# Mitigating climate and health impact of small-scale kiln industry using
multi-spectral classifier and deep learning

Usman Nazir, Murtaza Taj & Momin Uppal
Lahore University of Management Sciences
{17030059, murtaza.taj<EMAIL_ADDRESS>&Sara Khalid
University of Oxford
<EMAIL_ADDRESS>Tackling Climate Change with Machine Learning
workshop at ICLR 2023

###### Abstract

Industrial air pollution has a direct health impact and is a major contributor
to climate change. Small scale industries particularly bull-trench brick kilns
are one of the key sources of air pollution in South Asia often creating
hazardous levels of smog that is injurious to human health. To mitigate the
climate and health impact of the kiln industry, fine-grained kiln localization
at different geographic locations is needed. Kiln localization using multi-
spectral remote sensing data such as vegetation indices can result in a noisy
estimates whereas relying solely on high-resolution imagery is infeasible due
to cost and compute complexities. This paper proposes a fusion of spatio-
temporal multi-spectral data with high-resolution imagery for detection of
brick kilns within the “Brick-Kiln-Belt” of South Asia. We first perform
classification using low-resolution spatio-temporal multi-spectral data from
Sentinel-2 imagery by combining vegetation, burn, build up and moisture
indices. Next, orientation aware object detector YOLOv3 (with $\theta$ value)
is implemented for removal of false detections and fine-grained localization.
Our proposed technique, when compared with other benchmarks, results in a
$21\times$ improvement in speed with comparable or higher accuracy when tested
over multiple countries.

##  1 Path to Climate Impact

Industrial air pollution has a direct health impact and is a major contributor
to climate change. Unregulated small-scale informal industries spread across
vast areas are common in resource-limited settings and can be difficult to
locate and monitor. Remote identification of kilns and monitoring of their
carbon production can assist air pollution surveillance, regulation, and
climate mitigation efforts. The exact numbering and location of kilns is
needed to understand and address the kiln sector’s climate and health impacts.

##  2 Introduction

Vehicles and industries are considered as one of the major contributors of
pollution resulting in low air quality and smog Haque et al. (2022). According
to an estimate $20\%$ of global black carbon emission is from the brick kilns
Maithel (2014). These kilns are also a major source of employment, however
according to the Global Slavery Index of $2019$, the “Brick-Kiln-Belt” of
South Asia (stretching between Afghanistan, Pakistan, India, and Nepal) is
home to approximately $60\%$ ($24.3$ million individuals) of modern-day
slavery Landman & Silverman (2019). Keeping in view the UN’s Sustainable
Development Goals (SDGs) $3.9$ and $8.7$ which specifically aim to address air
pollution and forced labor respectively, mapping brick kilns in the South Asia
region is an essential first step.

Manual surveying of “Brick-Kiln-Belt” is infeasible due to the large
geographical spread ($1,551,997~{}{km}^{2}$) as well as geographic boundaries.
Due to recent advancements in machine learning as well as availability of
remote sensing data, automated surveys are now more commonly used for such
large-scale analysis Redmon & Farhadi (2018); He et al. (2017); Li et al.
(2018); Xie et al. (2016); You et al. (2017); Cotrufo et al. (2018). Recently,
remote sensing images have also been used to analyze the extent of modern
slavery Boyd et al. (2018); Jackson et al. (2018); Misra et al. (2019); Foody
et al. (2019). The “Slavery from Space” project Boyd et al. (2018) proposed a
crowd-sourced procedure to manually detect brick kilns from satellite imagery.
They randomly sampled the potential kiln areas into $320$ cells of $100$
$\text{km}^{2}$ each. However, they were only able to manually annotate $30$
geographic cells (i.e. only $2\%$ of the entire Brick-Kiln-Belt). As a result,
the manual crowd-sourced method lacks generalization and scalability as is
evident from the resulting sparsely annotated maps. On the other hand, low-
resolution multi-spectral satellite data has also been used to classify brick
kilns in the region surrounding the Delhi state in India Misra et al. (2019).
The analysis in this work was based on normalized difference vegetation index
(NDVI) and transfer learning, which unfortunately is prone to generate a large
number of false detections. In contrast, high-resolution satellite imagery has
also been used to detect brick kilns to the east of Jaipur, which is the
capital city of India’s Rajasthan state Foody et al. (2019). This work used
Faster R-CNNs to automate the process of brick kiln identification in the
given tile of images. However, owing to the large computational complexity,
this approach is difficult to apply at a large scale. Moreover, it yields a
high false positive rate for which the study proposed to train a two-staged
R-CNN classifier to achieve acceptable performance which further increased the
processing time. A more recent approach called KilnNet Nazir et al. (2020)
combined inexpensive an classifier with object detector in a two-stage
strategy to address the issue of time complexity. This approach too is only
based on high resolution satellite imagery and is infeasible due to data
acquisition and processing costs.

Here we also propose a two-stage strategy for automated detection of brick
kilns. However, our approach is over $21\times$ faster than state-of-the-art
(SOTA) benchmarks and mostly relies on freely available low resolution remote
sensing data. Most existing object detection techniques in low resolution
satellite imagery are significantly less accurate whereas computation is very
costly for high resolution satellite imagery. To overcome this our proposed
methodology decouples classification and localization. Classification is
performed using spectral properties while localization is accomplished using
orientation adapted detector. This results in a coarse-to-fine search in which
fine-grained orientation aware localization via object detection is performed
as a second stage only on less than $10\%$ of the total region. This results
in a $21\times$ improvement in speed in addition to improvement in accuracy.
We tested our approach on three countries (Pakistan, India and Afghanistan)
and showed that it is scalable as well as generalizable to varying structural,
environmental and terrain conditions.

Figure 1: Illustrative example of working of the proposed approach. In the
first step we apply a rule based classifier on spectral indices (NDVI, EVI,
NDMI, NDBI, BAI) on regions of interest to classify brick kilns as shown in
the heatmap (Satellite images courtesy Google Earth).

This paper has the following four technical contributions: (i) Fusion of
spectral Indices: Classification is performed using mixture of spectral
indices as shown by equation 1 in the paper. (ii) Fusion of low resolution and
high resolution imagery: Our proposed approach processes input from low-
resolution sensors for the generation of potential candidates for kilns which
are then filtered via high-resolution input via Orietation-aware YOLOv3. (iii)
Processing large datasets: The proposed strategy reduces the computational
burden associated with processing of large datasets by fusion of low
resolution and high resolution imagery. SOTA benchmarks take on average $674$
seconds to process three datasets in Table 1 of the paper. Our proposed
approach on the other hand reduces this compute time to $38$ seconds only.
(iv) Detection of other objects: Our multi-sensor and multi-stage strategy can
also be used to detect other objects that have differentiable spectral
signatures and well defined shapes e.g. industrial units, oil tanks,
warehouses, tennis courts, parking lots etc. Here we have only demonstrated
its application for geo-localization of brick kilns.

##  3 Proposed Methodology

Brick kilns are typically identifiable through a visual inspection of
satellite imagery. However, while considering a large geographic area, several
inherent complexities in satellite imagery make automated detection of brick
kilns a challenging task. This include, but are not limited to, i) variations
in imaging sensors, ii) differences in kiln structure across the countries,
iii) dynamic kiln surroundings and iv) variations in luminosity, seasonal
changes, and pollution levels etc. It is particularly challenging to identify
brick kilns in low resolution imagery ($\frac{10-30~{}meters}{pixel}$).
Existing pixel classification along with transfer learning approach Misra et
al. (2019; 2020) for detection of brick kilns is not scalable as well as
generalizable at large scale due to cost complexities. In our proposed multi-
spectral approach brick kilns are classified using spectral indices (without
transfer learning) due to the fact that reflectance spectra of different land
covers are different. The indices are designed to exploit these differences to
accentuate particular land cover types. The land covers are separable at one
or more wavelengths due to spectral reﬂectance of different materials (see
Fig. 3 and Fig. 4 in Appendix A). Brick kilns being man-made structures have a
high built-up index. Unlike other man-made structures and due to the specific
nature of work, the kiln surrounding has a low vegetation index. Furthermore
the baking process and smoke from chimneys also result in a high burn index.
Thus in this work we classify brick kilns using mixture of spectral indices
namely NDVI, EVI, NDBI, NDMI and BAI (see Appendix A). The proposed
architecture is shown in Fig. 1.

Table 1: Table showing quantitative evaluation of the proposed approach
compared with state-of-the-art methods. Top-3 ranking methods are in bold and,
in particular, red (1st), violet (2nd) and black (3rd).

| Classification Score |
---|---|---
Testing Datasets | Network Architectures | TP | TN | FP | FN | Duplicates | Precision | Recall | F1 score | Time (seconds)
Pakistan (Kasur) | Multi-spectral Approach | 21 | - | 303 | 0 | 2 | 0.065 | 1 | 0.12 | 3
Two-Stage Faster R-CNN | 13 | 3090 | 1 | 6 | 0 | 0.93 | 0.68 | 0.79 | 195.5
Two-Stage SSD | 12 | 3090 | 1 | 7 | 0 | 0.92 | 0.63 | 0.75 | 179.5
Kiln-Net (Two-Stage YOLOv3) Nazir et al. (2020) | 19 | 3091 | 0 | 0 | 0 | 1 | 1 | 1 | 162.5
Proposed (Multispectral Two-Stage Strategy) | 21 | - | 1 | 1 | 2 | 0.95 | 1 | 0.97 | 7.04
1-11 India (New Delhi) | Multi-spectral Approach | 52 | - | 636 | 0 | 12 | 0.076 | 1 | 0.14 | 4
Two-Stage Faster R-CNN | 37 | 4441 | 1 | 3 | 0 | 0.97 | 0.93 | 0.95 | 276.1
Two-Stage SSD | 38 | 4442 | 0 | 2 | 0 | 1 | 0.95 | 0.97 | 255.3
Kiln-Net (Two-Stage YOLOv3) Nazir et al. (2020) | 40 | 4442 | 0 | 0 | 0 | 1 | 1 | 1 | 232.8
Proposed (Multispectral Two-Stage Strategy) | 52 | - | 1 | 0 | 12 | 0.98 | 1 | 0.99 | 8.16
1-11 Afghanistan (Deh Sabz) | Multi-spectral Approach | 406 | - | 1940 | 0 | 142 | 0.17 | 1 | 0.29 | 8
Two-Stage Faster R-CNN | 100 | 4097 | 5 | 22 | 0 | 0.95 | 0.82 | 0.88 | 553.2
Two-Stage SSD | 90 | 4094 | 8 | 32 | 0 | 0.92 | 0.74 | 0.82 | 416.4
Kiln-Net (Two-Stage YOLOv3) Nazir et al. (2020) | 85 | 4073 | 29 | 37 | 0 | 0.75 | 0.70 | 0.72 | 279.6
Proposed (Multispectral Two-Stage Strategy) | 198 | - | 17 | 66 | 142 | 0.92 | 0.75 | 0.83 | 23.1
1-11 |  |  |  |  |  |  |  |  |  |

###  3.1 Kiln Candidates via Multi-Spectral Classification

Based on the assumption that kiln locations have low vegetation and moisture
index whereas high build-up and burn area index, small or negative values of
NDVI, EVI and NDMI with positive values of NDBI and BAI are classified as
brick kilns (see Fig. 2 (i)). Thus our classification rule is defined as:

$f(x,y)=\begin{cases}1\quad&if~{}~{}\text{$NDVI<0.2~{}\&~{}EVI<0.2$}~{}\&~{}\text{$NDMI<0~{}\&~{}NDBI>0$}~{}\&~{}\text{$BAI>5e^{-8}$}\\\
0&\text{otherwise}\end{cases}$ (1)

where $(x,y)$ is a location in latitude and longitude and function $f(\cdot)$
gives the classification decision. We set the threshold of $0.2$ for NDVI and
EVI as values $>0.2$ are considered as healthy vegetation Huete et al. (2002).
In the second stage we apply orientation aware detector (YOLOv3 with $\theta$
value) for false removal and kilns bounding boxes.

Multi-spectral classification |  | Orientation aware detector (Deep Learning) |
---|---|---|---
|  |  |
|  |
$99\%$ data is filtered out in classification stage | (a) Afghanistan | (b) Pakistan | (c) India
(i) |  | (ii) |

Figure 2: (i) Qualitative evaluation of our proposed Multi-spectral approach
on region of Punjab, Pakistan. In first stage of our proposed two stage
strategy, around $>99\%$ data is filtered out and only positive potential
candidates (red pixels images) are passed to second stage for localization.
(ii) Qualitative evaluation of Orientation aware YOLOv3. (Satellite images
courtesy Google Earth).

###  3.2 Orientation aware detector: YOLOv3

Although the unique spectral characteristics of brick kilns distinguish them
from other classes, however they are still confused with other small
industries’ chimneys as they exhibits similar spectral properties particularly
NDVI and NDBI. We eliminated the resulting false detection via object
detector. Unlike urban housing, kilns in South Asia are usually build at
sparse locations which are mostly surrounded by agriculture land. Consequently
they are usually built at arbitrary orientations. Thus the detection of axis
aligned kilns Misra et al. (2020); Nazir et al. (2020) is not applicable and
results in increased missed detections. To address this problem, bounding box
with orientation can be used Zhang et al. (2019). We therefore modified the
YOLOv3 detector and added the neuron for regressing orientation with each
bounding box. We only provided filtered images (potential candidates for
kilns), obtained after classification stage, to the orientation-aware YOLOv3
model and obtained brick kiln bounding boxes as output results.

##  4 Quatitative and Qualitative Evaluation

For detailed experimentation of our proposed procedure, we choose an
evaluation dataset of three cities named Deh Sabz, New Delhi and Kasur from
three different South Asian countries namely Afghanistan, India and Pakistan.
For fair comparison we use the same geographical regions in these cities as
defined in KilnNet paper Nazir et al. (2020). In addition, in order to
generate training data for our object detector, we also manually annotated
bounding boxes for each one of the $1300$ brick kiln images from the ‘Asia14’
dataset Nazir et al. (2020).

We evaluated our proposed multispectral two-stage strategy with comparison to
ResNet-152 He et al. (2016) classifier followed by the SOTA detector: Faster
R-CNN Ren et al. (2015), SSD Liu et al. (2016) and YOLOv3 Redmon & Farhadi
(2018). A coarse-to-fine strategy is proposed that aims to filter the bulk of
the data using spectral properties while the detector is only applied on a
small amount of positive detections to generate localization information while
filtering false positives.

It can be seen from Table 1 that the overall F1-score of our proposed strategy
is comparable with all the SOTA two-stage architectures. Simple multi-spectral
approach is $54\times$ faster as compared to other strategies but results in
many false positives and less F1-score. On the other hand our proposed
approach is $21\times$ faster while retaining high F1-Score. Testing dataset
images are $256\times 256$ pixels for quantitative evaluation, If the image
size is larger than $256\times 256$ pixels, it is detected in two image
patches. To deal with this issue, we describe the duplicates in Table 1. Our
proposed architecture also outperforms in region of Afghanistan where kiln and
non-kiln regions exhibit extremely low contrast as illustrated in our
qualitative evaluation in Fig. 2 (ii) (see training parameters, Kiln-Net vs.
proposed approach and compute cost comparison in Appendix B, C & D
respectively).

##  5 Conclusion and Future Work

This paper proposes a fusion of spatio-temporal multi-spectral data with high-
resolution imagery for detection of brick kilns within the “Brick-Kiln-Belt”
of South Asia. To achieve this, we first perform classification using low-
resolution spatio-temporal multi-spectral data from Sentinel-2 imagery
utilizing spectral indices. Then orientation aware object detector: modified
YOLOv3 (with $\theta$ value) is implemented for removal of false detections
and fine-grained localization. Our proposed technique results in a $21\times$
improvement in speed with comparable or higher accuracy when tested over
multiple countries. In future, we also aim to evaluate our proposed strategy
and detection of illegal brick kiln activity during winter smog period on all
over the “Brick-Kiln-Belt” of South Asia. Remote identification of illegal
industrial activity can improve monitoring of carbon production and any forced
labour in aid of law enforcement, spatial planning, and climate mitigation
policy-making.

## References

  * Boyd et al. (2018) Doreen S Boyd, Bethany Jackson, Jessica Wardlaw, Giles M Foody, Stuart Marsh, and Kevin Bales.  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 Sensing_ , 142:380–388, 2018.
  * Chuvieco et al. (2002) Emilio Chuvieco, M Pilar Martin, and A Palacios.  Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination.  _International Journal of Remote Sensing_ , 23(23):5103–5110, 2002.
  * Cotrufo et al. (2018) Silvana Cotrufo, Constantin Sandu, Fabio Giulio Tonolo, and Piero Boccardo.  Building damage assessment scale tailored to remote sensing vertical imagery.  _European Journal of Remote Sensing_ , 51(1):991–1005, 2018.
  * Foody et al. (2019) Giles M Foody, Feng Ling, Doreen S Boyd, Xiaodong Li, and Jessica Wardlaw.  Earth observation and machine learning to meet Sustainable Development Goal 8.7: Mapping sites associated with slavery from space.  _Remote Sensing_ , 11(3):266, 2019.
  * Gillespie et al. (2019) Thomas Gillespie, Austin Madson, Conor Cusack, and Yongkang Xue.  Changes in NDVI and population in protected areas on the Tibetan plateau.  _Arctic, Antarctic, and Alpine Research_ , 51(1):428–439, 2019.  doi: http://dx.doi.org/10.1080/15230430.2019.165054.  URL https://escholarship.org/uc/item/65t7r81p.
  * Haque et al. (2022) Shama E Haque, Minhaz M Shahriar, Nazmun Nahar, and Md Sazzadul Haque.  Impact of brick kiln emissions on soil quality: A case study of ashulia brick kiln cluster, bangladesh.  _Environmental Challenges_ , 9:100640, 2022.
  * He et al. (2016) Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.  Deep residual learning for image recognition.  In _IEEE Conference on Computer Vision and Pattern Recognition_ , pp. 770–778, 2016.
  * He et al. (2017) Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick.  Mask R-CNN.  In _IEEE International Conference on Computer Vision_ , pp. 2961–2969, 2017.
  * Huete et al. (2002) Alfredo Huete, Kamel Didan, Tomoaki Miura, E Patricia Rodriguez, Xiang Gao, and Laerte G Ferreira.  Overview of the radiometric and biophysical performance of the modis vegetation indices.  _Remote sensing of environment_ , 83(1-2):195–213, 2002.
  * Jackson et al. (2018) Bethany Jackson, Kevin Bales, Sarah Owen, Jessica Wardlaw, and Doreen S Boyd.  Analysing slavery through satellite technology: How remote sensing could revolutionise data collection to help end modern slavery.  _J. Mod. Slavery_ , 4:169–199, 2018.
  * Landman & Silverman (2019) Todd Landman and Bernard W Silverman.  Globalization and modern slavery.  _Politics and Governance_ , 7(4), 2019.
  * Li et al. (2018) Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, and Pheng-Ann Heng.  H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes.  _IEEE Transactions on Medical Imaging_ , 37(12):2663–2674, 2018.
  * Liu et al. (2016) Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg.  Ssd: Single shot multibox detector.  In _European Conference on Computer Vision_ , pp. 21–37. Springer, 2016.
  * Maithel (2014) Sameer Maithel.  Factsheets about brick kilns in south and south-east asia.  _Greentech Knowledge Solutions_ , 2014.
  * Misra et al. (2019) Prakhar Misra, Wataru Takeuchi, and Ryoichi Imasu.  Brick kiln detection in north India with sentinel imagery using deep learning of small datasets.  _In Asian Conference of Remote Sensing_ , 2019.
  * Misra et al. (2020) Prakhar Misra, Ryoichi Imasu, Sachiko Hayashida, Ardhi Adhary Arbain, Ram Avtar, and Wataru Takeuchi.  Mapping brick kilns to support environmental impact studies around delhi using sentinel-2.  _ISPRS International Journal of Geo-Information_ , 9(9):544, 2020.
  * Nazir et al. (2020) Usman Nazir, Usman Khalid Mian, Muhammad Usman Sohail, Murtaza Taj, and Momin Uppal.  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_ , 13:3251–3262, 2020.
  * Ogashawara & Bastos (2012) Igor Ogashawara and Vanessa Bastos.  A quantitative approach for analyzing the relationship between urban heat islands and land cover.  _Remote Sensing_ , 4(11):3596–3618, Nov 2012\.  ISSN 2072-4292.  doi: 10.3390/rs4113596.  URL http://dx.doi.org/10.3390/rs4113596.
  * Ranagalage et al. (2018) Manjula Ranagalage, Ronald Estoque, Xinmin Zhang, and Yuji Murayama.  Spatial changes of urban heat island formation in the colombo district, sri lanka: Implications for sustainability planning.  _Sustainability_ , 10, 04 2018.  doi: 10.3390/su10051367.
  * Redmon & Farhadi (2018) Joseph Redmon and Ali Farhadi.  YOLOv3: An incremental improvement.  _arXiv preprint arXiv:1804.02767_ , 2018.
  * Ren et al. (2015) Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun.  Faster R-CNN: Towards real-time object detection with region proposal networks.  In _Advances in Neural Information Processing Systems_ , pp. 91–99, 2015.
  * Xie et al. (2016) Michael Xie, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon.  Transfer learning from deep features for remote sensing and poverty mapping.  In _AAAI Conference on Artificial Intelligence_ , 2016.
  * You et al. (2017) Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, and Stefano Ermon.  Deep gaussian process for crop yield prediction based on remote sensing data.  In _AAAI Conference on Artificial Intelligence_ , 2017.
  * Zhang et al. (2019) Shaoming Zhang, Ruize Wu, Kunyuan Xu, Jianmei Wang, and Weiwei Sun.  R-cnn-based ship detection from high resolution remote sensing imagery.  _Remote Sensing_ , 11(6):631, 2019.

##  Appendix A Multi-spectral Indices

We use following five spectral indices to classify potential brick kilns on
Google Earth Engine:

NDVI: Normalized Difference Vegetation Index (NDVI) quantifies vegetation from
remote sensing imagery and is used in various applications such as tracking
population changes Gillespie et al. (2019) and spatial changes of a region
Ranagalage et al. (2018). Near Infra-Red (NIR) and Red bands of remote sensing
images are used by the index. NDVI always ranges from -1 to 1. The equation to
find NDVI is given in Eq. 2. Fig. 3 shows NDVI image at brick kiln locations
in region of Punjab, Pakistan and Fig. 5 shows the zoomed version at one kiln
location.

$NDVI=\frac{NIR-Red}{NIR+Red}$ (2)

EVI: The Enhanced Vegetation Index (EVI) is designed to minimize saturation
and background effects in NDVI Huete et al. (2002). Since it is not a
normalized difference index, compute it with this expression:

$EVI=\frac{2.5*(NIR-Red)}{NIR+6*RED-7.5*BLUE+1}$ (3)

NDBI: Normalized Difference Built-up Index (NDBI) is utilized to extract
built-up features using remote sensing imagery. Its other applications include
tracking spatial changes of a region Ranagalage et al. (2018) and to find
relation between urban heat islands and land cover Ogashawara & Bastos (2012).
Short Wave Infra-Red (SWIR) and Near Infra-Red (NIR) bands of remote sensing
images are used by the index. NDBI ranges from -1 to 1. The equation to find
NDBI is given in Eq. 4 (see also Fig. 5).

$NDBI=\frac{SWIR-NIR}{SWIR+NIR}$ (4)

NDMI: Normalized Difference Moisture Index (NDMI) is an index which is
utilized to extricate water bodies from satellite imagery. Green and Short
Wave Infra-Red (SWIR) bands of remote sensing images are used by the index.
NDMI ranges from -1 to 1. The equation to find NDMI is given in Eq. 5. Fig. 5
shows NDMI image at a brick kiln location. NDMI is used to find relation
between urban heat islands and land cover Ogashawara & Bastos (2012) as well
as several other applications.

$NDMI=\frac{Green-SWIR}{Green+SWIR}$ (5)

BAI: The Burned Area Index (BAI) was developed by Chuvieco et al. (2002) to
assist in the delineation of burn scars and assessment of burn severity. It is
based on the spectral distance to charcoal reflectance. We used following
expression to compute BAI.

$BAI=\frac{1.0}{(0.1-RED)^{2}+(0.06-NIR)^{2})}$ (6)

|  |
---|---|---
NDVI | NDBI | NDMI

Figure 3: Spectral Indices of kiln locations of Punjab, Pakistan (Darker
colour shows more index value).

###  A.1 Qualitative analysis of spectral indices

The kiln surrounding has a low vegetation index (EVI, NDVI), low moisture
index (NDMI) and a high built-up index (NDBI) (see Fig. 4 and Fig 5). Thus in
this work we classify brick kilns using mixture of spectral indices namely
Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index
(EVI), Normalized Difference Built-up Index (NDBI), Normalized Difference
Moisture Index (NDMI) and Burned Area Index (BAI).

|  |  |
---|---|---|---
(a) | (b) | (c) | (d)

Figure 4: Exemplary brick kiln images as seen in the reference high resolution
($\frac{0.5~{}meter}{pixel}$) imagery (a, c) [Courtesy: Digital Globe, Google
Earth] and the low resolution ($\frac{10~{}meters}{pixel}$) Sentinel-2 imagery
(b, d) with Mean NDVI values with $Opacity=1~{}\&~{}0.5$ resp. [Image
courtesy: Google Earth Engine].

|  |  |  |
---|---|---|---|---
Satellite Image | EVI | NDVI | NDMI | NDBI

Figure 5: Kiln surrounding has a low vegetation index (EVI, NDVI), low
moisture Index (NDMI) and a high built-up index (NDBI); (Darker colour shows
more index value) [Image courtesy: Google Earth Engine].

##  Appendix B Hyperparameters for the training of Orientation-aware YOLOv3

Optimization method is Adam with an initial learning rate of $0.001$. The
learning rate increases by $0.1$ if validation loss does not decline for $20$
epochs. Instead of using fixed number of epochs, we used early stopping
criteria which terminates the training process in case there is no improvement
for $50$ consecutive epochs.Unlike Zhang et al. (2019), in our work instead of
regressing out the value of $\theta$ directly, the methodology of Vanilla
YOLOv3 Redmon & Farhadi (2018) was retained but instead of using only one
class for the detection of brick kilns, the following $5$ classes were used:
Kiln-$0^{o}$, Kiln-$20^{o}$, Kiln-$40^{o}$, Kiln-$140^{o}$, and Kiln-$160^{o}$
which rotated the un-oriented bounding box (predicted by vanilla YOLOv3) by
$0$, $20$, $40$, $140$ and $160$ degrees respectively based on their
orientation. Using quantized values of $\theta$ rather than regression reduces
the search space and time taken in the training and testing/prediction stages.

##  Appendix C KilnNet Nazir et al. (2020) vs. Proposed Approach

The Key differences between KilnNet Nazir et al. (2020) and our proposed
approach are as follows (see Fig. 2):

  * •

In the prior approach Nazir et al. (2020), high resolution DigitalGlobe
imagery is used. Although use of high-resolution imagery improves the
accuracy, as mentioned in Fig. 10 of Nazir et al. (2020), it requires 93 days
to process the entire brick kiln belt ($1551997~{}km^{2}$) of South Asia. In
this work we addressed this concern and proposed an improved approach that
results in a $21\times$ improvement in speed with comparable or higher
accuracy when tested over multiple countries. We achieved this by a strategy
that uses low-resolution multi-spectral imagery as well as high-resolution
imagery. The low-resolution multi-spectral pre-filter bulk of data without
introducing missed detections. Thus the detailed analysis is only applied on a
very small chunk of high-resolution imagery.

  * •

In the prior methodology, two-stage gated neural network architecture
consisting of a ResNet-152 classifier and a YOLOv3 detector is proposed. Our
proposed coarse-to-fine strategy is also a two-staged approach with two major
differences. In the first stage we replace CNN based classification on high-
resolution ($0.149~{}\frac{meter}{pixel}$) data with fusion of low-resolution
($10~{}\frac{meter}{pixel}$) spectral indices to pre-filter bulk of the date
followed by orientation-aware YOLOv3 detector on the filtered data.
Furthermore, in this work we proposed a modified object detector that along
with bounding boxes also produces the orientation information.

  * •

In the previous methodology, classifier is selected by considering the class
imbalance issues using F-Beta measure. In the proposed approach we select a
classifier based on the observation that kiln locations have low vegetation
and moisture index whereas high build-up and burn area index.

  * •

The prior methodology takes $674$ seconds to process three datasets in Table 1
of the paper. As a result of the above mentioned differences the proposed
approach on the other hand reduces this compute time to $38$ seconds only.

In nutshell, our coarse-to-fine strategy aims to filter the bulk of the data
using spectral properties on Google Earth Engine while the detector is only
applied on small amount of positive detections as to generate localization
information while filtering false positives on Google Colab (Tesla T4). We
evaluated our trained network on unseen dataset consisting of Kasur
(Pakistan), New Delhi (India) and Deh Sabz (Afghanistan) for quantitative
analysis. (see Table 1. If the brick kiln is larger than 256 x 256 it is
detected in two image patches. To deal with this issue we describes the
duplicates in Table 1.

##  Appendix D Compute Cost Comparison with State-of-the-Art

The detection of brick kilns by analyzing the spectral properties on Google
Earth Engine takes approximately $3$, $4$ and $8$ seconds to process Kasur
(Pakistan), New Delhi (India) and Deh Sabz (Afghanistan), respectively. Then
the orientation aware detector: YOLOv3 is ran on potential brick kiln
locations including false positives on Google Colab Tesla T4 GPU. Each
experiment is repeated 5 times to find the the average time on Google Colab
which is 4.04, 4.16 and 15.1 to localize potential kilns in Kasur (Pakistan),
New Delhi (India) and Deh Sabz (Afghanistan), respectively (see Table 1).