Datasets:

SustainabilityLabIITGN
/

SentinelKilnDB

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. Datasets and code are available in SentinelKiln Dataset and SentinelKiln Benchmark, under the Creative Commons Attribution–NonCommercial 4.0 International License.

Useful Links

Project Page - https://lnkd.in/dn2SKwWv
Official Paper - https://neurips.cc/virtual/2025/poster/121530
Github - https://github.com/rishabh-mondal/NeurIPS_2025
Sustainability Lab - https://sustainability-lab.github.io

For questions or collaborations, please contact:

Rishabh Mondal - [email protected]
Nipun Batra - [email protected]

Dataset Overview

This dataset contains Sentinel-2 satellite imagery focused on identifying and classifying brick kilns across the Indo-Gangetic Plain and neighboring South Asian countries, including Afghanistan, Pakistan, and Bangladesh.

Imagery Source: Sentinel-2 (Surface Reflectance)
Image Size: 128 × 128 pixels
Spatial Resolution: 10 m/pixel
Timeframe: November 2023 – February 2024
Geographic Coverage: Indo-Gangetic Plain, Afghanistan, Pakistan, Bangladesh
Overlap: 30-pixel overlap between patches
File Naming Convention: lat,lon.png and lat,lon.txt

Classes

CFCBK – Continuous Fixed Chimney Bull’s Trench Kiln
FCBK – Fixed Chimney Bull’s Trench Kiln
Zigzag – Zigzag Kiln

Annotation Formats

YOLO OBB:

class_name, x1, y1, x2, y2, x3, y3, x4, y4

YOLO AA:

class_name, x_center, y_center, width, height

DOTA Format:

x1, y1, x2, y2, x3, y3, x4, y4, class_name, difficult

Dataset Splits

The dataset is split using a class-wise stratified approach for balanced representation.

Split	Images (.png)	Label Files (.txt)	No. of BBoxes
Train	71,856	47,214	63,787
Val	23,952	15,738	21,042
Test	18,492	10,278	12,819
Total	114,300	73,239	97,648

Each split contains separate folders for images and annotations:

dataset/
├── train/
│   ├── images/
│   └── labels/
├── val/
│   ├── images/
│   └── labels/
└── test/
    ├── images/
    └── labels/

Extracting Images from Parquet Files

The dataset stores all images in Parquet format as raw bytes. To convert these bytes back into .png images, you can use the following Python code:

import pandas as pd
from PIL import Image
import io
import os

# Load Parquet
train_df = pd.read_parquet("train/train.parquet")

# Directory to save images
output_dir = "train/images"
os.makedirs(output_dir, exist_ok=True)

# Loop over all rows
for idx, row in train_df.iterrows():
    imagename = row['image_name']
    image_bytes = row['image']

    # Convert bytes to PIL Image
    img = Image.open(io.BytesIO(image_bytes))

    # Save image as PNG
    save_path = os.path.join(output_dir, f"{imagename}")
    img.save(save_path)

Note:

This will create a folder train/images/ and save all images as .png.
You can modify the path if your Parquet file is in a different location or if you want to save images elsewhere.

Usage

Example: loading labels in Python

import pandas as pd

# Example for YOLO AA format
labels = pd.read_csv("dataset/train/yolo_aa_labels/28.64,77.21.txt/28.64,77.21.txt", sep=" ", header=None)
labels.columns = ["class", "x_center", "y_center", "width", "height"]
print(labels.head())

# Example for YOLO OBB format
labels = pd.read_csv("dataset/train/yolo_obb_labels/28.64,77.21.txt", sep=" ", header=None)
labels.columns = ["class", "x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4"]
print(labels.head())

# Example for DOTA format
labels = pd.read_csv("dataset/train/dota_labels/28.64,77.21.txt/28.64,77.21.txt", sep=" ", header=None)
labels.columns = ["x1","y1","x2","y2","x3","y3","x4","y4","class","difficult"]
print(labels.head())

Statistics

Total Kilns: 62,671
- CFCBK: 1,944
- FCBK: 33,963
- Zigzag: 26,764
Negative Samples: 41,068 (tiles with no kilns)

License

This dataset is released under the Creative Commons Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0).
See: https://creativecommons.org/licenses/by-nc/4.0/

Citation

If you use SentinelKilnDB in your research, please cite:

@inproceedings{mondal2025sentinelkilndb, title={SentinelKilnDB: A Large-Scale Dataset and Benchmark for OBB Brick Kiln Detection in South Asia Using Satellite Imagery}, author={Rishabh Mondal and Jeet Parab and Heer Kubadia and Shataxi Dubey and Shardul Junagade and Zeel B. Patel and Nipun Batra}, booktitle={The Thirty-Ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2025}, url={https://openreview.net/forum?id=efGzsxVSEC} }