This book comprehensively utilizes the new generation of artificial intelligence and remote sensing science and technology to systematically carry out researches on high-precision recognition, monitoring, analysis, and assessment of geological disasters by using different technologies of "ground, airspace, and space-based systems" and different scales of "target-semantic-region". The main contents include:

1) Intelligent interpretation theory and methods of geological disasters,

2) Intelligent analysis of landslide based on long-term ground monitoring data,

3) Intelligent analysis of landslide evolution based on optical satellite remote sensing data,

5) Intelligent assessment methods of landslide susceptibility,

6) Intelligent recognition of ground figure based on airspace-based remote sensing data.

The book is of interest to graduate student, scientific, and technological personnel who work in the area of geological disasters, natural hazards, remote sensing, and artificial intelligence.

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.

This book comprehensively utilizes the new generation of artificial intelligence and remote sensing science and technology to systematically carry out researches on high-precision recognition, monitoring, analysis, and assessment of geological disasters by using different technologies of "ground, airspace, and space-based systems" and different scales of "target-semantic-region". The main contents include:

1) Intelligent interpretation theory and methods of geological disasters,

2) Intelligent analysis of landslide based on long-term ground monitoring data,

3) Intelligent analysis of landslide evolution based on optical satellite remote sensing data,

5) Intelligent assessment methods of landslide susceptibility,

6) Intelligent recognition of ground figure based on airspace-based remote sensing data.

The book is of interest to graduate student, scientific, and technological personnel who work in the area of geological disasters, natural hazards, remote sensing, and artificial intelligence.

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.