## 5 Discussion  
Previously published remote sensing map for this region [19] only mapped the Jatta Gypsum Formation using ASTER data. The challenging problem of mapping other lithologies of high mineralogical similarities was overcome successfully through AI algorithms using relatively limited bands of Sentinel-2 MSI data. Improvements to the previously published geological map (Figure S1) were reported regarding the accuracy and lithological detail, as shown in Figures 7 and 8. We can see that the PA of both algorithms has some excellent results for each class except for the Muree Formation. This may be associated with the fact that this formation was thinly exposed on the ground compared to other formations and may have been affected by atmospheric effects, vegetation cover, the spectral and spatial resolution of the image, heterogeneity of the chemical and mineralogical composition of the rock at the sub-pixel level, and soil cover. The UA of both algorithms was greater than 90% for all rock types and had an excellent agreement with the previously published GSP maps. SVM had better accuracies for all formations compared to ANN and can therefore be considered a more generalized representation of the area. In addition, the high computational efficiency of GEE and high-resolution Sentinel-2 data are further advantages. The SVM classifier took five minutes to train and display results. Moreover, these accuracies and cost-effective solutions were better than the previously reported case studies of recently  
Figure 8: Field photographs of the lithost stratigraphic units in the mapped area. (**a**)—Looking north at contacts among the Kohat, Murree, and Kamilal formations. (**b**)—Showing evaporites (Jatta Gypsum and Bahadurkheil Salt). (**c**)—Looking northeast at contacts among the Jatta Gypsum, Chinji, and Nargi formations. (**d**)—Looking north at the contact between the Chinji and Nagri formations (man’s height = 6 feet).  
reported ML-based lithological mapping strategies [22; 40; 41; 42; 43; 70; 71]. ANN with four hidden layers, layer normalization with each hidden and input layer, categorical cross-entropy loss function, and Adam optimizer showed the best results with a training accuracy of 94.48%. However, the training time for this ANN model was 3 h. ANN was time-consuming since there is no built-in library for ANN in the GEE, and therefore it required TensorFlow Keras API through Google Colab. By linking GEE to Colab and uploading training data to Google Cloud in TF format, the predictions were uploaded back to the GEE for visualization. This process took 8 hrs. of data transfer for the same data size, compared to 5 min of SVM.  
The region is a high hydrocarbon- and minerals-producing area; therefore, such automated mapping tools would further lead to devising future strategies as an improved tool for further exploration beyond this region. The study can be extended further using explainable AI [72] to assess the learning of the model in the context of the response of input variables (bands) to the outputs (various formations). Further, the surface maps can be combined with AI-based spatial estimation models [72] applied to subsurface geochemical and geophysical data to develop 3D geological models of potential mineralized zones. The approaches will enable the mineral exploration and mining industry to achieve Industry 4.0 [73] through IoT [74; 75; 76] and blockchain [77] solutions for secure data sharing within the mining industry.