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Intelligent High-Resolution Geological Mapping Based on SLIC-CNN

School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resource, Nanjing 210019, China
College of Earth Sciences, Jilin University, Changchun 130061, China
College of Applied Technology, Jilin University, Changchun 130061, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 99;
Received: 4 December 2019 / Revised: 23 January 2020 / Accepted: 27 January 2020 / Published: 5 February 2020
High-resolution geological mapping is an important supporting condition for mineral and energy exploration. However, high-resolution geological mapping work still faces many problems. At present, high-resolution 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 high-resolution mapping using Unmanned Aerial Vehicle (UAV) and deep learning algorithms. This method uses the UAV to collect high-resolution remote sensing images, cooperates with some groundwork to anchor the lithology, and then completes most of the mapping work on high-resolution 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 Clustering-Convolutional Neural Network (SLIC-CNN) 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 high-resolution geological map was obtained. View Full-Text
Keywords: geological survey; geological mapping; deep learning; SLIC-CNN; UAV; ductile shear zone geological survey; geological mapping; deep learning; SLIC-CNN; UAV; ductile shear zone
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MDPI and ACS Style

Sang, X.; Xue, L.; Ran, X.; Li, X.; Liu, J.; Liu, Z. Intelligent High-Resolution Geological Mapping Based on SLIC-CNN. ISPRS Int. J. Geo-Inf. 2020, 9, 99.

AMA Style

Sang X, Xue L, Ran X, Li X, Liu J, Liu Z. Intelligent High-Resolution Geological Mapping Based on SLIC-CNN. ISPRS International Journal of Geo-Information. 2020; 9(2):99.

Chicago/Turabian Style

Sang, Xuejia, Linfu Xue, Xiangjin Ran, Xiaoshun Li, Jiwen Liu, and Zeyu Liu. 2020. "Intelligent High-Resolution Geological Mapping Based on SLIC-CNN" ISPRS International Journal of Geo-Information 9, no. 2: 99.

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