In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies.

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies.

Geospatial Analysis Applied to Mineral Exploration: Remote Sensing, GIS, Geochemical, and Geophysical Applications to Mineral Resources presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling for Earth scientists, researchers, mineral exploration communities and mining companies. This book will help readers solve high complexity issues in remote sensing data processing, geochemical data analysis, geophysical data analysis, and appropriate applications of GIS techniques for data fusion designed for mineral exploration purposes. It contains updated knowledge of remote sensing imagery, geochemistry, geophysics and geospatial techniques that can assist in delineating the signatures and patterns linked to deep-seated, covered, blind or buried mineral deposits. - Covers advances in remote sensing data processing algorithms and geochemical data analysis - Includes sections on geophysical data analysis and machine learning algorithms for mineral exploration - Introduces the suite of geo-spatial tools currently available for mineral exploration - Presents case studies to provide real-world examples of the theories covered

This book examines the theory and methods of remote sensing intelligent interpretation based on deep learning. Based on geological and environmental effects on mines, this book constructs a set of systematic mine remote sensing datasets focusing on the multi-level task with the system of “target detection→scene classification→semantic segmentation." Taking China’s Hubei Province as an example, this book focuses on the following four aspects: 1. Development of a multiscale remote sensing dataset of the mining area, including mine target remote sensing dataset, mine (including non-mine areas) remote sensing scene dataset, and semantic segmentation remote sensing dataset of mining land cover. The three datasets are the basis of intelligent interpretation based on deep learning. 2. Research on mine target remote sensing detection method based on deep learning. 3. Research on remote sensing scene classification method of mine and non-mine areas based on deep learning. 4. Research on the fine-scale classification method of mining land cover based on semantic segmentation. The book is a valuable reference both for scholars, practitioners and as well as graduate students who are interested in mining environment research.

Remote sensing and Geographic Information Systems (GIS) have become indispensable tools for understanding our planet's complex systems and addressing a wide range of environmental, social, and economic challenges. With several environmental and social factors coming to a head, it is important that our society utilizes every tool at its disposal in order to identify these issues and make comprehensive action plans in order to avoid negative social and environmental consequences. Applying Remote Sensing and GIS for Spatial Analysis and Decision-Making offers a comprehensive exploration of the applications of remote sensing, Geographic Information Systems (GIS), and emerging technologies in spatial analysis and decision-making across various domains. The book explores fundamental principles, methodologies, and advanced techniques pertinent to remote sensing and GIS, while also discussing the integration of emerging technologies such as unmanned aerial vehicles (UAVs), hyperspectral imaging, LiDAR, machine learning, and artificial intelligence (AI). Covering topics such as climate change modeling, land resources, and spatial data analysis, this book is an excellent resource for researchers and academicians, urban planners, practitioners, professionals, policy makers, postgraduate students and educators, and more.

Volume 1 comprehensive This book explains the topics related to the introduction to Mining Engineering in detail. It has been prepared especially for the benefit of students and academicians studying at the Faculty of Mining. The topics have been prepared in order and by taking into consideration the important issues.

As we navigate the challenges posed by fluctuating market demands, environmental regulations, and community expectations, effective site monitoring emerges as an indispensable aspect of sustainable mining practices. The harmonization of geotechnical, hydrological, air quality, and noise monitoring provides a comprehensive approach to identifying potential hazards, thereby facilitating timely interventions and optimizing resource management.

The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive.

Advancements in object-based image analysis, commercial high-resolution satellite sensors, and unmanned aerial vehicles, there is growing interest in studying terrestrial, pollution, catastrophe, and ocean dynamics using a range of high-resolution remote sensing data (UAV). High and extremely high-resolution optical and microwave images enable the extraction of more information from a variety of resource management domains, including agriculture, forestry, geological resources, water resources, cryosphere, atmosphere, and analytics for climate change. Researchers have started utilizing advanced techniques for fine-level information extraction, new sensors and platforms, improved quantification, and characterization of physical environments, patterns, and processes. High-quality articles addressing the use and application of remote sensing in environmental studies will be published in this Research Topic.