The development of analytical and computational techniques and growing scientific funds
collectively contribute to the rapid accumulation of geoscience data. The massive amount of …

Remote sensing (RS) technology has significantly contributed to geological exploration and
mineral resource assessment. However, its effective application in vegetated areas …

In this study, we proposed an automated lithological mapping approach by using spectral
enhancement techniques and Machine Learning Algorithms (MLAs) using Airborne Visible …

Remote sensing data proved to be a valuable resource in a variety of earth science
applications. Using high-dimensional data with advanced methods such as machine …

Lithological unit classification (LUC) refers to the classification of different types of rocks
within an area, and it has been widely used in many fields, such as resource surveys and …

The identification of lithology from well log data is an important task in petroleum exploration
and development. However, due to the complexity of the sedimentary environment and …

Abstract Machine Learning Algorithms (MLAs) have recently introduced considerable
lithologic mapping. Thus, this study scrutinizes the efficacy of Artificial Neural Network …

Global warming is one of the main challenges of recent times. The glaciers are melting faster
than expected which has resulted in global mean sea level rise and increased the risk of …

In recent decades, lithological mapping techniques using hyperspectral remotely sensed
imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) …

Different types of remote sensing data are commonly used as inputs for lithological
classification schemes, yet determining the best data source for each specific application is …