Update README.

README.md

@@ -27,19 +27,31 @@ The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi
 5. SWIR 1
 6. SWIR 2
 ![](burn_scar.png)
-It is important to point out that the HLS Burn Scar Scenes dataset includes a single timestep, while the Prithvi-100m was pretrained with 3 timesteps. This highlights the flexibility of this model to adapt to different downstream tasks and requirements.
+It is important to point out that the HLS Burn Scar Scenes dataset includes a single timestep, while the Prithvi-100m was pretrained with three timesteps. The difference highlights the flexibility of this model to adapt to different downstream tasks and requirements.
 
 ### Code
-Code for Finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main)
+Code for fine-tuning is available through [Github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main/configs)
 
-Configuration used for finetuning is available through [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/configs/burn_scars.py)
+Configuration used for fine-tuning is available through [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/configs/burn_scars.py)
 ).
 
 ### Results
-The experiment by running the mmseg stack for 50 epochs using the above config led to an IoU of **0.73** on the burn scar class and **0.96** overall accuracy. It is noteworthy that this leads to a resonably good model, but further developement will most likely improve performance.
+The experiment conducted by running the mmseg stack for 50 epochs using the above config led to an IoU of **0.73** on the burn scar class and **0.96** overall accuracy. It is noteworthy that this leads to a reasonably good model, but further developement will most likely improve performance.
 
 ### Inference and demo
-The github repo includes an inference script that allows to run the burn scar model for inference on HLS images. These input have to be geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units [0-1]. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**.
-
-
-
+The github repo includes an inference script that allows to run the burn scar model for inference on HLS images. These inputs have to be in geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units [0-1]. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**.
+
+### Citation
+
+If this model helped your research, please cite `Prithvi-100M-burn-scar` in your publications. Here is an example BibTeX entry:
+
+```
+@misc{Prithvi-100M-burn-scar,
+    author = {Roy, Sujit and Phillips, Christopher and Jakubik, Johannes and Fraccaro, Paolo and Ankur, Kumar and Avery, Ryan and Ji, Wei and Zadrozny, Bianca and Ramachandran, Rahul},
+    doi    = {https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar},
+    month  = aug,
+    title  = {{Prithvi 100M burn scar}},
+    url    = {https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-burn-scar},
+    year   = {2023}
+}
+```