Scalable Deep Learning to Identify Brick Kilns and Aid Regulatory Capacity

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S.P.L. research; contributed and and D.B., designed D.B. N.R.B., S.P.L. and J.L., and N.R.B. analysis; research; D.B.L., the performed S.E., F.T. M.B., and N.R.B., N.R.B., J.L., contributions: Author satellite sur- in visible and them large, (see making bricks, images reasonably drying of distinct, rows visually (16–18). by rounded very kilns are coal-fired kilns inefficient, pro- Brick highly are bricks traditional, Asia, in South duced across and Bangladesh, In approach. past the a over As rapidly 17). 18). 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Bangladesh, support regulatory society and and risk, civil policy at or ter are agencies who populations regulatory identify help violations, can located are owo orsodnemyb drse.Eal [email protected] Email: addressed. be may correspondence whom work.y To this to equally contributed N.R.B. and J.L. eeaeifraino e olto sources. approach pollution Our key to on country. agencies information regulatory the generate for method of replicable low-cost, well-being a and offers implications health has the which for manufacturing, regula- national brick the governing of tions violations widespread reveal imagery. data satellite Our in Bangladesh, in industry highly dis- informal a polluting of kilns, brick numbers identifying for large approach scalable machine-learning monitor accurate, an and demonstrates study locate This polluters. persed to governments the ability because the pollution, of lack amount responsi- large governments is a for for industry ble informal difficult where particularly countries, low-income a is in is It regulations challenge. environmental global with compliance Monitoring Significance eod rc in edtesle oa betdetection object an to themselves lend kilns brick Second, nwn rcsl hr l ftebikklsi Bangladesh in kilns brick the of all where precisely Knowing c,d i.S1 Fig. Appendix, SI tfn Ermon Stefano , https://doi.org/10.1073/pnas.2018863118 d eateto at ytmScience, System Earth of Department . y raieCmosAttribution-NonCommercial- Commons Creative a,c o xml mgso kilns). of images example for . y ai .Lobell B. 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COMPUTER SCIENCES SUSTAINABILITY SCIENCE constructed, this manual mapping approach is also a highly ineffi- dictions to incorporate them into an expanded and improved cient way to obtain the high-frequency information necessary for training dataset (Materials and Methods). We show how our monitoring. Moreover, government data in low-income countries approach is robust to the visual, geographic diversity of satel- is often inaccurate and subject to bias (21). lite imagery. We also demonstrate its scalability. Although our Here we develop a scalable, deep-learning pipeline for accu- approach required human input during model development, this rately locating brick kilns across a broad geography. Brick kilns is a one-time fixed cost; our model is virtually free to run repeat- are visually distinct in satellite imagery, but it is expensive and edly and generate predictions across huge geographies without time-consuming to annotate images with their exact locations. further input. We make improvements on two recent papers that use machine- We then demonstrate how this map of brick kiln locations learning methods to identify brick kilns (22, 23) by presenting can be used to study compliance with environmental regula- an approach that only requires classification-level annotations tions, show how populations are exposed to brick manufacturing (indicating whether an image contains a brick kiln or not) to nationally, and examine the contribution of kilns to air pollution train a single model that performs the more difficult task of in Dhaka. detection (indicating where within an image the brick kiln is located). Building on past applications of deep-learning to envi- Results ronmental monitoring (5, 6, 23), and on specific efforts to use Identifying Brick Kilns. The model we trained on the initial, lim- deep learning to identify brick kilns (22, 23), we develop a ited dataset attained a low precision (the proportion of true weakly supervised learning approach that learns to localize kilns positives among all images predicted positive) and very low recall given lower cost classification-level data, rebuilds kilns that have (the proportion of true positives among all positives) on the been fragmented across multiple images, an inherent problem final validation set (respectively 82% and 56%; SI Appendix, Fig with satellite imagery, and distinguishes between two kiln tech- S3A). After steps to improve the model’s ability to generalize nologies, based on their shape. To produce this final model, over the entire country (Materials and Methods), we launched we conducted two main model development steps to improve the final kiln classification model over the 1.8 million images the accuracy of our approach, first training an initial model that cover Bangladesh at a spatial resolution of 1 m per pixel. on a small, limited dataset, using it to classify images from Our model predicted 7,380 images contained kilns (positive) different parts of the country, and then hand-validating the pre- and 1,785,904 did not contain kilns (negative). To evaluate the

ABConfusion Matrix for Kiln Detection Confusion Matrix for Shape Classification

PREDICTION PREDICTION Kiln No Kiln Zigzag FCK

Kiln 6547 5 Zigzag 944 96

No Kiln 833 6995 FCK 208 375

Precision: 0.887 Zigzag Accuracy: 0.908 (LB: 0.809, UB: 0.918) Recall: 0.999 FCK Accuracy: 0.643 (LB: 0.528, UB: 0.707) Accuracy: 0.942 Overall Accuracy: 0.813 (LB: 0.754, UB: 0.826) Prob. of missing a kiln: 0.07% (95% CI: [0.0088%, 0.13%])

Fig. 1. Confusion matrices and performance summary for imagery from all of Bangladesh. (A) The confusion matrix for the performance of the final model from SI Appendix, Fig. S2 launched over the imagery from all of Bangladesh, after being hand-validated by our team. Note that all 7,380 images the model detected as containing kilns (positive images) and a division-stratified sample of 7,000 nonkiln (negative images) from around 1.7 million were hand-validated. The rows indicate the actual label (kiln, no kiln), while the columns indicate the prediction from the model. The green cells indicate correct predictions, where the top left are true positives (images with kilns that were correctly predicted by the model to contain kilns) and bottom right are true negatives (images with no kilns that were correctly predicted by the model to contain no kiln). The red cells are incorrect predictions, where the top right indicates false negatives (where the model predicted an image did not contain a kiln but the image did contain a kiln) and the bottom left indicates false positives (images that contained no kiln but were predicted by the model to contain a kiln). Performance statistics (precision, recall, and accuracy are presented beneath the confusion matrix. “Accuracy” is defined as the number of images correctly identified divided by the total number of images, “precision” is defined the number of true positives divided by the total number of images predicted as positive, and “recall” is defined as the number of true positives divided by all positive images. Because the validation of negative images was performed on a sample of images, we constructed an estimate and 95% CI for the probability of missing a kiln. (B) The comparable confusion matrix for the performance of the shape classification model on a hand-validated sample of 1,623 images (note that the original sample contained 1,750 images, but 127 were dropped due to the inability to distinguish the shape during hand validation). Lower and upper bounds on the predictions were calculated by assigning the 127 “unclassifiable” images to different cells. For example, to get the lower bound for “ZZK accuracy” we assume all of the 127 images contain ZZK but were incorrectly classified by our model as FCK.

2 of 10 | PNAS Lee et al. https://doi.org/10.1073/pnas.2018863118 Scalable deep learning to identify brick kilns and aid regulatory capacity Downloaded by guest on October 1, 2021 Downloaded by guest on October 1, 2021 clbede erigt dniybikklsadadrgltr capacity regulatory aid and kilns brick identify to learning deep Scalable al. et kiln. Lee each around (Left color brown Bangladesh the Gazipur, as near appear that Dhaka, bricks of drying of north rows just by surrounded kilns of cluster a 2. 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ZZK, the in 64.5%, be In ZZK to of assumed 61.7%). are proportion ZZK 57.1%, kilns the CI: of 127 the (95% proportion where 59.4% the scenario “unclas- alternative becomes FCK, 127 sample be the the to when in assumed scenario 127 are first all assuming kilns the 2) sifiable” Under and extreme FCK ZZK. two are are under kilns reesti- kilns CIs 127 all we 95% assuming and this, 1) ZZK cases: address of quality, proportion To image the poor uncertainty. mated to additional due classify 127 adds not the which could for team account our not that does kilns however, estimate, This 66.5%). to ntefia tpo h ieiew ovre h centroids the converted we pipeline the of step final the In Right .Teklstesle r h e bet,wihare which objects, red the are themselves kilns The ). https://doi.org/10.1073/pnas.2018863118 fe eoigfalse removing After ,7)(20). 7,271) = PNAS | f10 of 3

COMPUTER SCIENCES SUSTAINABILITY SCIENCE 1,341 5,930 1,048 gov-mapped model-idenﬁed kilns matched kilns remaining kilns remaining

Government-Mapped Kilns Model-Idenﬁed Kilns (7,271 total) (6,978 total) Kilns Idenﬁed by Both

Fig. 3. Comparison of kilns identified by the Bangladesh government (7,271 total) and kilns identified by our model (6,978 total). First, we matched model- identified kilns (6,978 total) to government-mapped kilns (7,271) by identifying the nearest neighbor within 2 km. A total of 5,930 kilns were matched between both sets, leaving 1,341 unmatched kilns among the government-mapped kilns and 1,048 unmatched kilns among the model-identified kilns.

predictions (Fig 3). The number of unmatched kilns in the gov- structed without government permits. The proportion of FCK ernment data are well within the interval we would expect, given among the detected illegal kilns (that is, the model-identiﬁed our model’s recall. However, since we hand-validated all of the kilns that we could not match to a government-mapped kiln) is positive images detected by our model, the 1,048 unmatched 28.7%. This is quite similar to the proportion of FCK in the over- kilns in the model predictions are assumed to be illegal kilns con- all predictions (28.2%) but lower than the proportion of FCK

ABKilns per District Discrepancies with Government Data (by District)

26°N 26°N

25°N 25°N

24°N 24°N

23°N 23°N

22°N 22°N

21°N 21°N

88°E89°E90°E91°E92°E 88°E89°E90°E91°E92°E

Kilns Banned Kilns per District 100 200 300 400 Discrepancies with GoB Data −100 −50 0 50 100

Fig. 4. Distribution of kilns across Bangladesh and comparison to government data. (A) Plot of the number of kilns identified by our model per district. (B) Plot of the difference between kilns identified by our model and those registered with the government. Red districts indicate districts where our model identified more kilns than the government records contain, and blue districts indicate the opposite. Three districts where kilns are banned are indicated by the dashed line border (Bandarban, Khagrachhari, and Rangamati).

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Fig. mandates which Act, Manufacturing (Control) Brick Establishment the Kilns with Brick compliance and in are kilns whether that 5A). kilns. pregnancies (Fig. brick of kiln to million, number a proximity the 150 of in for km occur over distributions 10 while similar within of kiln, see lives population population, We a entire the of the of km 95% of we or 1 12% 2017, within or from live million, pol- occur Bangladesh, data 18 air that over population the that pregnancies gridded to found the vulnerable Using particularly as emitted. be well lution may population as which the kilns, kilns, of to near proportion exposed the is identified that we country, entire transparent external, an kilns. of of systematic accounting value This law. the overreport- CI the highlights with an 95% comply underreporting implies that the technologies further of kiln which of bound ing predictions, in upper model reported the our ZZK exceeds of of percentage data banned the government older, fact, the In (an 7%. FCK ZZK by to of technology) relative percentage technology) the cleaner overreports we (newer, government type, kiln the the of that in registry found government we constructed to which for then shape kilns the but 1,623 hand-validated the legal adja- compared we the are When districts. in they banned registered where formally districts are government cent the kilns in some than indi- model suggesting which our 4B, data, our by Fig. predicted in that whereas kilns blue districts fewer are respectively, cates the districts Moreover, banned kilns, kilns. three 19 the registered border and 27, 1 30, data and detected government model 9, the Fig. 1, of Rangamati), report portion and southeast Khagrachhari, the darban, in discrep- districts more red and 4B far three 4B), found we Fig. (the banned, in Chittagong, ancies specifically of map districts are the hilly kilns of three where in center specifically the looked in we when district (DoE) red Environment of dark Division Department Dhaka (the the of with district registered Gazipur are the in than kilns more 77 4 found (Fig. differs we kilns of distribution the kiln the data, ment on of based sample is 28.7% full this the so hand-validate subsample, predictions. a nonvalidated not only did but results important we type is that it note However, (35.9%). to sample hand-validated the in Protected Areas Total Population AB Health Facilities h pta rcso forrslsas losu ocheck to us allows also results our of precision spatial The the across mapped kilns brick of locations precise the With govern- the to similar is kilns of number overall the Although ulndi h ahdlns.I hs he itit (Ban- districts three these In lines). dashed the in outlined Pregnancies h ecn fagvnrgltdett rpplto hthsabikkl ihnacrandsac.Frrailways, For distance. certain a within kiln brick a has that population or entity regulated given a of percent The (A) Railways Schools Forests Regulated EntityProximity toKilns 55 5100 75 50 25 0 % withkilningiven distance m5k 10km 5km 1 km .Frexample, For A). olto rm5 in oadteU mas;tersl on in result increase blows the an wind Embassy; is US the day the period, a toward time kilns such this 54 in from day pollution unfavorable most the h feto in ndaily on kilns of effect the increase average daily the of 16% to equivalent in an is daily from effect the increased pollution during This season increase) carries one-SD production wind a to brick the (equivalent where kilns 12 days additional while 4), umn esn(hnte r prtoa)on operational) brick-production are the they during (when kilns season additional from the downwind represents being lines of in effect the change between difference for The CIs sons. 95% and model 15 daily within raises kiln season one production from µg/m brick smoke find the blows We during wind Methods). km the and where (Materials embassy days effects meteorological the that fixed for when year down- controlling days and was kilns, to factors embassy fewer compared from the as rest downwind when kilns was days (the more on season from larger nonproducing wind were the year) the and of changes Dhaka March) whether (approximately to in prevailing measured season November brick-producing the Embassy then the identify We between US pollution to day. in the dataset each reanalysis at direction wind wind monitor global quality a PM and air available com- publicly the we with outcomes, locations from environmental kiln on brick light the shed bined to used be also national with compliance limited Taken respectively. very regulations. 0.5%, represents and this 6%, are 7%, together ille- are kilns The forests, areas railways, are of 5B). protected to close 9% (Fig. kilns and too facilities and constructed of illegally health school kilns 77% of to a percent close that of too found km constructed 1 We illegally within law. constructed the kilns gally of of percentage the violation identified also in we constructed entity, be each can to kilns close vio- multiple and too that Because length, kiln 5A). one (Fig. railway least law at of this to lates 22% proximate facil- are forests, health areas of protected of of 8% 13% 26% that schools, to found of model and 11% our these edu- ities, of by from each clinics, identified on km kilns and data 2 the spatial hospitals matched and from We railways forests. and km public areas, 1 protected within facilities, built cational can kiln no Protected Areas Health Facilities oudrtn hte u a fbikkl oain could locations kiln brick of map our whether understand To PM 3 2.5 9%C:01 o07;see 0.76; to 0.18 CI: (95% Railways Schools Forests .On S1 ). Table Appendix, (SI season brick the during Kiln Proximity toRegulatedEntities 55 5100 75 50 25 0 % ofkilnswithinagiven distance PM PM m5k 10km 5km 1 km https://doi.org/10.1073/pnas.2018863118 2.5 2.5 col- , S1 Table Appendix, SI PM mle yteregression the by implied A y24 by PM lt h vrg percentage average the plots 2.5 PM 2.5 costetosea- two the across , µg/m fet fklsin kilns of Effects . 2.5 3 y5.4 by i.6plots 6 Fig. . PNAS PM 2.5 2.5 | µg/m f10 of 5 data 0.47 3 .

COMPUTER SCIENCES SUSTAINABILITY SCIENCE 150 since they provide a predicted probability of whether a kiln is an FCK (banned since 2013) and could be used to target likely violators. Additionally, there is a trade-off between spatial resolution 100 and processing time. Satellite images taken at higher zoom levels cover less spatial area for a ﬁxed image size than lower zoom levels (because they are more “zoomed in”), which means images are clearer but more images are necessary to cover an area, 50 increasing processing time. One potential solution could employ

PM2.5 (ug/m^3) methods that screen low-resolution images first to identify likely kiln areas then use high-resolution imagery only for those areas to make predictions, saving computational cost (24). In our results, the final coordinates we identified often landed 0 directly on the kiln, but there were instances when the model 02040 was not able to mark every single kiln, particularly in dense clus- # of kilns ters. Although we were able to address the problem of double counting by rebuilding kilns split across multiple tiles, the prob- Kilns Off Kilns On lem of splitting clusters remains another area for future work. A limitation of our image classification approach is that it cannot Fig. 6. Effect of brick kilns on daily PM2.5 during the brick-production sea- distinguish between operational and nonoperational kilns if the son. Predicted effects of daily upwind kilns within 15 km, holding all other structure of the kiln is still present. An interesting direction for variables at means, shown from the difference-in-difference regression, future research is to incorporate satellite-based information on plotted in blue for the brick-production season and red for the nonproducing season. The shaded areas are 95% CIs. The difference between the lines heat or emissions into the neural network to identify operational represents the additional effect of upwind kilns during the brick production kilns. season (when they are operational). We selected a few applications to demonstrate the benefits of this approach but also ones that shed light on the regulatory land- scape for brick kilns in Bangladesh and provide information on other distances (12 and 20 km) were similar and are shown in who is affected by the harmful by-products of brick production. SI Appendix. These were meant as examples of what is possible with this type of data and demonstrated the applicability to other industrial Discussion operations or public health hazards. We have demonstrated a successful and highly accurate deep- Our results suggest that a substantial proportion of the learning approach for identifying brick kilns in satellite imagery, Bangladesh population lives in close proximity to brick kilns as well as a series of applications using the model predictions and almost the entire population lives within 10 km of a kiln. that highlight the utility of obtaining spatially precise, external This is because brick kilns tend to be clustered just outside data on an informal industry. Compared to manual approaches large urban areas where the majority of construction occurs that require either sending individuals out to collect GPS infor- in response to rapid urbanization and development. We also mation or manually scanning satellite imagery, our approach is find that adherence to the Brick Manufacturing and Brick Kilns quick, inexpensive, reproducible, and easily scalable. Although Establishment (Control) Act is low. Thirteen percent of health the model development required human involvement, now that facilities and 11% of schools are within 1 km of a brick kiln, in it has been trained and is highly accurate, it is virtually free to run violation of the Brick Manufacturing and Brick Kiln Establish- again, provided there is access to computing power and imagery. ment (Control) Act, while 75% of kilns are illegally constructed Our model can now be applied “off-the-shelf” by government too close to schools (Fig. 5B). The brick sector is no anomaly, agencies or civil society to locate kilns over large geographies as corruption and limited capacity hinder enforcement and without further human input. Our approach also has the added compliance with regulations across traffic and transportation, benefit of objectivity and transparency for independent civil soci- forests and conservation, food safety, building codes, and income ety organizations interested in environmental monitoring as it taxes (25–30). does not rely on government records, which we demonstrated By examining the contribution of brick kilns to PM2.5 con- contain inaccuracies, such as missing kilns (Fig. 3), and are some- centrations, we shed light on the consequences for the exposed times biased with respect to regulations (Fig. 4B). Additionally, populations, school, and health facilities. On days where the wind even when taking the upper bound of the shape classification blew pollution from 54 kilns toward the embassy, PM2.5 concen- predictions and assuming all 127 “hard” images were ZZK, the trations were 24 µg/m3 higher relative to days where the wind proportion of ZZK relative to (illegal) FCK in the government is did not carry any kiln pollution. This is almost equivalent to the overstated. World Health Organization guideline for 24-h PM2.5 exposure From a machine-learning perspective, we demonstrated the (25 µg/m3) coming only from kilns on a bad wind day. viability of a model that learns to perform the task of geolo- With the evidence presented on illegal proximity of kilns to cating objects from only classification-level labels, a successful schools, health facilities, and populations (including pregnan- weakly supervised learning approach. However, there were lim- cies), our findings suggest that large proportions of the popu- itations. Image quality was not always consistent, as clouds lation in Bangladesh are exposed to the air pollution generated obscured certain areas or the satellite encountered issues in cap- by kilns. This exposure is particularly troubling when considering turing imagery, causing parts of or entire images to be blurry or pregnant women and schools that are close to kilns. Pregnant missing. Although our shape classification algorithm was fairly women and school children are highly vulnerable to pollution accurate, it performed worse than the overall prediction model, exposure (31), and both in utero and early-life exposure to air which was likely influenced by image quality. Future work could pollution has been linked to poor pregnancy outcomes, poor improve the accuracy of this step in the pipeline and addressing child health status, lower academic performance and cognitive imagery quality would be an important step here. Nonethe- development, and other adverse adult outcomes (32, 33). Given less, even in its current state the shape-classification algorithm that our model missed around 1,276 kilns, the extent of expo- could provide results that are extremely useful for regulators sure and adverse impacts are likely underestimates. Ultimately,

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Machine-Learning Methods and Materials sectors. and industries of of implications range environmental a across and noncompliance health compliance, human in regulatory reducing for improving thus advances promise and great Ultimately, hold transparency law. a observation promoting Earth the introduce gov- and pressure enforce learning and as machine to violators well as officials disclose activities, ernment illegal publicly hiding to for disincentive used information this be organizations, society Alterna- civil could potential. of and great hands monitor the have in to violations tively, govern- detect capacity the to enhance limited ability can have of ment’s that we tools context regula- low-cost the as regulations, existing In enforce yet with low. environment, compliance is analysis the tions this and in health demonstrated human to health harm or of costs schools, the government forests, reduce sending could to of violations oversight. and address close presence verifying to as too representatives manually such built before violations, kilns algorithm likely facilities, an identify or Running to predic- FCK data). model ours our own as since to government’s such Bangladesh according the (both in and exist banned tions still been many yet have to 2013, dis- respect FCK banned with three Similarly, and built railways, tricts. be forests, facilities, of can health violations kilns schools, extensive where fairly governing con- documented human regulations organizations potential of We other society violations. and locations civil labor rights bonded precise for and child useful the cor- about be cerned by knowing would plagued example, kilns system brick For violations a (16). on to data ruption transparency transparent the external and Moreover, external introduces violations. allocate generate regarding best to priorities to ability set how or determine resources apparatus health regulatory a from strained insufficient are areas critical from km perspective. built 1 be kilns least mandating at regulations the demonstrate results our eue eplann otanacnouinlnua ewr (CNN) network neural convolutional a train to learning deep used We substantial causes manufacturing brick of mode current The con- a help could law existing of violations identifying Easily edwlae aelt mgr rmDig- from imagery satellite downloaded We 2% e n rie ormdl,oefrec aae.W etdthe tested We dataset. each for one models, four trained and set (20%) 4) 3) 2) Tangail: and 1) Sylhet in mistakes model’s the of combinations model’s different the thus and data training the of were (“mis- diversity knowledge. 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COMPUTER SCIENCES SUSTAINABILITY SCIENCE models on all four validation sets, as well as the entire contiguous regions centered around the centroid of size 224 × 224. We input each of these of Sylhet and Tangail to check their performance at scale. The model trained recentered and cropped images to the shape classification CNN to pro- on AugData1 + S performed best. We moved forward with this final train- duce the final predictions. Finally, we translated the pixel location (x,y) of ing dataset, which we call AugData2 (SI Appendix, Fig. S2). We conducted the centroid within the image to its geographic coordinates, modeling the more iterations of training to tune hyperparameters (parameters that are distance in x as a difference in longitude and the distance in y as a dif- set before training and affect aspects such as how the model learns, how ference in latitude. The entire pipeline is portrayed visually in SI Appendix, quickly, and for how long) to optimize performance gains, resulting in our Fig. S3). final model. To compare the different models we developed throughout these steps to improve the robustness and generalizability, we evaluated Evaluating final model performance. In a standard prediction problem, to each one on the same validation set (SI Appendix, Figs. S4 and S5). These evaluate the performance of an algorithm one would compare the model results demonstrate the gains in performance achieved by the training data predictions to the true values in a test dataset. During model development, expansion and robustness experiments and the high level of accuracy our we used data with underlying labels (kiln or no kiln) split into training, vali- final model obtained. dation, and test sets to evaluate the model’s performance (SI Appendix, Figs. Kiln localization. We developed a weakly supervised approach in which S4 and S5). However, our final model was run on imagery covering the coun- the model learns from the image-level classification labels of positives try, which does not come with labels. To assess the model’s performance, we and negatives to perform the harder task of localization (i.e., identify- hand-validated all images predicted as “kiln” (7,380 images) and a division- ing where within an image the kiln is located). This approach is weakly stratified, random sample of 7,000 negative predictions. We generated a supervised precisely because it does not require the training data to be confusion matrix with the results of this hand validation (Fig. 1). However, annotated with exact kiln locations, which can be very time-intensive to because we did not hand-validate all 1.79 million negative images, the accu- produce. First, we modified the architecture of the CNN. We added a global racy statistics are an estimate. To account for this uncertainty, we used the average pooling layer as in ref. 42, which takes the feature maps the false-negative rate we found in the hand-validated sample to approximate model has learned in past layers and weighs them in terms of how much the number of false negatives across the full set of negative predictions and they contribute to the final prediction. By summing the contributions, it construct a CI around the estimation. is possible to produce a class activation map (CAM), which visualizes the To do this, we used the maximum likelihood estimator to estimate the model’s signals, like a heat map of which pixels contribute most strongly false-negative rate as the mean and variance of a Bernoulli variable and to the model’s prediction (SI Appendix, Fig. S3). These clusters of acti- then constructed a 95% CI using a critical value of 1.96. Specifically, let p be vated kilns provided an approximation of where the kilns were located in a the probability of any negative image (“no kiln”) of being a false negative positive image. (image that actually contains a kiln). If in a sample of negative images of Kilns can be split across multiple satellite image tiles, resulting in the same size N, there are n false negatives, then the maximum likelihood estimate kiln’s being detected in multiple images and thus counted more than once. (MLE) of p is Therefore, we developed an approach to rebuilding split kilns by generat- n pˆ = . [1] ing “connected components,” in which spatially adjacent tiles are stitched N together to form a contiguous area. Starting from a given positive image, Also, for a large sample size, pˆ converges to p in distribution (asymptotic our approach used breadth first search to branch out to find all its neigh- distribution of the MLE), i.e., boring positive images. By stitching the images together, we also connected p activated areas from the CAMs. NI(p)(pˆ − p) ∼ N (0, 1), [2] However, the CAM output was very noisy because features in an image other than the kilns contributed to the model’s prediction. To focus the where I(p) is the Fisher information. For a Bernoulli variable, we have model signals on just the kilns, we conducted postprocessing on the connected components. Each pixel had a value between 0 and 255, where a 1 I(p) = , [3] higher value meant a stronger “kiln” signal. The first step filtered out weak p(1 − p) signals. We subtracted the mean pixel value from all pixels, which zeroed out the areas with low values. Then, we calculated the mean pixel value which is also the inverse of the variance. With this, we construct a 95% CI from only nonzero pixels and subtracted that value as well, leaving only the around pˆ using a critical value of 1.96 (zα/2, with α = 0.05): strongest signals in the image. We then normalized the values, such that the s lowest value was 0 and highest was 255. Next, we concentrated the signals ˆ ˆ p(1 − p) . and filtered out any remaining noise. We increased contrast to amplify the pˆ ± 1.96 × [4] N signals and passed the image through a median filter and Gaussian filter to smooth areas and remove any small patches of signals that were too small Similarly, to construct a CI for the performance of the shape detection to be a kiln. We iterated this second step of amplifying and smoothing sig- algorithm, which distinguished between FCK and ZZK, we used the same nals. Finally, since the smoothing could unintentionally expand the size of approach. If we let n be the number of ZZK in our hand-validated sample of kilns, we used a morphological operator that erodes pixels around the edges N brick kilns, then the estimated ratio of ZZK to all kilns, pˆ, can be derived of the remaining clusters of signals. The centroids of each of these clusters using the same formula (Eq. 1), and the 95% CI can be obtained using Eq. 4. provided the locations of the kilns. In cases with multiple kilns in a single image, the activated pixels could Scale and Scope of Brick Manufacturing. be conjoined as one large mass in the CAM output. We kept track of a Data. We compiled numerous sources of data to assess the scope of brick moving average of the size of these activated areas to estimate the aver- manufacturing in Bangladesh and the extent of regulatory compliance. First, age size of a kiln. When an activated area was larger than the average, we used two sources of data from the DoE of the GoB on the official number we marked more centroids. For example, when an area was twice as large of kilns. One is the aggregate number of formally registered kilns in each as the average kiln size, we collected two centroids spread evenly across district from 2017 to 2019, split out by the type of kiln and whether the kiln the pixel mass. owner secured environmental clearance (18). The other is GPS coordinates of kilns that were manually mapped by the Clean Air and Sustainable Envi- Shape classification and generating coordinates. Equipped with the centroids ronment (CASE) project (20). Second, we used globally gridded datasets on of the kiln clusters, we implemented the final step of the pipeline: clas- total population and pregnancies produced by WorldPop (43) at a 100-m sifying the shape of each kiln. First, we prepared a dataset by labeling the spatial resolution to identify exposed populations, particularly populations hand-validated positive images detected across the country with kiln shapes. of interest to policymakers. There were two classes: FCK, which are ovular, and ZZK, which are rectan- Third, we utilized publicly available data on the Humanitarian Data gular. Of the 3,345 images we labeled, 1,193 were FCK and 2,152 were ZZK, Exchange (44) to assess compliance with the GoB regulations (details on which were split into a training and validation set with the same ratio of the regulations are given below). Specifically, we used shapefiles containing FCK to ZZK. Using deep learning and transfer learning again, we trained nationally designated forests and national parks, railways, health facilities, a second CNN to classify the kiln shape, which achieved 98% accuracy on and schools, and administrative boundaries, as well as shapefiles from the the training set and 96% accuracy on the validation set. With an accu- World Database on Protected Areas (45). rate shape classification model trained, we then launched our end-to-end Finally, to assess the contribution of brick kilns to air quality, we utilized pipeline on unseen imagery covering the entire country. For each centroid publicly available PM2.5 data spanning 2017 to 2019 from the air-quality identified from the localization step of the pipeline, we cropped an image monitor at the US Embassy in Dhaka. The data contained observations every

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Grant), and Devel- (EVP 1651565 Environment Global We grants the Ser- for work. for NSF Center Institute and this Woods King of Stanford Erden, Stanford the iterations the Bora opment, earlier from Tan, with funding for Chris the assistance Tang acknowledge research and Zhongyi for imagery; and Camelo the Sintek gio accessing Christina available; with imagery support satellite the ing ACKNOWLEDGMENTS. ieieadsailaayi r vial nteHradDtvreat Dataverse Harvard the learning machine on entire available HVGW8L the are for analysis scripts https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ R spatial them. and obtain and to Python how pipeline with on along details provide data we All and bound- available administrative publicly and are manufac- temperature, aries) brick precipitation, of protected wind, All scope facilities, PM2.5, health Environment. and areas, schools, scale of forests, the distributions, Department assess (population the to turing used from data directly geospatial proprietary requested other is be kilns is brick analysis can on the data and of Bangladesh remainder GPS of the (the Government so pipeline data. 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