## 4 Proposed LU/LC Prediction Using Vision Transformer-Based Bi-LSTM Model
The backend geospatial software such as QGIS of version 3.6.1 developed by QGIS Development Team ([[https://qgis.org/en/site/](https://qgis.org/en/site/)]([https://qgis.org/en/site/](https://qgis.org/en/site/))), ArcGIS of version 10.1 developed by ESRI ([[http://www.esri.com/software/arcgis](http://www.esri.com/software/arcgis)]([http://www.esri.com/software/arcgis](http://www.esri.com/software/arcgis))) and Google Earth Engine developed by Google ([[https://www.google.com/earth/](https://www.google.com/earth/)]([https://www.google.com/earth/](https://www.google.com/earth/))) was used for preparing and analyzing the satellite data. ### Training Data and Parameter Settings

For appropriate mapping of the input features to the output features using machine-learning or a deep-learning model, the training data and its parameters were used and tuned. Algorithm 1 shows the detailed procedure of our research on LU/LC prediction. The multispectral input map (\(M\)) of our study area Javadi Hills for the year 2012 and 2015 was considered as (\(I_{1},~{}I_{2}\)). The preprocessed multispectral image was processed for the further processing of our model. The training samples of an image are divided into patches. The 16 patches (size = 64 \(\times\) 4) were extracted from the input training image (256 \(\times\) 256), of which each patch contains the trained LU/LC classes (high and less vegetation). The training samples for the area of Javadi Hills were generated through the latitude and longitudinal coordinates of Javadi Hills manually through Google Earth image. For the input image of Javadi Hills for the years 2012 and 2015, the LU/LC classification was performed through the Vision Transformer model. The working process of the Vision Transformer model was explained in Section 3.2. For a better understanding of our training samples in the patched image, we show the trained patches of 1 and 16 in Figure 17. The hyper-parameters used during the training process of the Vision Transformer model are shown in Table 2. The output extracted at the end of the fully connected layer was used as the LU/LC classified map for further processing. After the classification, each classified sample was tested through the referenced data of Google Earth images. The LU/LC classified image (\(LU_{I}\)) was tested through the referenced Google Earth image. Each reference datum was labeled according to the respective LU/LC classes of the Javadi Hills through Google Earth images. The LU/LC class considered in our research work includes the high- and less-vegetation regions of the forest- and non-forest-covered regions of Javadi Hills. For better understanding, we have shown the validation of the point shape file with the Google Earth images in Figure 18, and the class values associated with each coordinate of the trained image are shown in Table 3. The accuracy assessment was calculated for the Vision Transformer model, and the results are shown in Section 5.2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Learning Rate & 0.001 \\ Weight Decay & 0.0001 \\ Batch Size & 10 \\ Number of epochs & 100 \\ Image size & 256 \(\times\) 256 \\ Patch size & 64 \\ Patches per image & 16 \\ Number of heads & 4 \\ Transformer Layers & 8 \\ Activation Function & GeLU \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters of the Vision Transformer model. Figure 17: Trained patches for the area of Javadi Hills. The percentage of LU/LC change detection was calculated for the LU/LC classified image, and the results are shown in Section 5.3. Based on the good accuracy, the LU/LC classification map was processed for further findings of the LU/LC prediction map. The LST map for the years 2012 and 2015 was calculated to extract the spatial features of Javadi Hills. The estimation of the LST map was explained in Section 3.3. The LST map shows the features of the high- and low-temperature values of the earth's surface of Javadi Hills.