2nd Edition
Big Data Techniques and Technologies in Geoinformatics
Over the past decade, since the publication of the first edition, there have been new advances in solving complex geoinformatics problems. Advancements in computing power, computing platforms, mathematical models, statistical models, geospatial algorithms, and the availability of data in various domains, among other things, have aided in the automation of complex real-world tasks and decision-making that inherently rely on geospatial data. Of the many fields benefiting from these latest advancements, machine learning, particularly deep learning, virtual reality, and game engine, have increasingly gained the interest of many researchers and practitioners. This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems.
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.
1. Distributed and Parallel Computing
Monir H. Sharker and Hassan A. Karimi
2. GEOSS Clearinghouse Integrating Geospatial Resources to Support the Global Earth Observation System of Systems
Chaowei Yang, Kai Liu, Zhenlong Li, Wenwen Li, Huayi Wu, Jizhe Xia, Qunying Huang, Jing Li, Min Sun, Lizhi Miao, Nanyin Zhou, and Doug Nebert
3. Using a Cloud Computing Environment to Process Large 3D Spatial Datasets
Ramanathan Sugumaran, Jeffrey Burnett, and Marc P. Armstrong
4. Building Open Environments to Meet Big Data Challenges in Earth Sciences
Meixia Deng and Liping Di
5. Developing Online Visualization and Analysis Services for NASA Satellite-Derived Global Precipitation Products during the Big Geospatial Data Era
Zhong Liu, Dana Ostrenga, William Teng, and Steven Kempler
6. Algorithmic Design Considerations for Geospatial and/or Temporal Big Data
Terence van Zyl
7. Machine Learning on Geospatial Big Data
Terence van Zyl
8. Spatial Big Data: Case Studies on Volume, Velocity, and Variety
Michael R. Evans, Dev Oliver, Xun Zhou, and Shashi Shekhar
9. Exploiting Big VGI to Improve Routing and Navigation Services
Mohamed Bakillah, Johannes Lauer, Steve H.L. Liang, Alexander Zipf, Jamal Jokar Arsanjani, Amin Mobasheri, and Lukas Loos
10. Efficient Frequent Sequence Mining on Taxi Trip Records Using Road Network Shortcuts
Jianting Zhang
11. Geoinformatics and Social Media: New Big Data Challenge
Arie Croitoru, Andrew Crooks, Jacek Radzikowski, Anthony Stefanidis, Ranga R. Vatsavai, and Nicole Wayant
12. Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern
Roland Assam and Thomas Seidl
13. Geospatial Cyberinfrastructure for Addressing the Big Data Challenges on the Worldwide Sensor Web
Steve H.L. Liang and Chih-Yuan Huang
14. OGC Standards and Geospatial Big Data
Carl Reed
15. Advanced Deep Learning Models and Algorithms for Spatial-Temporal Data
Yang Wang and Hassan A. Karimi
16. Deep Learning for Spatial Data: Heterogeneity and Adaptation
Weiye Chen, Yiqun Xie, Xiaowei Jia, and Erhu He
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
Q. Sun, S. Das, and S. Wang
18. Fairness-Aware Deep Learning in Space
Erhu He, Weiye Chen, Yiqun Xie, and Xiaowei Jia
19. Integrating Large Language Models and Qualitative Spatial Reasoning
Mohammad Kazemi Beydokhti, Yaguang Tao, Matt Duckham, and Amy L. Griffin
20. Toward a Spatial Metaverse: Building Immersive Virtual Experiences with Georeferenced Digital Twin and Game Engine
Q. Sun, S. Das, K. Wang, and A. Teofilo
21. A Topological Machine Learning Approach with Multichannel Integration for Detecting Geospatial Objects
M. Syzdykbayev, B. Karimi, and H. A. Karimi
Biography
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.
