Novel Vision Transformer–Based Bi-LSTM Model for LU/LC Prediction—Javadi Hills, India

Continuous monitoring and observing of the earth’s environment has become interactive research in field remote sensing. Many researchers have provided Land Use/Land Cover information for past, present, future their study areas around world. This work builds Novel Vision Transformer–based Bidirectional long-short term memory model predicting Changes by using LISS-III Landsat bands forest- non-forest-covered regions Javadi Hills, India. The proposed Transformer achieves a good classification accuracy, with an average 98.76%. impact Surface Temperature map provides validation results, accuracy 98.38%, during process bidirectional long short-term memory–based prediction analysis. authors also introduced application-based explanation predicted results through Google Earth Engine platform Cloud so that will be more informative trustworthy to urban planners forest department take proper actions protection environment.