Lime and marble deposits mapping and estimation through deep neural layers-random forest merger and remote sensing imagery

Abstract Lime and marble are sedimentary (carbonate) rocks, mainly composed of calcite and dolomite as major ingredients. Calcite (CaCO 3 ) and dolomite (MgCO 3 ) offer excellent reflection in visible and short-wave infrared bands (0.4 to 2.5 μm), which distinguish them from other rock types and makes them a solid application area for multispectral sentinel-2 sensors. In this study, the monumental deposits of carbonated rocks of the intended study region are mapped and estimated with better accuracy by using a novel Deep Neural Layers-Random Forest merger model and Sentinel-2 imagery. We used ArcGIS 10.5 to prepare a high-quality classified map of the study regions, which can be used further to effectively and efficiently reach out and extract those deposits. The proposed model, trained on a locally generated dataset, containing 134,897 pixels of size 10 m, mapped the deposits with 0.95 accuracy (kappa coefficient 0.94), which is 70% of the total mineral deposits of the study regions. The proposed model is the improved version of a prior experimented model consisting of convolution layers and dense layers only, whose accuracy was 0.93. The experimental outcomes of the study stood successful in finding the optimum combination of hyperparameters for the AI models used, to delineate mineralization zones with precision, focusing on carbonate in particular.