Spatial Data Mining
Theory and Application
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Beschreibung
This book delivers a comprehensive guide to spatial data mining—where Geomatics meets artificial intelligence and computer science. Spatial data is at the heart of today’s big data revolution, offering unprecedented opportunities for addressing environmental and societal challenges. With decades of pioneering research, the authors present cutting-edge methods for unlocking the hidden value within vast datasets.
This book introduces innovative concepts like data fields, the cloud model, and a spatial data mining pyramid that visually illustrates the mining mechanism. Key algorithms, such as hierarchical clustering with data fields and the cloud model, provide practical tools for geospatial data analysis. Through real-world applications, including landslide monitoring, spatio-temporal video mining, and risk map inference, the text demonstrates how spatial data can be harnessed to solve complex problems in remote sensing and GIS data mining.
Designed for researchers and students in spatio-temporal intelligence, data science, artificial intelligence, and computer science, this book blends theory with practical insights. Readers gain a deeper understanding of spatial data mining techniques, while benefiting from the authors’ interdisciplinary approach. With its novel theories and real-world case studies, this resource is essential for anyone looking to advance their knowledge in this rapidly evolving field.
This book delivers a comprehensive guide to spatial data mining—where Geomatics meets artificial intelligence and computer science. Spatial data is at the heart of today’s big data revolution, offering unprecedented opportunities for addressing environmental and societal challenges. With decades of pioneering research, the authors present cutting-edge methods for unlocking the hidden value within vast datasets.
This book introduces innovative concepts like data fields, the cloud model, and a spatial data mining pyramid that visually illustrates the mining mechanism. Key algorithms, such as hierarchical clustering with data fields and the cloud model, provide practical tools for geospatial data analysis. Through real-world applications, including landslide monitoring, spatio-temporal video mining, and risk map inference, the text demonstrates how spatial data can be harnessed to solve complex problems in remote sensing and GIS data mining.
Designed for researchers and students in spatio-temporal intelligence, data science, artificial intelligence, and computer science, this book blends theory with practical insights. Readers gain a deeper understanding of spatial data mining techniques, while benefiting from the authors’ interdisciplinary approach. With its novel theories and real-world case studies, this resource is essential for anyone looking to advance their knowledge in this rapidly evolving field.
Presents advanced spatial data mining techniques to enhance readers‘ geospatial and big data analysis skills Explores innovative cloud models and geo-rough space methods to solve complex spatial data challenges Offers real-world insights into landslide monitoring and remote sensing image analysis
Autor*in
Deren Li
Themen in »Spatial Data Mining«
Spatial data mining GIS data mining Remote sensing image mininig Geospatial Information Mining Data field Cloud model Big data clustering Spatiotemporal video data mining remote sensing/photogrammetry