Air pollution kills 7 million people annually. The brick kiln sector significantly contributes to economic development but also accounts for 8-14\% of air pollution in India. Policymakers have implemented compliance measures to regulate brick kilns. Emission inventories are critical for air quality modeling and source apportionment studies. However, the largely unorganized nature of the brick kiln sector necessitates labor-intensive survey efforts for monitoring. Recent efforts by air quality researchers have relied on manual annotation of brick kilns using satellite imagery to build emission inventories, but this approach lacks scalability. Machine-learning-based object detection methods have shown promise for detecting brick kilns; however, previous studies often rely on costly high-resolution imagery and fail to integrate with governmental policies. In this work, we developed a scalable machine-learning pipeline that detected and classified 30638 brick kilns across five states in the Indo-Gangetic Plain using free, moderate-resolution satellite imagery from Planet Labs. Our detections have a high correlation with on-ground surveys. We performed automated compliance analysis based on government policies. In the Delhi airshed, stricter policy enforcement has led to the adoption of efficient brick kiln technologies. This study highlights the need for inclusive policies that balance environmental sustainability with the livelihoods of workers.

Like superheroes capable of seeing through obstacles, environmental regulators may soon wield the power of all-seeing eyes that can identify violators anywhere at any time, according to a new Stanford University-led study. The paper, published the week of April 19 in Proceedings of the National Academy of Sciences (PNAS), demonstrates how artificial intelligence combined with satellite imagery can provide a low-cost, scalable method for locating and monitoring otherwise hard-to-regulate industries. Go to the web site to view the video. Brick production, a major industry in South Asia, is a source of pollution that threatens health. Regulating brick kilns is difficult because there is no database of kiln locations.

Like superheroes capable of seeing through obstacles, environmental regulators may soon wield the power of all-seeing eyes that can identify violators anywhere at any time, according to a new Stanford University-led study. The paper, published the week of April 19 in Proceedings of the National Academy of Sciences (PNAS), demonstrates how artificial intelligence combined with satellite imagery can provide a low-cost, scalable method for locating and monitoring otherwise hard-to-regulate industries. "Brick kilns have proliferated across Bangladesh to supply the growing economy with construction materials, which makes it really hard for regulators to keep up with new kilns that are constructed," said co-lead author Nina Brooks, a postdoctoral associate at the University of Minnesota's Institute for Social Research and Data Innovation who did the research while a Ph.D. student at Stanford. While previous research has shown the potential to use machine learning and satellite observations for environmental regulation, most studies have focused on wealthy countries with dependable data on industrial locations and activities. To explore the feasibility in developing countries, the Stanford-led research focused on Bangladesh, where government regulators struggle to locate highly pollutive informal brick kilns, let alone enforce rules.

In response, the government takes some scripted measures like shutting down schools on occasion and enforcing the infamous Odd-Even scheme, forcing half the cities cars off the streets. Pollution in Delhi has several causes: seasonal stubble burning in neighboring states, vehicular emission, as well as smoke from power plants and brick kilns dotting the national capital region. Fighting pollution needs a multi-pronged approach -- government policies alone are not enough, they need to be coupled with action on the ground. Let's take brick kilns as an example. Before we can address the pollution caused by them, we need to know exactly how many such kilns are there and their location, whether they are increasing in number or decreasing, and how many are adopting technology to reduce emission, as mandated by the law. Satellite imagery coupled with deep learning can answer these questions, increase accountability and drive results on the ground.

Such technologically aided tracking could be a big breakthrough for anti-slavery organizations that work in the region. These groups have already identified broad swaths of Pakistan, Nepal, and India--nicknamed the Brick Belt--where these kilns operate. The kilns are easy to spot in aerial photos, so the team has been using satellite images to pinpoint their locations. But they've been slowed by the vetting speeds of human volunteers. By manually mining the enormous set of images from the region, they've only been able to catalogue a fraction of the estimated 20,000 to 50,000 sites. Now, however, SFS aims to have a computer do that painstaking work.