The questions are presented in blue, with our corresponding responses shown in black. For what purpose was the dataset created? Was there a specific task in mind? This dataset was created for academic and research purposes to advance scientific understanding and support policy development on air quality and sustainability issues. The findings highlight important opportunities to improve regulatory compliance and encourage the adoption of cleaner technologies within the brick kiln sector, which is a significant contributor to regional air pollution. Beyond its environmental relevance, this dataset is especially valuable for the fields of object detection and computer vision. It provides a large-scale, hand-validated collection of brick kiln locations annotated with oriented bounding boxes (OBBs) on freely available Sentinel-2 satellite imagery.

Air pollution was responsible for 2.6 million deaths across South Asia in 2021 alone, with brick manufacturing contributing significantly to this burden. In particular, the Indo-Gangetic Plain; a densely populated and highly polluted region spanning northern India, Pakistan, Bangladesh, and parts of Afghanistan sees brick kilns contributing 8-14% of ambient air pollution. Traditional monitoring approaches, such as field surveys and manual annotation using tools like Google Earth Pro, are time and labor-intensive. Prior ML-based efforts for automated detection have relied on costly high-resolution commercial imagery and non-public datasets, limiting reproducibility and scalability. In this work, we introduce SENTINELKILNDB, a publicly available, hand-validated benchmark of 62,671 brick kilns spanning threekiln types Fixed Chimney Bull's Trench Kiln (FCBK), Circular FCBK (CFCBK), and Zigzag kilns - annotated with oriented bounding boxes (OBBs) across 2.8 million km2 using free and globally accessible Sentinel-2 imagery. We benchmark state-of-the-art oriented object detection models and evaluate generalization across in-region, out-of-region, and super-resolution settings. SENTINELKILNDB enables rigorous evaluation of geospatial generalization and robustness for low-resolution object detection, and provides a new testbed for ML models addressing real-world environmental and remote sensing challenges at a continental scale. Datasets and code are available in SentinelKilnDB Dataset and SentinelKilnDB Bench-mark, under the Creative Commons Attribution-NonCommercial 4.0 International License.

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.

Police seized ketamine hidden inside life-size Transformer robots in Thailand. A woman who was previously caught trying to ship meth hidden in a food processing machine was trying to send the robots to Taiwan. Cambodian authorities on Friday destroyed more than seven tons of illicit drugs and the ingredients for them, as a drug-fighting official said educating people about their danger is the best way of combating the illegal trade. Some 4.1 tons of the destroyed items were drugs including heroin, marijuana, methamphetamine, ecstasy and ketamine that had been confiscated from traffickers across the country, the National Authority for Combating Drugs said. The remaining 3.2 tons were various chemicals and other ingredients used to produce illegal drugs, it said.

The quantities of physical capital, or object counts, provide important insights into human activities and the socio-economic development of a region. For example, the number of buildings reflects the level of urbanization in a region; the number of brick kilns is related to the level of air pollution, and the number of cars correlates with the poverty level of a region. For example, the Demographic and Health Surveys (DHS) collects population-related statistics of about 90 countries at a cost of 1.9 million dollars over a five-year interval [1]. Recently, object detection in high-resolution satellite imagery has emerged as an alternative to ground-based survey data collection in socioeconomic monitoring tasks like counting brick kilns in Bangladesh [2] and counting solar panels in the U.S. [3]. A common detection-based pipeline [2, 4] to collect object count statistics over a large region exhaustively downloads all satellite images covering the target region, counts the objects in each image using a trained detection model, and takes the summation of counts in all the images to produce a total count.

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.

A new crowdsourcing project aims to identify South Asian brick kilns – frequently the site of forced labour – in satellite images. This data will then be used to train machine learning algorithms to automatically recognise brick kilns in satellite imagery. He's already working on the next stage of the project, which will use a similar approach to help identify open pit mines in countries such as the Democratic Republic of the Congo, which are also often sites of forced labour. TraffickCam, a project set up by the social action group Exchange Initiative, uses image recognition to identify sex trafficking in hotel rooms.

Online volunteers are helping to track slavery from space. A new crowdsourcing project aims to identify South Asian brick kilns – frequently the site of forced labour – in satellite images. This data will then be used to train machine learning algorithms to automatically recognise brick kilns in satellite imagery. If computers can pinpoint the location of possible slavery sites, then the coordinates could be passed to local non-governmental organisations to investigate, says Kevin Bales, who is leading the project at the University of Nottingham in the UK. South Asian brick kilns are notorious sites of modern-day slavery.