Update README.md

README.md

@@ -15,9 +15,12 @@ size_categories:
   - 1K<n<10K
 ---
 
-# LandsatQuake
+# LandsatQuake: A Large-Scale Dataset For Practical Landslide Detection
 
-Landslide detection in Landsat satellite imagery across 16 earthquake and rainstorm events (1987–2020).
+**Paper:** [LandsatQuake (ICLR 2025 Workshop on Machine Learning for Remote Sensing)](https://iclr.cc/virtual/2025/36761)  
+**Authors:** Vihaan Akshaay Rajendiran, Amanda Roeliza Hunt, Gen Li, Lei Li
+
+Identifying landslides from remote imagery is critical for rapid responses after landslide hazards and for assessing their environmental impacts. LandsatQuake is a benchmark dataset composed of 31 landslide inventories from 21 earthquake-prone regions across the world, covering a total area of 5.56 × 10⁷ km² and spanning the last 40 years. This dataset emphasises practicality by using satellite images acquired by Landsat — the only satellite system that has recorded Earth's land surface for over 40 years.
 
 ## ⚠️ Important: Do NOT use `load_dataset()`
 
@@ -119,3 +122,18 @@ img = Image.open("LandsatQuake/train/1987 Sichuan pre-earthquake_patch_2464_1187
 boxes = parse_voc_xml("LandsatQuake/train/1987 Sichuan pre-earthquake_patch_2464_11872.xml")
 print(f"Image size: {img.size}, Landslide boxes: {len(boxes)}")
 ```
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@inproceedings{rajendiran2025landsatquake,
+  title={LandsatQuake: A Large-Scale Dataset For Practical Landslide Detection},
+  author={Rajendiran, Vihaan Akshaay and Hunt, Amanda Roeliza and Li, Gen and Li, Lei},
+  booktitle={3rd ICLR Workshop on Machine Learning for Remote Sensing},
+  year={2025},
+  url={https://iclr.cc/virtual/2025/36761}
+}
+```
+