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Detecting and Categorizing Brick Kilns from Satellite Imagery

  • 🔬 Data Science
  • 🥠 Deep Learning and Object Detection

Introduction

With rapid urbanization, demand for bricks is increasing. Brick production is a very large, traditional industry in many parts of Asia. The brick sector in India, although unorganized, is huge in size. India is the second largest producer of bricks in the world — China dominates with 54% share[1].

Brick kilns are a major contributor in air pollution in North India. Production of bricks leads to emission of harmful gases and particulate matter, including toxic elements such as carbon monoxides and oxides of Sulphur (SOx). Exposure to these emissions is extremely hazardous to health and can impact physical and mental growth of children.

Most of the brick kilns in India are of the Fixed Chimney Bull’s Trench Kilns (FCBTK) type. However, there is a newer design of kilns, known as the Zigzag kiln, which is more energy efficient and causes much lesser air pollution. The older design kilns can be converted into the Zigzag design leading to increase in efficiency — however, there is an cost involved in retrofitting and this is leading to slower adoption of this technology.

Central Pollution Control Board (CPCB) issued a directive in June 2017 mandating brick kilns across India to convert to the less polluting zigzag setting design. This directive clearly stated that brick kilns operating without permission would be shut down [2]. Despite the directive, there are many brick kilns that are still operating without following the prescribed design norms.

In this sample, we will use Deep Learning on ArcGIS Platform to detect the location and design category of all brick kilns around Delhi NCR area in India to find the brick kilns which are not following the directions from CPCB. Deep Learning is a tried and tested method for object detection on satellite imagery and high level steps that we will follow are:

  • Collect Data using ArcGIS Pro
  • Train Deep Learning model using arcgis.learn
  • Deploy the trained model using ArcGIS Pro

We will be using the ESRI World Imagery basemap layer to train the model, and for a comparative analysis we will be using the ESRI World Imagery basemap layer from year 2014 ( this historical imagery is available on Esri’s Living Atlas and can be browsed using the wayback imagery tool).