Intelligent HighResolution Geological Mapping Based on SLICCNN Highresolution geological mapping is an important supporting condition for mineral and energy exploration. However, highresolution geological mapping work still faces many problems. At present, highresolution geological mapping is still generated by expert interpretation of survey lines, compasses, and field data. The work in the field is constrained by the weather, terrain, and personnel, and the working methods need to be improved. This paper proposes a new method for highresolution mapping using Unmanned Aerial Vehicle (UAV) and deep learning algorithms. This method uses the UAV to collect highresolution remote sensing images, cooperates with some groundwork to anchor the lithology, and then completes most of the mapping work on highresolution remote sensing images. This method transfers a large amount of field work into the room and provides an automatic mapping process based on the Simple Linear Iterative ClusteringConvolutional Neural Network (SLICCNN) algorithm. It uses the convolutional neural network (CNN) to identify the image content and confirms the lithologic distribution, the simple linear iterative cluster (SLIC) algorithm can be used to outline the boundary of the rock mass and determine the contact interface of the rock mass, and the mode and expert decision method is used to clarify the results of the fusion and mapping. The mapping method was applied to the Taili waterfront in Xingcheng City, Liaoning Province, China. In this study, the Area Under the Curve (AUC) of the mapping method was 0.937. The Kappa test result was k  0.8523, and a highresolution geological map was obtained.

Model prediction of depthspecific soil texture distributions with artificial neural network: A case study in Yunfu, a typical area of Udults Zone, South China Abstract   The depthspecific soil texture map with highresolution (i.e. xe2x89xa410xc2xa0m) is essential for soil management and forest silviculture. The objective of this research was to develop a modelling method to generate highresolution soil texture maps at five depths (D1: 0xe2x80x9320, D2: 20xe2x80x9340, D3: 40xe2x80x9360, D4: 60xe2x80x9380, and D5: 80xe2x80x93100xc2xa0cm) in Yunfu, a typical area of Udults Zone, South China. Taking a coarseresolution soil texture (CST) map with a 1: 2,800,000 scale and nine topohydrologic variables derived from a digital elevation model (DEM) with 10xc2xa0mresolution as input candidates, a series of artificial neural network (ANN) models for five depths were built and evaluated by a 10fold crossvalidation with 385 soil profiles from the Yunfu forest. The results indicated that the optimal model for five depths engaged five, five, five, four, and four DEMgenerated variables as inputs, respectively, and model accuracies for estimating sand and clay contents varied with root mean squared error (RMSE) of 6.8xe2x80x939.7%, R2 of 0.56xe2x80x930.72, and relative overall accuracy (ROA)xc2xa0xc2xb1xc2xa05% of 54xe2x80x9381%, which were better than most of other researches. An extra independent validation with 64 soil profiles outside of the modelbuilding area also indicated that the optimal models had adequate capabilities for generalization with RMSE of 9.2xe2x80x9312.2%, R2 of 0.33xe2x80x930.47, and ROAxc2xa0xc2xb1xc2xa05% of 37xe2x80x9353%. The depthspecific sand and clay content maps with 10xc2xa0mresolution generated from the optimal models in Yunfu showed more detailed information than the CST map, and could reflected the influence of the DEMderived topohydrologic variables. Based on the generated maps, horizontal characteristics of soil texture in the study area exhibited an obvious process of clay translocation from the topsoil (D1) to subsoil (D25), a maximum accumulation of clay in D4, and a dominant sandy soil in the topsoil (D1). Thus, the modelling method, i.e. developing ANNs with kfold crossvalidation, can be used to generate depthspecific soil texture maps in Udults Zone, South China. In addition, the generated highresolution maps can clearly show the changes of soil texture in threedimension.