not studied.' Therefore 11,277 is a confirmed lower bound, not a validated near-complete count. The authors should either provide an independent kiln-level recall assessment (for example, a visual census of randomly sampled 1×1 km tiles, including dense clusters and areas with high false-positive rates) or explicitly reframe the dataset as a minimum-count inventory.
  2. [Supplementary Section II] The per-kiln emission estimates are circular with respect to the central detection result. The calculation 'Daily Production per Kiln = 29.25 billion bricks / (11,277 × 215)' makes every downstream emission value inversely proportional to the detected kiln count, because the denominator is the pipeline's own output, not an independently measured quantity. If the true count is higher or lower, all per-kiln PM10, PM2.5, SOx, and NOx estimates change accordingly. The assumptions of 45 billion national bricks, 65% study-area share, and 215 working days are also unvalidated. These estimates should be presented as scenario-based sensitivity calculations, with explicit ranges over the count and activity assumptions, rather than as validated per-kiln emission rates.
  3. [Phase Two: YOLO] The YOLO validation is too small and too closely tied to the training distribution to support the final count. The entire YOLO annotation set is approximately 670 kilns (375 FCBK and 295 ZigZag), with a 10% test split, and the reported mAP values are computed on that small in-distribution test set. No evaluation is reported on an independent sample of high-resolution imagery over the full study area or on kiln-like objects missed by the Random Forest stage. Consequently, the reported 95% mAP@50 does not quantify the number of true kilns that the two-stage pipeline misses. Please report per-region YOLO precision and recall on a held-out sample that is independent of the Random Forest candidate list.
  4. [Technical Validation] The statement that 'the final results... were manually verified by experts' is not quantified. No sample size, verification protocol, inter-annotator agreement, or region-wise breakdown is given, so a reader cannot assess how much confidence the manual verification adds. Please specify how many kilns were checked, by how many annotators, and with what agreement; alternatively, remove this claim or replace it with a measurable validation metric.
minor comments (5)
  1. [Table 2] The regional counts in Table 2 do not sum to the stated total: 6,706 + 4,271 + 301 = 11,278, not 11,277. Please correct this arithmetic inconsistency.
  2. [Data Records] The units for emission estimates are described inconsistently: the Data Records section says 'kilograms per day,' Supplementary Section II's table shows both kg/day and kg/year, and the Usage Notes state 'grams per kilogram (g/kg) of bricks produced.' Please harmonize units and clarify whether the published attributes are daily, seasonal, or per-mass emission factors.
  3. [Usage Notes] Usage Notes state the CSV has three columns (type, latitude, longitude), while Data Records describes a CSV with multiple attributes including emission estimates and proximity metrics. Please reconcile these two descriptions of the published dataset.
  4. [Figure 1 caption] The caption reads 'Indian-Gangetic Plain' but the standard term used elsewhere in the paper is 'Indo-Gangetic Plain.' Please fix this inconsistency.
  5. [Introduction] The phrase 'penetrate deep into the lungs, circular system' should read 'circulatory system,' and 'greenhouse has emissions' should read 'greenhouse gas emissions.' These typos should be corrected.

Circularity Check

1 steps flagged · score 4.0 of 10

Per-kiln emission estimates are forced by an assumed national production total divided by the detected kiln count; the kiln-location pipeline itself is not circular.

  1. fitted input called prediction [Supplementary Section II, Pollutant Emissions / Seasonal Brick Production]
    "Total Seasonal Brick Production (65%) = 0.65 × 45 billion bricks = 29.25 billion bricks. Given there are 11,277 kilns in the study area, the per-day production per kiln is calculated as follows: Daily Production per Kiln = 29.25 billion bricks / (11,277 × 215) ≈ 12,068 bricks/day. Each brick weighs approximately 3 kg, so the total daily brick weight per kiln is: Daily Brick Weight per Kiln = 12,068 × 3 = 36,204 kg/day per kiln."

    The per-kiln emission estimates are defined as E_i × (29.25e9 / (N × 215)) × 3, where N = 11,277 is the detected kiln count. Summing the seasonal emissions over all N kilns gives E_i × 29.25e9 × 3 kg, which is exactly the assumed total regional production multiplied by the emission factor. The dataset's emission outputs therefore do not independently estimate the industry contribution; they merely redistribute the externally assumed 29.25-billion-brick total across the detected kilns. Any error in the kiln count is absorbed into the per-kiln denominator, so the per-kiln values are forced by construction rather than measured or independently predicted.

full rationale

The location-detection claim—20,873 Random Forest candidates refined by YOLOv8 to 11,277 kilns—is not circular: the YOLO stage is applied to candidate points and produces new confirmations, and the paper does not use the emission estimates to derive the kiln count. No self-citation chain is load-bearing. The genuine circular step is confined to the emission estimates in Supplementary Section II: the per-kiln production denominator is constructed from the assumed national total divided by the detected count, so aggregating the per-kiln outputs returns the input assumption. This makes the emission numbers a repackaging of the 29.25-billion-brick assumption rather than an independent inventory-based result. The acknowledged lack of false-negative verification is a completeness limitation, not a circularity, and is already stated in Supplementary Section III.

Assumptions & free parameters 7 free parameters · 7 assumptions · 0 invented entities

The central claims rest on several hand-chosen thresholds and domain assumptions: a 15 kilns/km² cap, merge radii of 20 m and 12 m, a 0.45 km² grouping area, and an assumed operating year of 215 days. The emission estimates rely on an assumed 45 billion national bricks and a 65% study-area share, neither of which is cited. The detection assumes Sentinel-2 RGB and Google Static Maps imagery are sufficient and that the YOLO model trained on roughly 670 examples generalizes. No new physical entities are introduced.

free parameters (7)
  • Kiln density cap = 15 per square kilometer
    Hand-chosen upper bound used in post-processing to eliminate false positives; may exclude real kilns in dense clusters. Introduced in 'Post Processing and Geolocating'.
  • Duplicate removal radius (Random Forest) = 20 meters
    Points within 20 m are considered redundant and merged; affects final count.
  • Duplicate removal radius (YOLO) = 12 meters
    Detections within 12 m of each other are counted once; affects final count.
  • Coordinate grouping radius = 0.45 km²
    Coordinates within a 0.45 km² area are grouped and a single high-resolution image is downloaded; reduces redundancy but may miss kilns near group boundaries.
  • Operational days per year = 215
    Assumed working days per kiln, excluding monsoon and smog season; used in emissions and production calculations. Stated in Supplementary Section II.
  • Share of national kilns in study area = 65%
    Assumed fraction of Pakistan's 45 billion annual bricks produced in the study region; used to compute per-kiln daily production.
  • Per-kiln daily brick production = 12,068 bricks/day
    Derived as 29.25 billion bricks divided by (11,277 kilns times 215 days); depends on the detected kiln count and assumed production share, so it is not an independent measurement.
assumptions (7)
  • domain assumption Sentinel-2 RGB bands at 10 m resolution are sufficient to visually distinguish brick kilns from other land cover classes.
    The Random Forest model is trained on only RGB bands (Supplementary Section III), and the semantic characteristics in Table 1 rely on color and shape.
  • domain assumption Brick kilns have a distinctive ovular or rectangular reddish-brown appearance in satellite imagery.
    This is the basis for both the annotation guide (Table 1) and the YOLO training labels.
  • domain assumption The Google Maps Static API imagery is geometrically accurate enough for the linear pixel-to-coordinate mapping in Equations 1 to 6.
    The paper states that images from the Google Static Maps API are geometrically flattened and assumes linear interpolation between pixel offsets and geographic coordinates.
  • domain assumption The manually annotated training labels for YOLO (375 FCBK, 295 ZigZag) are representative of kiln appearances across the entire IGP region.
    The YOLO model's validation mAP50 of 95% depends on this representativeness to generalize to the full inference region.
  • ad hoc to paper The assumed national brick production of 45 billion bricks per year and the 65% share for the study area are accurate.
    Used in Supplementary Section II to compute per-kiln emissions; no source is cited in the text for these figures.
  • ad hoc to paper Operational kilns are active in July (the imagery window) and shut down for 150 days per year.
    The 215-day operating assumption and the July 1-15 Sentinel-2 acquisition window are stated without direct evidence.
  • domain assumption Manual verification by the authors is an unbiased ground truth for the final detections.
    The Technical Validation section states that experts manually verified results but provides no metrics or inter-annotator agreement.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Brick Kiln Dataset for Pakistan's IGP Region Using AI." pith.science (2026). https://pith.science/paper/6WXNA7SO

@misc{pith2026241200052,
  author       = {Pith},
  title        = {Pith review of: Brick Kiln Dataset for Pakistan's IGP Region Using AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6WXNA7SO}},
  note         = {Machine review of arXiv:2412.00052}
}
read the original abstract

Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-Gangetic Plain is the limited air quality monitoring and lack of transparent data on pollution sources. To address this, we present a two-fold AI approach that combines low-resolution Sentinel-2 and high-resolution imagery to map brick kiln locations. Our process begins with a low-resolution analysis, followed by a post-processing step to reduce false positives, minimizing the need for extensive high-resolution imagery. This analysis initially identified 20,000 potential brick kilns, with high-resolution validation confirming around 11,000 kilns. The dataset also distinguishes between Fixed Chimney and Zigzag kilns, enabling more accurate pollution estimates for each type. Our approach demonstrates how combining satellite imagery with AI can effectively detect specific polluting sources. This dataset provides regulators with insights into brick kiln pollution, supporting interventions for unregistered kilns and actions during high pollution episodes.

Figures

Figures reproduced from arXiv: 2412.00052 by the authors.

Figure 1
Figure 1. Study Area Map - (a) The Indian-Gangetic Plain (IGP) region, [PITH_FULL_IMAGE:figures/full_fig_p020_1.png] view at source ↗
Figure 2
Figure 2. Image tiles illustrating different classes labelled for training our [PITH_FULL_IMAGE:figures/full_fig_p021_2.png] view at source ↗
Figure 3
Figure 3. Normalized Confusion Matrix for YOLOv8n. [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: a) and (b) represent two adjacent image tiles where the same kiln [PITH_FULL_IMAGE:figures/full_fig_p022_4.png]
Figure 5
Figure 5. Figure 5: a) The figure outlines the process of brick kiln detection in the [PITH_FULL_IMAGE:figures/full_fig_p023_5.png]
Figure 6
Figure 6. Figure 6: Overview of brick kiln distribution, types, and proximity to sensi [PITH_FULL_IMAGE:figures/full_fig_p024_6.png]
Figure 1
Figure 1. Figure 1: (a) Brick kilns highlighted within a 5x5 km grid. (b) Brick kilns highlighted within a 1x1 km grid. [PITH_FULL_IMAGE:figures/full_fig_p029_1.png]
Figure 2
Figure 2. Figure 2: Outlined tiles (black borders) indicate regions where Sentinel-2 imagery was unavailable [PITH_FULL_IMAGE:figures/full_fig_p029_2.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.LG 2024-12 conditional novelty 6.0 of 10

    A free-satellite YOLO pipeline detected and hand-validated 30,638 brick kilns in the Indo-Gangetic Plain and linked them to compliance, emissions, and population exposure.

Reference graph

Works this paper leans on

39 extracted references · 37 canonical work pages · cited by 1 Pith paper

  1. [1]

    A critical review of managing air pollution through airshed approach. 9

    work page

  2. [2]

    Available at: https://sentinel.esa.int/web/sentinel/missions/sentinel-2, 2023

    Sentinel-2 - missions - sentinel online - sentinel online. Available at: https://sentinel.esa.int/web/sentinel/missions/sentinel-2, 2023

    work page 2023

  3. [3]

    Assessment of long-term energy and environmental impactsofthecleanertechnologiesforbrickproduction

    Akhtar Abbas, Muhammad Bilal Sajid, Muhammad Asaad Iftikhar, Asif Hussain Khoja, Muhammad Muneeb Ahmad, Muhammad Shahid, and Kafait Ullah. Assessment of long-term energy and environmental impactsofthecleanertechnologiesforbrickproduction. Energy Reports, 7:7157–7169, 2021

    work page 2021

  4. [4]

    Air quality, pollution and sustainability trends in south asia: A population-based study.International Journal of En- vironmental Research and Public Health, 19(12), 2022

    Saima Abdul Jabbar, Laila Tul Qadar, Sulaman Ghafoor, Lubna Rasheed, Zouina Sarfraz, Azza Sarfraz, Muzna Sarfraz, Miguel Felix, and Ivan Cherrez-Ojeda. Air quality, pollution and sustainability trends in south asia: A population-based study.International Journal of En- vironmental Research and Public Health, 19(12), 2022

    work page 2022

  5. [5]

    Striving for Clean Air: Air Pollution and Public Health in South Asia

    World Bank. Striving for Clean Air: Air Pollution and Public Health in South Asia. South Asia Development Matters. World Bank, Wash- ington, DC, 2023

    work page 2023

  6. [6]

    Investigating the impact of shifting the brick kiln industry from conventional to zigzag technology for a sustainable environment.Sustainability, 15(10), 2023

    Zain Bashir, Muhammad Amjad, Syed Farhan Raza, Shafiq Ahmad, Mali Abdollahian, and Muhammad Farooq. Investigating the impact of shifting the brick kiln industry from conventional to zigzag technology for a sustainable environment.Sustainability, 15(10), 2023

    work page 2023

  7. [7]

    A comprehensive eval- uation of the association between ambient air pollution and adverse 14 health outcomes of major organ systems: a systematic review with a worldwide approach

    Jafar Bazyar, Negar Pourvakhshoori, Hamidreza Khankeh, Mehrdad Farrokhi, Vahid Delshad, and Elham Rajabi. A comprehensive eval- uation of the association between ambient air pollution and adverse 14 health outcomes of major organ systems: a systematic review with a worldwide approach. Environmental Science and Pollution Research, 26:12648–12661, 2019

    work page 2019

  8. [8]

    Pakistan, 2024

    Shahid Javed Burki and Lawrence Ziring. Pakistan, 2024. Encyclopedia Britannica, Accessed 17 September 2024

    work page 2024

Show all 39 references
  1. [9]

    Poor respiratory health is a consequence of dhaka’s polluted air: A bangladeshi perspective.Environmental Health Insights, 17:1–4, 2023

    Trishul Basak Dibya, Amrin Yeasin Proma, and Syed Masudur Rahman Dewan. Poor respiratory health is a consequence of dhaka’s polluted air: A bangladeshi perspective.Environmental Health Insights, 17:1–4, 2023

    2023

  2. [10]

    Dirty stacks, high stakes: An overview of brick sector in south asia, 2020

    Andrew Eil, Jie Li, Prajwal Baral, and Eri Saikawa. Dirty stacks, high stakes: An overview of brick sector in south asia, 2020. World Bank Document

    2020

  3. [11]

    Adistrict-level emissioninventoryofanthropogenicpm 2.5 fromtheprimarysourcesover the indian indo gangetic plain: Identification of the emission hotspots

    Abhinandan Ghosh, Pavan Kumar Nagar, Jyoti Maddhesia, Mukesh Sharma, SahirAzmi, BrajeshSingh, andMonamiDutta. Adistrict-level emissioninventoryofanthropogenicpm 2.5 fromtheprimarysourcesover the indian indo gangetic plain: Identification of the emission hotspots. Science of Th...

    2024

  4. [12]

    Bangladesh fact sheet 2023,

    Michael Greenstone and Qing (Claire) Fan. Bangladesh fact sheet 2023,

    2023

  5. [13]

    India fact sheet 2023, 2023

    Michael Greenstone and Qing (Claire) Fan. India fact sheet 2023, 2023. AIR QUALITY LIFE INDEX® (AQLI)

    2023

  6. [14]

    Pakistan fact sheet, 2023

    Michael Greenstone and Qing (Claire) Fan. Pakistan fact sheet, 2023. AIR QUALITY LIFE INDEX® (AQLI)

    2023

  7. [15]

    Juanmei Guo, Guorong Chai, Xuping Song, Xu Hui, Zhihong Li, Xi- aowen Feng, and Kehu Yang. Long-term exposure to particulate matter on cardiovascular and respiratory diseases in low- and middle-income countries: A systematic review and meta-analysis.Frontiers in Public Health,...

    2023

  8. [16]

    World’s most polluted cities 2023, 2023

    IQAir. World’s most polluted cities 2023, 2023. Accessed: 2023-12-09

    2023

  9. [17]

    Ultralytics YOLO, Jan- uary 2023

    Glenn Jocher, Jing Qiu, and Ayush Chaurasia. Ultralytics YOLO, Jan- uary 2023

    2023

  10. [18]

    Climate change and its impact on south asian agriculture

    Naveen Kumar and Ayushi Chaurasia. Climate change and its impact on south asian agriculture. In Sustainable Agriculture in the Era of Climate Change, pages 879–890. Springer, 2021. 15

    2021

  11. [19]

    Brooks, Fahim Tajwar, Marshall Burke, Ste- fano Ermon, David B

    Jihyeon Lee, Nina R. Brooks, Fahim Tajwar, Marshall Burke, Ste- fano Ermon, David B. Lobell, Debashish Biswas, and Stephen P. Luby. Scalable deep learning to identify brick kilns and aid regulatory ca- pacity. Proceedings of the National Academy of Sciences (PNAS), 118(17):e20...

    2021

  12. [20]

    Environmentalregulation, pollution and the informal economy

    UmmadMazharandCeyhunElgin. Environmentalregulation, pollution and the informal economy. SBP Research Bulletin, 9(1):62–81, 2013. Accessed on: 2024-10-10

    2013

  13. [21]

    Scalable methods for brick kiln detection and compliance monitoring from satel- lite imagery: A deployment case study in india

    Rishabh Mondal, Zeel B Patel, Vannsh Jani, and Nipun Batra. Scalable methods for brick kiln detection and compliance monitoring from satel- lite imagery: A deployment case study in india. arXiv preprint, 2024. arXiv:2402.13796v1

    2024 arXiv

  14. [22]

    Mitigat- ing climate and health impact of small-scale kiln industry using multi- spectral classifier and deep learning, 2023

    Usman Nazir, Murtaza Taj, Momin Uppal, and Sara Khalid. Mitigat- ing climate and health impact of small-scale kiln industry using multi- spectral classifier and deep learning, 2023

    2023

  15. [23]

    Brick kiln detec- tion in remote sensing imagery using deep neural network and change analysis

    Arati Paul, Soumya Bandyopadhyay, and Uday Raj. Brick kiln detec- tion in remote sensing imagery using deep neural network and change analysis. Spatial Information Research, 30:607–616, 2022

    2022

  16. [24]

    Pryor, Lachlan O

    Jack T. Pryor, Lachlan O. Cowley, and Stephanie E. Simonds. The physiological effects of air pollution: Particulate matter, physiology and disease. Frontiers in Public Health, 10, 2022

    2022

  17. [25]

    Md Masudur Rahman, Shuo Wang, Weixiong Zhao, Arfan Arshad, Wei- junZhang, andCenlinHe. Comprehensiveevaluationofspatialdistribu- tion and temporal trend of no2, so2 and aod using satellite observations over south and east asia from 2011 to 2021.Remote Sensing, 15(20), 2023

    2011

  18. [26]

    Masud Rana, Mastura Mahmud, Munjurul Hannan Khan, Bjarne Sivertsen, and Norela Sulaiman

    Md. Masud Rana, Mastura Mahmud, Munjurul Hannan Khan, Bjarne Sivertsen, and Norela Sulaiman. Investigating incursion of transbound- ary pollution into the atmosphere of dhaka, bangladesh.Advances in Meteorology, 2016:1–11, 2016

    2016

  19. [27]

    Attri, and Suman Mor

    Khaiwal Ravindra, Tanbir Singh, Vinayak Sinha, Baerbel Sinha, Suren- der Paul, S.D. Attri, and Suman Mor. Appraisal of regional haze event and its relationship with pm2.5 concentration, crop residue burning and meteorology in chandigarh, india.Chemosphere, 273:128562, 2021. 16

    2021

  20. [28]

    Sarofim, and Michael Kolian

    Brannon Seay, Anna Adetona, Natasha Sadoff, Marcus C. Sarofim, and Michael Kolian. Impact of south asian brick kiln emission miti- gation strategies on select pollutants and near-term arctic temperature responses. Environmental Research Communications, 3(6):061004, jun 2021

    2021

  21. [29]

    Urban- ization and regional air pollution across south asian developing coun- tries: A nationwide land use regression for ambient PM2.5 assessment in pakistan

    Yuan Shi, Muhammad Bilal, Hung Chak Ho, and Abid Omar. Urban- ization and regional air pollution across south asian developing coun- tries: A nationwide land use regression for ambient PM2.5 assessment in pakistan. Environmental Pollution, 266:115145, 2020

    2020

  22. [30]

    Goryacheva, and Pradyumna K

    Anushi Shukla, Neha Bunkar, Rajat Kumar, Arpit Bhargava, Rajnarayan Tiwari, Koel Chaudhury, Irina Y. Goryacheva, and Pradyumna K. Mishra. Air pollution associated epigenetic modifica- tions: Transgenerational inheritance and underlying molecular mecha- nisms. Science of The To...

    2019

  23. [31]

    Industrialization, energy consumption, and environmental pollution: Evidence from south asia

    Sumaira and Hafiz Muhammad Abubakar Siddique. Industrialization, energy consumption, and environmental pollution: Evidence from south asia. Environmental Science and Pollution Research, 30:4094–4102, 2022

    2022

  24. [32]

    Brick kiln detection and localization using deep learning techniques

    Rosheen Tahir, Muhammad Shaaf Imran, Sidra Minhas, Nosheen Saba- hat, Sardar Haider Waseem Ilyas, and Haider Raza Gadi. Brick kiln detection and localization using deep learning techniques. In2021 In- ternational Conference on Artificial Intelligence (ICAI), pages 37–43, 2021

    2021

  25. [33]

    Scoping study for south asia air pollution, 2019

    The Energy and Resources Institute (TERI). Scoping study for south asia air pollution, 2019. Commissioned by the South Asia Research Hub, Department for International Development, Government of UK

    2019

  26. [34]

    Saikia, Sayantee Roy, Gazala Habib, Shubham Rathi, Anubha Goel, Sakshi Ahlawat, Tuhin Kumar Mandal, M

    Kushal Tibrewal, Chandra Venkataraman, Harish Phuleria, Veena Joshi, Sameer Maithel, Anand Damle, Anurag Gupta, Pradnya Lokhande, Shahadev Rabha, Binoy K. Saikia, Sayantee Roy, Gazala Habib, Shubham Rathi, Anubha Goel, Sakshi Ahlawat, Tuhin Kumar Mandal, M. Azharuddin Hashmi, ...

    2023

  27. [35]

    Random forest analy- sis of land use and land cover change using sentinel-2 data in van yen, yen bai province, vietnam

    Xuan Quang Truong, Nguyen Hien Duong Dang, Thi Hang Do, Nhat Duong Tran, Thi Thu Nga Do, Van Anh Tran, Vasil Yordanov, Maria Antonia Brovelli, and Thanh Dong Khuc. Random forest analy- sis of land use and land cover change using sentinel-2 data in van yen, yen bai province, vi...

    2023

  28. [36]

    Fertility rate, total (births per woman) - pakistan, 2022

    World Bank. Fertility rate, total (births per woman) - pakistan, 2022. Accessed: 2023-12-09

    2022

  29. [37]

    Health impacts - types of pollu- tants, 2023

    World Health Organization (WHO). Health impacts - types of pollu- tants, 2023. Accessed: 2023-12-09

    2023

  30. [38]

    Oriented object detection in aerial images with box boundary-aware vectors

    Jingru Yi, Pengxiang Wu, Bo Liu, Qiaoying Huang, Hui Qu, and Dim- itris Metaxas. Oriented object detection in aerial images with box boundary-aware vectors. pages 2149–2158, 01 2021. Figures & Tables T0 T1 T2 Semantic Characteristic Land Cover Vegetated Areas Green Areas Areas...

    2021 arXiv

  31. [2023]

    AIR QUALITY LIFE INDEX® (AQLI)

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.