Update README.md

README.md

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 ---
 license: cc-by-nc-4.0
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-# SentinelKiln Dataset
+# SentinelKilnDB - A Large-Scale Dataset and Benchmark for OBB Brick Kiln Detection in South Asia Using Satellite Imagery
 
-## About the Dataset
+## Abstract
 
-### 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.
+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.
+
+![Statistics](https://storage.googleapis.com/kagglesdsdata/datasets/8335452/13156901/statistics.png?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20250924%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20250924T095905Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
+---
+
+## 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]
 
 ---
 
-### Key Details
+## 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 meters per pixel  
+- **Spatial Resolution:** 10 m/pixel  
 - **Timeframe:** November 2023 – February 2024  
 - **Geographic Coverage:** Indo-Gangetic Plain, Afghanistan, Pakistan, Bangladesh  
-- **Overlap:** Images have a 30-pixel overlap  
+- **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
+## 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`
+## 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** to ensure balanced representation:
+## Dataset Splits
+
+The dataset is split using a **class-wise stratified approach** for balanced representation.
 
-| Split | Images (.png) | Label files (.txt) | No. of BBoxes | Folder  |
-|-------|---------------|--------------------|--------------|---------|
-| Train | 71,856        | 47,214             |63,787        | `train/`|
-| Val   | 23,952        | 15,738             |21,042        | `val/`  |
-| Test  | 18,492        | 10,278             |12,819        | `test/` |
+| 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 annotations and images: `train/`, `val/`, `test/`
+Each split contains separate folders for images and annotations:
+
+```
+dataset/
+├── train/
+│   ├── images/
+│   └── labels/
+├── val/
+│   ├── images/
+│   └── labels/
+└── test/
+    ├── images/
+    └── labels/
+```
+
+---
 
 ## Extracting Images from Parquet Files
 
@@ -75,4 +124,63 @@ for idx, row in train_df.iterrows():
 ```
 ### 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.
+* 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  
+
+```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/](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}
+}
+---
+![logo](https://storage.googleapis.com/kagglesdsdata/datasets/8335452/13157427/logo.png?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20250924%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20250924T103943Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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)
+