## 4 Proposed LU/LC Prediction Using Vision Transformer-Based Bi-LSTM Model
Our research on LU/LC prediction provides information to the forest departments, urban planners, and government officials to take necessary action in the protection of the LU/LC environment through application-based XAI. In the future, we plan to focus more on using the TIRS bands of hyperspectral data to obtain the temperature values associated with each pixel and to classify the hyperspectral data in real-time scenarios. [PERSON] conceived the study, created the literature review, and designed the flow of the proposed model; [PERSON] contributed to the satellite data acquisition, algorithm development, and writing of the manuscript; [PERSON] has contributed to testing the performance of the algorithm, and the internal review of the manuscript. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The input satellite images used in our research work can be accessed freely from different online sources. The availability of the LISS-III satellite images for our study area was checked, and the images can be downloaded freely in the open data archive from the website of the Bhuvan Indian Geo-Platform of ISRO ([[https://bhuvan.nrsc.gov.in/](https://bhuvan.nrsc.gov.in/)]([https://bhuvan.nrsc.gov.in/](https://bhuvan.nrsc.gov.in/)) (accessed on 9 December 2019)). The availability of the Landsat satellite images for our study area was checked, and the images can be downloaded freely from the website of the United States Geological Survey (USGS), United States ([[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)) (accessed on 16 December 2019)). The images available on the Google Earth Engine platform ([[https://www.google.com/earth/](https://www.google.com/earth/)]([https://www.google.com/earth/](https://www.google.com/earth/)) (accessed on 10 November 2021)) were used as the reference data during the accuracy assessment for different periods from 1984 to the current date. The authors wish to thank the United States Geological Survey (USGS), United States, for providing Landsat TIRS, RED, and NIR bands. We are thankful to Bhuvan Indian Geo-Platform of ISRO, India, for providing the LISS-III multispectral data. The authors also wish to thank the developers of the Google Earth Engine platform for providing the time-series data with less image resolution. We are thankful to the Vellore Institute of Technology for providing the VIT SEED GRANT for carrying out this work and the CDMM (Centre for Disaster Mitigation and Management) for providing a good lab facility. The authors declare no conflict of interest. ## References

* [1] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Assessment of land use land cover changes and future predictions using CA-ANN simulation for sclangor, Malaysia. _Water_**2022**, _14_, 402. [CrossRef]
* [2] [PERSON]; [PERSON] Analysis and mapping of present and future drivers of local urban climate using remote sensing: A case of Lahore, Pakistan. _Arab. J. Geosci._**2020**, _13_, 1-14. [CrossRef]
* [3] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Effects of preservation policy on land use changes in Iranian Northern Zagros forests. _Land Use Policy_**2019**, _81_, 76-90. [CrossRef]
* [4] [PERSON]; [PERSON]; [PERSON] Recent applications of Landsat 8/OLI and Sentinel-2/MSI for land use and land cover mapping: A systematic review. _Remote Sens._**2020**, _12_, 3062. [CrossRef][PERSON]; [PERSON]; [PERSON]; [PERSON] A comparative study of advanced land use/land cover classification algorithms using Sentinel-2 data. _Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci._**2018**, _42_, 20-23. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Urban growth dynamics of an Indian metropolitan using CA markov and logistic regression. _Egypt. J. Remote Sens. Space Sci._**2018**, _21_, 229-236. [CrossRef]
* [PERSON] et al.