## 4 Proposed LU/LC Prediction Using Vision Transformer-Based Bi-LSTM Model
A variation of results was observed for our study area with the assessment of multi-satellite datasets through the proposed algorithm. We delivered a clear view of the importance of Vision Transformer-based LU/LC classification and Bi-LSTM-based prediction for forecasting the time series LU/LC vegetation map. The advantage of our proposed work lies in using only the LST map as the spatial data for predicting the LU/LC vegetation map. We also achieved a good prediction accuracy of 98.38%. Our proposed algorithm can be applied to other study areas around the world in predicting the LU/LC vegetation map. Moreover, our proposed model has been efficient for urban planners, forest departments, and government officials in analyzing the LU/LC information through XAI and taking necessary actions in the protection of the LU/LC environment. ## 7 Conclusions

The LU/LC prediction modeling was considered important research in the area of remote sensing. In this research work, the multispectral LISS-III and Landsat satellite image of Javadi Hills for the periods 2012 and 2015 were downloaded and performed \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study Area** & **Input Data** & **Prediction Accuracy (\%)** \\ \hline \multirow{4}{*}{Javadi Hills, India} & LU/LC Classification—LST Map & 98.38 \\ \cline{2-3}  & LU/LC Classification—Slope Map & 92.33 \\ \cline{2-3}  & LU/LC Classification—Distance from & 91.64 \\ \cline{2-3}  & Road Map & \\ \cline{2-3}  & LU/LC Classification—Slope, Distance & \\ \cline{2-3}  & from road map & 92.52 \\ \cline{2-3}  & LU/LC Classification—Slope, LST map & 93.45 \\ \cline{2-3}  & LU/LC Classification—Distance from & \\ \cline{2-3}  & Road, LST map & 93.17 \\ \cline{2-3}  & LU/LC Classification—Slope, Distance & \\ \cline{2-3}  & from Road, LST map & 94.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Testing of the Vision Transformer–based Bi-LSTM model using the various combinations of Input Spatial Data for Javadi Hills, India. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study Area** & **Algorithm** & **Prediction Accuracy (\%)** \\ \hline Javadi Hills, India (our study) & Vision Transformer–Based Bi-LSTM Model & 98.38 \\ \hline \multirow{2}{*}{Wuhan, China [28]} & Self-Adaptive Cellular-Based Deep-Learning & 93.1 \\  & LSTM Model & 95.86 \\ \cline{2-3}  & Deep Learning & \\ Guangdong province of South China [23] & (RNN–CNN) and CA–MC Model & 93.46 \\ \hline Western Fansu Province, China [26] & CNN–GRU Model & 90.21 \\ \hline Dhaka, Bangladesh [29] & CA–MC and ANN Model & 90.21 \\ \hline University of Nebraska–Lincoln [24] & CNN–Bi-LSTM Model & 91.73 \\ \hline City of Surrey, British Columbia [56] & RNN–ConvLSTM Model & 88.0 \\ \hline Klingenberg, Germany [55] & Fully CNN–LSTM Model & 87.0 \\ \hline Awadh, Lucknow [6] & CA–MC and LR model & 84.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparative analysis of LU/LC prediction models for different study areas. for analyzing the LU/LC prediction for the years 2018, 2021, 2024, and 2027. The Vision Transformer model for performing the LU/LC classification was proposed, and the accuracy assessment was performed by using Google Earth Images. The average classification accuracy obtained for our Vision Transformer model was 98.76%. The spatial features from the LST map and LU/LC classified map were used as input for predicting the LU/LC changes in Javadi Hills. For predicting the future LU/LC changes of Javadi Hills, the Bi-LSTM model was successfully applied. We infer that the impact of the LST spatial features with the LU/LC classified map provides a good percentage of results with 98.38%. The predicted results provide the variation in the high- and less-vegetation regions of Javadi Hills from 2012 to 2027. Our Vision Transformer-based Bi-LSTM model has produced good validation results when compared with other standardized models.