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The project focused on the ‘Brick-Kiln-Belt’ of South Asia, a corridor which stretches across parts of Pakistan, India, Bangladesh, and Nepal. Over 55,000 brick kilns are thought to be in this region, many of which are unregulated, small-scale industries. Although a major source of employment, these kilns are a significant source of air pollution, including smog harmful to human health. They are also responsible for a large proportion of modern-day slavery and child labour, and cause severe environmental impacts through soil degradation and water extraction.
With the Brick Kiln Belt spanning 1.5 million km2 and crossing country borders, it would be impossible for law enforcers in these resource-poor areas to monitor it using ‘on the ground’ methods alone. An alternative approach is to identify the distinct shape of brick kilns from aerial images captured by satellites. Doing this manually, however, is highly time consuming and prone to error. Although some studies have applied machine learning to aerial images to try to detect illegal brick kilns automatically, none have so far produced a highly scalable solution.
A collaboration between the Center of Urban Informatics, Technology and Policy at Lahore University of Management Sciences, Pakistan, and the University of Oxford’s Centre for Statistics in Medicine, developed an improved model that can detect brick kilns from aerial images up to 21 times faster than existing techniques. The model uses a unique two-stage computational process where images are first filtered at a low-resolution so that only those that contain a likely brick kiln proceed to the next stage. This lowers the computational power required, making it a highly scalable solution.