Advances in Scalable and Intelligent Geospatial Analytics : Challenges and Applications (Paperback)

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.