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.

This book presents the theories and methods for geology intelligent interpretation based on deep learning and remote sensing technologies. The main research subjects of this book include lithology and mineral abundance. This book focuses on the following five aspects: 1. Construction of geology remote sensing datasets from multi-level (pixel-level, scene-level, semantic segmentation-level, prior knowledge-assisted, transfer learning dataset), which are the basis of geology interpretation based on deep learning. 2. Research on lithology scene classification based on deep learning, prior knowledge, and remote sensing. 3. Research on lithology semantic segmentation based on deep learning and remote sensing. 4. Research on lithology classification based on transfer learning and remote sensing. 5. Research on inversion of mineral abundance based on the sparse unmixing theory and hyperspectral remote sensing. The book is intended for undergraduate and graduate students who are interested in geology, remote sensing, and artificial intelligence. It is also used as a reference book for scientific and technological personnel of geological exploration.

‘Coal’ and ‘China’ to some extent have become synonymous. China is by far the largest user of coal in the world. In 2016, coal production in China amounted to 3.21 billion tons, about half of the total global coal production. Coal consumption accounts for more than 65% of primary energy consumption in China. The Chinese coal industry greatly contributes to the economic development in China, the second largest economy in the world. However, periodically, ubiquitous images of smog blanketing major Chinese cities are viewed all over the world. Coal combustion is one of the important contributors to smog, which is considered to be a major environmental and human health problem for China and other countries. News stories also highlight the periodic coal mine disasters that kill hundreds of Chinese coal miners annually. The need to address these and other human health, environmental, and mine safety issues and to maximize resource recovery and use justifies a vigorous coal research effort. This book brings together experts on almost every aspect of coal geology, coal production, composition and use of the coal and its by-products, and coal’s environmental and human health impacts. The chapters in this book were originally published in a special issue of the International Geology Review.

This two-volume set (CCIS 848 and CCIS 849) constitutes the thoroughly refereed proceedings of the 5th International Conference Geo-Spatial Knowledge and Intelligence, GSKI 2017, held in Chiang Mai, Thailand, in December 2018.The 142 full papers presented were carefully reviewed and selected from 579 submissions. They are organized in topical sections on smart city in resource management and sustainable ecosystem; spatial data acquisition through RS and GIS in resource management and sustainable ecosystem; ecological and environmental data processing and management; advanced geospatial model and analysis for understanding ecological and environmental process; applications of geo-informatics in resource management and sustainable ecosystem.