GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China

Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and a deep learning convolutional neural network (CNN), were employed to conduct a data-driven W prospectivity modelling of the southern Jiangxi Province, China. A total of 118 known W occurrences derived from long-term exploration of this brownfield area and eight evidential layers of multi-source geoscience information related to W mineralization constituted the input datasets. This provided a data-rich foundation for training machine learning models. The optimal configuration of model parameters was trained by a grid search procedure and validated by 10-fold cross-validation. The resulting predictive models were comprehensively assessed by a confusion matrix, receiver operating characteristic curve, and success-rate curve. The modelling results indicate that the CNN model achieves the best classification performance with an accuracy of 92.38%, followed by the RF model (87.62%). In contrast, the RF model outperforms the rest of ML models in overall predictive performance and predictive efficiency. This is characterized by the highest value of area under the curve and the steepest slope of success-rate curve. The RF model was chosen as the optimal model for mineral prospectivity in this region as it is the best predictor. The prospective zones delineated by the prospectivity map occupy 9% of the study area and capture 66.95% of the known mineral occurrences. The geological interpretation of the model reveals that previously neglected Mn anomalies are significant indicators. This implies that enrichment of ore-forming material in the host rocks may play an important role in the formation process of wolframite and can represent an innovative exploration criterion for further exploration in this area.

Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance.

https://www.mdpi.com/2220-9964/8/6/258/pdf

Learning Cartographic Building Generalization with Deep Convolutional Neural Networks

. Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g. simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the benchmark is the human operator, who is able to design an aesthetic and correct representation of the physical reality. Deep Learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform the traditional computer vision methods. In both domains – computer vision and cartography – humans are able to produce a solution; a prerequisite for this is, that there is the possibility to generate many training examples for the different cases. Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. An advantage of this task is the fact that many training data sets are available from given map series. The approach is a first attempt using an existing network. In the paper, the details of the implementation will be reported, together with an in depth analysis of the results. An outlook on future work will be given.

/pdf/building-generalization-using-deep-learning-1rp0itx8n5.pdf

Building generalization using deep learning

Machine learning for spatial analyses in urban areas: a scoping review

A critical problem in mapping data is the frequent updating of large data sets. To solve this problem, the updating of small-scale data based on large-scale data is very effective. Various map generalization techniques, such as simplification, displacement, typification, elimination, and aggregation, must therefore be applied. In this study, we focused on the elimination and aggregation of the building layer, for which each building in a large scale was classified as “0-eliminated,” “1-retained,” or “2-aggregated.” Machine-learning classification algorithms were then used for classifying the buildings. The data of 1:1000 scale and 1:25,000 scale digital maps obtained from the National Geographic Information Institute were used. We applied to these data various machine-learning classification algorithms, including naive Bayes (NB), decision tree (DT), k-nearest neighbor (k-NN), and support vector machine (SVM). The overall accuracies of each algorithm were satisfactory: DT, 88.96%; k-NN, 88.27%; SVM, 87.57%; and NB, 79.50%. Although elimination is a direct part of the proposed process, generalization operations, such as simplification and aggregation of polygons, must still be performed for buildings classified as retained and aggregated. Thus, these algorithms can be used for building classification and can serve as preparatory steps for building generalization.

Machine Learning Classification of Buildings for Map Generalization

The present invention relates to a method for building wearable device based volunteered disabled facility information for navigation The method comprises the following steps of: (a) displaying a main menu on a mobile terminal screen and a wearable device screen, respectively; (b) enabling a figure input module to calculate and input GPS coordinate information; (c) photographing and recording a corresponding disabled facility by using a camera installed in a wearable device; and (e) associating and storing the figure information, attribute information, and image information in a storage module and updating and storing the information in a DB through a navigation server if the information is terminated in accordance with a voice command of a walk disabled person

Methods for building volunteered disabled facility information for navigation using wearable devices

Semantic search recently been used in the search field. POI is one of the most essential information that make up the geographic information, and many of the geographic information system has POI search function as a basic. In this study, we propose POI semantic search using collective intelligence. For this, we designed and implemented service that constructs empirical information from tag and image, and provides an intuitive spatial navigation experience. For POI search, collective intelligence platform that many users can participate to collect variety information was designed and implemented.

Design and Implementation of Semantic Search for POI Utilizing Collective Intelligence

The result of the line simplification algorithm varies with the choice of algorithms, the change in tolerance and the selection of target objects. Three of the algorithms used in this study are Sleeve-fitting, Visvalingam-Whyatt, and Bend-simplify. They were applied to the three kinds of objects which were buildings, rivers, and roads with the five degrees of the tolerance. Through this experiments the vector displacement, the areal displacement, and the angular displacement were measured and the qualitative analysis was performed with the trend line of the errors. The experimental results show that errors were differ from tolerance values, and characteristics of line simplification algorithms based on changes of tolerance were understood.

Jaeeun Lee

Papers

Machine Learning Classification of Buildings for Map Generalization

Methods for building volunteered disabled facility information for navigation using wearable devices

Design and Implementation of Semantic Search for POI Utilizing Collective Intelligence

Performance Assessments of Three Line Simplification Algorithms with Tolerance Changes