# Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India

arXiv: https://arxiv.org/abs/2402.13796 · HTML: https://mlsanity.com/paper/2402.13796/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Grad-CAM: Why did you say that?](https://mlsanity.com/paper/1611.07450/) (2016)
- [Generating Interpretable Poverty Maps using Object Detection in Satellite Images](https://mlsanity.com/paper/2002.01612/) (2020)
- [Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale](https://mlsanity.com/paper/1704.02965/) (2017)
- [Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery](https://mlsanity.com/paper/1812.01756/) (2018)
- [Predicting Forest Fire Using Remote Sensing Data And Machine Learning](https://mlsanity.com/paper/2101.01975/) (2021)
- [Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data](https://mlsanity.com/paper/2010.14977/) (2020)
- [Superpixels and Graph Convolutional Neural Networks for Efficient Detection of Nutrient Deficiency Stress from Aerial Imagery](https://mlsanity.com/paper/2104.10249/) (2021)

## Built on by (most cited first, 3 of 3)

- [Brick Kiln Dataset for Pakistan's IGP Region Using AI](https://mlsanity.com/paper/2412.00052/) (2024)
- [Space to Policy: Scalable Brick Kiln Detection and Automatic Compliance Monitoring with Geospatial Data](https://mlsanity.com/paper/2412.04065/) (2024)
- [Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data](https://mlsanity.com/paper/2602.13350/) (2026)

## Abstract (arXiv)

Air pollution kills 7 million people annually. Brick manufacturing industry is the second largest consumer of coal contributing to 8%-14% of air pollution in Indo-Gangetic plain (highly populated tract of land in the Indian subcontinent). As brick kilns are an unorganized sector and present in large numbers, detecting policy violations such as distance from habitat is non-trivial. Air quality and other domain experts rely on manual human annotation to maintain brick kiln inventory. Previous work used computer vision based machine learning methods to detect brick kilns from satellite imagery but they are limited to certain geographies and labeling the data is laborious. In this paper, we propose a framework to deploy a scalable brick kiln detection system for large countries such as India and identify 7477 new brick kilns from 28 districts in 5 states in the Indo-Gangetic plain. We then showcase efficient ways to check policy violations such as high spatial density of kilns and abnormal increase over time in a region. We show that 90% of brick kilns in Delhi-NCR violate a density-based policy. Our framework can be directly adopted by the governments across the world to automate the policy regulations around brick kilns.