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 three kiln 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.
@inproceedings{mondal2025sentinelkilndb,title={SentinelKilnDB: A Large-Scale Dataset and Benchmark for OBB Brick Kiln Detection in South Asia Using Satellite Imagery},author={Mondal, Rishabh and Parab, Jeet and Kubadia, Heer and Dubey, Shataxi and Junagade, Shardul and Patel, Zeel Bharatkumar and Batra, Nipun},booktitle={Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track},volume={38},publisher={Curran Associates, Inc.},year={2025},doi={10.52202/085713-3593},}
