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@@ -38,9 +38,12 @@ For more details, check out the tutorials below which guide the user through the
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  1. Check out the [Getting Started Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/1_getting_started.ipynb)
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- 2. For data download and data pre-processing to create your own dataset check out the [Download Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/2_download_data.ipynb) and the [Preprocessing Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/3_preprocess_data.ipynb)
 
 
 
 
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- 3. For Tweening (Temporal Gap-Filling) check out the [Introduction to LST Tweening Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/4_introduction_to_LST_Tweening.ipynb) for a tutorial on how to implement Tweening and the [Tweening Data Preparation Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/5_tweening_data_preparation.ipynb) for a tutorial on preparing the data for Tweening
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  ## Model Description
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  We present an application of the granite-geospatial-land-surface-temperature model for temporal gap filling (“Tweening” or in betweening). This approach attempts to solve for the temporal limitations in LST observations by synthesizing hourly inputs of stacked HLS and ERA5 temperature statistics.
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  For more details on this approach, refer to:
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- - [Introduction to LST Tweening](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/4_introduction_to_LST_Tweening.ipynb)
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  ## Model Releases (along with the branch name where the models are stored):
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  For more details on the download and preprocessing pipelines used to produce the fine-tuning and inference datasets, please refer to:
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- - [Download](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/2_download_data.ipynb)
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- - [Preprocessing](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/3_preprocess_data.ipynb)
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  ## Model Card Authors
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  1. Check out the [Getting Started Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/1_getting_started.ipynb)
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+ 2. For Tweening (Temporal Gap-Filling) check out the [Introduction to LST Tweening Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/2_introduction_to_LST_Tweening.ipynb) for a tutorial on how to implement Tweening and the [Tweening Data Preparation Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/3_tweening_data_preparation.ipynb) for a tutorial on preparing the data for Tweening.
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+ [<b><i>>>Try LST Tweening on Colab<<</i></b>](https://colab.research.google.com/github/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/2_introduction_to_LST_Tweening.ipynb)
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+ 3. For data download and data pre-processing to create your own dataset check out the [Download Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/4_download_data.ipynb) and the [Preprocessing Notebook!](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/5_preprocess_data.ipynb)
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  ## Model Description
 
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  We present an application of the granite-geospatial-land-surface-temperature model for temporal gap filling (“Tweening” or in betweening). This approach attempts to solve for the temporal limitations in LST observations by synthesizing hourly inputs of stacked HLS and ERA5 temperature statistics.
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  For more details on this approach, refer to:
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+ - [Introduction to LST Tweening](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/2_introduction_to_LST_Tweening.ipynb)
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  ## Model Releases (along with the branch name where the models are stored):
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  For more details on the download and preprocessing pipelines used to produce the fine-tuning and inference datasets, please refer to:
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+ - [Download](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/4_download_data.ipynb)
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+ - [Preprocessing](https://github.com/ibm-granite/granite-geospatial-land-surface-temperature/blob/main/notebooks/5_preprocess_data.ipynb)
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  ## Model Card Authors
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