Deep Learning and Multi-modal Data Processing for Geological Environment Remote Sensing Interpretation: Methods, Techniques and Applications
This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Special Issue Information
Dear Colleagues,
The geological environment encompasses the shallow lithosphere and the Earth’s surface, which contains rocks, minerals, glaciers, structures, and other elements, providing essential land, water, and mineral resources for societal and industrial development. In recent years, the rapid growth of multi-source remote sensing imagery, ground monitoring, and geological survey data has provided multi-level and multi-perspective information on the geological environment. Deep learning techniques have showcased remarkable capabilities across various domains, including remote sensing, computer vision, and data processing. Integrating deep learning with multi-modal remote sensing data enhances our ability to understand and interpret elements of the geological environment for high-precision resource exploration, environmental monitoring, and natural disaster prediction, among other applications.
However, in real-world scenarios, the geological environment elements are numerous and fragmented, with homogenization of features, blurred boundaries, and susceptibility to the limitations of remote sensing imaging quality and complex backgrounds, posing considerable challenges to interpreting the category of the geological environment elements efficiently and accurately. Understanding the synergies between deep learning and multi-modal data processing is essential for unlocking new possibilities in geological environment data analysis and applications. Therefore, this Special Issue is dedicated to exploring innovative deep learning methods and their applications within geological environment remote sensing data processing.
Dr. Wei Han
Dr. Jining Yan
Dr. Xiaohui Huang
Prof. Dr. Yi Wang
Guest Editors
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Keywords
- novel data sources and deep learning methods for the geological environment, marine, and urban element interpretation
- multi-source and multi-modal remote sensing data fusion
- enhancing and denoising geological images using deep learning techniques
- deep learning applications in monitoring geological disasters, surveys, mineral resources, and other elements
- deep learning for land cover change analysis
- cutting-edge techniques for efficient deep learning-based geological processing in distributed environment
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Published Papers
Remote Sens. - ISSN 2072-4292