Detection of Brick Kilns Using Multi-Spectral Bands of Sentinel-2 Imagery

The burning of bricks affects air quality and human health, and the toxic pollutants released into the air seriously impact the surrounding areas. In order to curb the pollution caused by these brick kilns, it is very important to identify them. As part of this study, an analysis in the Delhi- NCR region is performed. An approach for mapping brick kiln locations by using various Machine Learning (ML) algorithms is presented in this work. The Sentinel-2 imagery with 10m resolution was used to create four classes, namely, built up, field, water, and brick kiln. It is possible to distinguish brick kilns from other classes by using the said dataset due to its unique spectral and geometric features. Several ML classifiers have been trained on the sentinel-2 dataset, including the Random Forest Classifier and four built-in classifiers of Google Earth Engine, such as Smile Random Forest, Smile CART (Classification and Regression Trees), Smile Gradient Tree Boost, and Smile Naive Bayes Classifier. In order to train the model based on different classifiers, 70% of data points are used for training and 30% for validation purposes. Among the machine learning classifiers, Smile Random Forest achieved the maximum accuracy of 96.4%. As a result of this study, an ML model has been proposed for the classification of brick kilns from Sentinel-2 imagery, and a comparative analysis has been done between the various ML models. The proposed ML model can classify brick kilns from Sentinel-2 images with an accuracy of 96.4%, precision of 0.88, and recall of 0.73. The functionality of the classification model lies in its utilization by local authorities and various pollution control bodies, so that proper action can be initiated. Hence contributing to a greener environment and battling serious issues like climate change and global warming.