Big Data
Beschreibung
FEATURES
- Contains a comprehensive collection of advanced big data approaches, techniques, and technologies for geoinformatics problems
- Provides seven new chapters on deep learning models, algorithms, and structures, including a new chapter on how spatial metaverse is used to build immersive realistic virtual experiences
- Presents information on how deep learning is used for solving real-world geoinformatics problems
This book is intended for researchers, academics, professionals, and students in such fields as computing and information, civil and environmental engineering, environmental sciences, geosciences, geology, geography, and urban studies.
Weitere Details
Weitere Ausgaben
Andere Ausgaben
Vorauflage
Person
Hassan A. Karimi is a Professor and the Director of the Geoinformatics Laboratory in the School of Computing and Information at the University of Pittsburgh. He earned a PhD in geomatics engineering at the University of Calgary. Dr. Karimi's research interests include computational geometry and topology, machine learning, spatial data analytics, navigation techniques and applications, location-based services, mobile computing, and distributed/parallel computing. His research in geoinformatics has resulted in over 230 publications in peer-reviewed journals and conference proceedings, as well as in many workshops and presentations at national and international forums. Dr. Karimi has published the following books with Taylor & Francis: Geospatial Data Science Techniques and Applications (2018), Indoor Wayfinding and Navigation (2015), Big Data: Techniques and Technologies in Geoinformatics (2014), Advanced Location-Based Technologies and Services (2013), CAD and GIS Integration (2010), and Telegeoinformatics: Location-Based Computing and Services (2004). He has published Universal Navigation on Smartphones (2011) with Springer and Handbook of Research on Geoinformatics (2009) with IGI.
Inhalt
1. Distributed and Parallel Computing. 2. GEOSS Clearinghouse Integrating Geospatial Resources to Support the Global Earth Observation System of Systems. 3. Using a Cloud Computing Environment to Process Large 3D Spatial Datasets. 4. Building Open Environments to Meet Big Data Challenges in Earth Sciences. 5. Developing Online Visualization and Analysis Services for NASA Satellite-Derived Global Precipitation Products during the Big Geospatial Data Era. 6. Algorithmic Design Considerations for Geospatial and/or Temporal Big Data. 7. Machine Learning on Geospatial Big Data. 8. Spatial Big Data: Case Studies on Volume, Velocity, and Variety. 9. Exploiting Big VGI to Improve Routing and Navigation Services. 10. Efficient Frequent Sequence Mining on Taxi Trip Records Using Road Network Shortcuts. 11. Geoinformatics and Social Media: New Big Data Challenge. 12. Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern. 13. Geospatial Cyberinfrastructure for Addressing the Big Data Challenges on the Worldwide Sensor Web. 14. OGC Standards and Geospatial Big Data. 15. Advanced Deep Learning Models and Algorithms for Spatial-Temporal Data. 16. Deep Learning for Spatial Data: Heterogeneity and Adaptation. 17. Assessing Multilevel Environmental and Air Quality Changes in Australia Pre- and Post-COVID-19 Lockdown: A Spatial Machine Learning Approach Utilizing Earth Observation Data. 18. Fairness-Aware Deep Learning in Space. 19. Integrating Large Language Models and Qualitative Spatial Reasoning. 20. Toward a Spatial Metaverse: Building Immersive Virtual Experiences with Georeferenced Digital Twin and Game Engine. 21. A Topological Machine Learning Approach with Multichannel Integration for Detecting Geospatial Objects.