Advances in Scalable and Intelligent Geospatial Analytics (eBook, PDF)

Challenges and Applications
Redaktion: Durbha, Surya S; Kurte, Kuldeep; Bharambe, Ujwala; Bhangale, Ujwala; S Chaudhari, Sangita; Yang, Lexie; Sanyal, Jibonananda

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 421
• Erscheinungstermin: 12. Mai 2023
• Englisch
• ISBN-13: 9781000877489
• Artikelnr.: 67663495

Autorenporträt

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 Laboratory's Computational Urban Sciences research group. He is an IEEE Senior Member, an ACM Distinguished Speaker, and a 2017 Knoxville's 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.

Inhaltsangabe

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.