Applications of data augmentation in mineral prospectivity prediction based on convolutional neural networks

2022/02/12 by Na Yang, Zhenkai Zhang, Jianhua Yang +1 · 1 citation
Computer Science · Engineering · #Geochemistry and Geologic Mapping #Mineral Processing and Grinding #Remote-Sensing Image Classification

paper · doi:10.1016/j.cageo.2022.105075

openalex publication_date 2022/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Citations

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)
• Recognition of geochemical anomalies using a deep autoencoder network
• Gradient-based learning applied to document recognition
• GIS-based rare events logistic regression for mineral prospectivity mapping
• Deep learning and its application in geochemical mapping

Cited by

• Challenges in data-driven geospatial modeling for environmental research and practice