# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 5 Discussion
### Comparison with Existing Maps  
Using data from ASTER and OLI satellite imagery, the task of mapping different lithologies with mineraled dissimilarities was successfully completed using remote sensing techniques and AI algorithms. Figure 17 is used to support the argument that the lithological detail and accuracy of the previously published geological map had improved. The current study's integrated remote sensing-based approach effectively addresses these drawbacks of traditional geological mapping. Researchers [113] employed ASTER data along with field data to map the lithological variance within an accretionary complex. Geological mapping at 1:20,000 scale has been conducted using the same integrated remote sensing and field data methodology [114]. In the semi-arid northeastern Kohat plateau region of Pakatan, ref. [115] used PCA, band ratios, and false color composites as image processing and enhancement techniques to perform lithological differentiation. The lithology classification results in this study proved to be good with the exception of the Landi Kotal slates and Lowara Mena complex. The reason for this could be that the rock in this formation was not as exposed to the elements as other formations. Additionally, the mineralogical and chemical composition of the rock at the sub-pixel level, soil cover, vegetation coverage, atmospheric factors, and the image's spatial and spectral resolution could all have an impact. SVM beats MLC in terms of accuracy for all forms, which suggests that it provides a more complete picture of the area. Also, ASTER shows more improved results than OLI, which could be due to ASTER data's high resolution and strong computational efficiency. It is also necessary to look at how potential mineral mixing can affect the precision of lithologic classification and mapping using multispectral data. With this kind of study, it is possible to expand the data's ability to discern equivalent lithologies outside the region of interest with high accuracy and minimum financial and computational costs.  
As a result of the region's substantial mineral potential, such automated mapping techniques would also help in the development of future exploration plans and technologies. Lineament mapping serves as a crucial roadmap for the exploration of mineral deposits and geological mapping [116; 117]. Mineral exploration can benefit from the use of lineament markers like faults and fractures. Refinements in computer-aided spatial feature analysis should result in less human intervention in the process of geological mapping. Future research could use a sensor (improved hyperspectral) with high ground resolution (at least five meters) to map the areas more precisely. Drones can also have these sensors mounted in addition to satellites. Furthermore, the application of AI-based spatial estimation models [118] and surface maps to subsurface data (geophysical or geochemical) may be used to generate 3D geological models of potential mineralized zones. The strategies will allow the mining and mineral exploration industries to reach Industry 4.0 [119] by using blockchain [120] and IoT [121; 122; 123] technologies for safe data exchange.  
The depth is one of the primary limitations of the remote sensing data as the various bands of electromagnetic radiation do not penetrate the surface of Earth. The remote sensing data are dependent only on the surficial features that reflect the electromagnetic energy of the sunlight. According to [124], the depth penetration of geological remote sensing is limited to a few centimeters in the TIR and only reaches the highest micrometers in the NIR (5-10 \(\upmu\)m).