"Geospatial data acquisition and analysis techniques have grown tremendously, providing an opportunity to solve environmental and natural resource related problems. Despite the challenges encountered in processing highly voluminous geospatial data in a scalable and efficient manner, advances in high-performance computing, computer vision, and big data analytics enable the efficient processing of big-geospatial data. As a comprehensive overview of the state-of-the-art, and future developments in this domain, readers, including scholars, academicians, industry experts, and government agencies, will gain insights into the emerging trends on scalable geospatial data analytics"--
Geospatial data acquisition and analysis techniques have experienced tremendous growth in the last few years, providing an opportunity to solve previously unsolved environmental- and natural resource-related problems. However, a variety of challenges are encountered in processing the highly voluminous geospatial data in a scalable and efficient manner. Technological advancements in high-performance computing, computer vision, and big data analytics are enabling the processing of big geospatial data in an efficient and timely manner. Many geospatial communities have already adopted these techniques in multidisciplinary geospatial applications around the world. This book is a single source that offers a comprehensive overview of the state of the art and future developments in this domain.
FEATURES
- Demonstrates the recent advances in geospatial analytics tools, technologies, and algorithms
- Provides insight and direction to the geospatial community regarding the future trends in scalable and intelligent geospatial analytics
- Exhibits recent geospatial applications and demonstrates innovative ways to use big geospatial data to address various domain-specific, real-world problems
- Recognizes the analytical and computational challenges posed and opportunities provided by the increased volume, velocity, and veracity of geospatial data
This book is beneficial to graduate and postgraduate students, academicians, research scholars, working professionals, industry experts, and government research agencies working in the geospatial domain, where GIS and remote sensing are used for a variety of purposes. Readers will gain insights into the emerging trends on scalable geospatial data analytics.
Advances in high-performance computing, computer vision, and big data analytics enable the efficient processing of big-geospatial data. Geospatial communities have adopted these techniques in a variety of applications. This book is a comprehensive overview of the state-of-the-art, and future developments in this domain.
Section I: Introduction to Geospatial Analytics.
1. Geospatial
Technology Developments, Present Scenario and Research Challenges. Section
II: Geo-Ai.
2. Perspectives on Geospatial Artificial Intelligence Platforms
for Multimodal Spatiotemporal Datasets.
3. Temporal Dynamics of Place and
Mobility.
4. Geospatial Knowledge Graph Construction Workflow for
Semantics-Enabled Remote Sensing Scene Understanding.
5. Geosemantic
Standards-Driven Intelligent Information Retrieval Framework for 3D LiDAR
Point Clouds.
6. Geospatial Analytics Using Natural Language Processing.
Section III: Scalable Geospatial Analytics.
7. A Scalable Automated Satellite
Data Downloading and Processing Pipeline Developed on AWS Cloud for
Agricultural Applications.
8. Providing Geospatial Intelligence through a
Scalable Imagery Pipeline.
9. Distributed Deep Learning and Its Application
in Geo-spatial Analytics.
10. High-Performance Computing for Processing Big
Geospatial Disaster Data. Section IV: Geovisualization: Innovative Approaches
for Geovisualization and Geovisual Analytics for Big Geospatial Data.
11.
Dashboard for Earth Observation.
12. Visual Exploration of LiDAR Point
Clouds. Section V: Other Advances in Geospatial Domain.
13. Toward a Smart
Metaverse City: Immersive Realism and 3D Visualization of Digital Twin
Cities.
14. Current UAS Capabilities for Geospatial Spectral Solutions.
15.
Flood Mapping and Damage Assessment Using Sentinel 1 & 2 in Google Earth
Engine of Port Berge & Mampikony Districts, Sophia Region, Madagascar.
Section VI: Case Studies from the Geospatial Domain.
16. Fuzzy-Based
Meta-Heuristic and Bi-Variate Geo-Statistical Modelling for Spatial
Prediction of Landslides.
17. Understanding the Dynamics of the City through
Crowdsourced Datasets: A Case Study of Indore City.
18. A Hybrid Model for
the Prediction of Land Use/Land Cover Pattern in Kurunegala City, Sri Lanka.
19. Spatio-Temporal Dynamics of Tropical Deciduous Forests under Climate
Change Scenarios in India.
20. A Survey of Machine Learning Techniques in
Forestry Applications Using SAR Data.
Dr. Surya Durbha is a Professor at CSRE, Indian Institute of Technology Bombay (IITB). Before joining IITB, he held an adjunct faculty position in the Electrical and Computer Engineering Department at Mississippi State University. He has published over 80 peer reviewed articles and has written a book on the Internet of Things published by Oxford University Press in March 2021.
Dr. Jibonananda Sanyal serves as the Group Leader for Oak Ridge National Laboratorys Computational Urban Sciences research group. He is an IEEE Senior Member, an ACM Distinguished Speaker, and a 2017 Knoxvilles 40 under 40 honoree.
Dr. Lexie Yang is a lead research scientist in the GeoAI Group at Oak Ridge National Laboratory. She leads several AI-enabled geoscience data analytics projects with large-scale multi-modality geospatial data. The recent work from her team has been widely used to support national-scale disaster assessment and management by federal and local agencies.
Dr. Sangita S. Chaudhari is a Professor in the Department of Computer Engineering, Ramrao Adik Institute of Technology Nerul, Navi Mumbai, India. She is vice chair of IEEE GRSS Mumbai Chapter and has published several journal articles and book chapters.
Dr. Ujwala Bhangale is an Associate Professor in the Department of Information Technology, at K.J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai, India. She has published several papers in IEEE/ACM publications.
Dr. Ujwala Bharambe is an Assistant Professor in the Department of Computer Engineering, at Thadomal Shahani Engineering College, Mumbai, India. She has published several papers in IEEE/ACM publications.
Dr. Kuldeep Kurte is a research scientist in the Computational Urban Sciences Group (CUSG) at Oak Ridge National Laboratory. He has experience working on various applications on different HPC platforms from NVIDIA Jetson Tk1 to the Summit supercomputer.
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