Advances in Scalable and Intelligent Geospatial Analytics. Challenges and Applications

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Section I Introduction to Geospatial Analytics
	Chapter 1 Geospatial Technology – Developments, Present Scenario and Research Challenges
		1.1 Introduction
			1.1.1 Concept of Spatial Data
			1.1.2 Spatial Data Sources
			1.1.3 Geographic Coordinate System
			1.1.4 Map Projections
			1.1.5 Spatial Data Modelling
			1.1.6 Spatial Database Creation
			1.1.7 Spatial Relations
			1.1.8 Spatial Data Analysis
			1.1.9 Spatial Data Interpolation
			1.1.10 Digital Terrain Modelling
			1.1.11 Network Analysis
			1.1.12 Statistical Analysis
			1.1.13 Visualisation of Spatial Data Analysis
			1.1.14 Spatial Decision Support Systems
			1.1.15 Spatial Data Accuracy
		1.2 Applications
		1.3 Research Challenges
		1.4 Open Areas of Research
		1.5 Conclusion
		References
Section II Geo-Ai
	Chapter 2 Perspectives on Geospatial Artificial Intelligence Platforms for Multimodal Spatiotemporal Datasets
		2.1 Introduction
		2.2 Challenges and Opportunities of Different Geospatial Data Modalities
		2.3 Motivation for a Data-Centric, Multimodal Geospatial Artificial Intelligence Platform
			2.3.1 Current Challenges in ML-Based Geospatial Analysis
			2.3.2 An Example of a Geospatial AI Platform: Trinity
			2.3.3 Key Advantages and Observed Benefits of Trinity
		2.4 Representation, Alignment, and Fusion of Multimodal Geospatial Datasets
			2.4.1 Preliminary: Spherical Mercator Projection and Zoom-q Tiles
			2.4.2 Spatial Transformations of Mobility Data
			2.4.3 Spatial Transformations of Road Network Geometry
			2.4.4 Vector Geometry Data
			2.4.5 Temporal Transformations of Mobility Data
			2.4.6 Synthetic Generation of Geospatial Data Representations
			2.4.7 Self-Supervised Representation Learning from Geospatial Data
			2.4.8 Geospatial Imagery
			2.4.9 Auxiliary Datasets and Data Provenance
		2.5 Design Overview of a Geospatial AI Platform
			2.5.1 Machine Learning Operations: MLOps
			2.5.2 Components of a Geospatial AI Platform
		2.6 ML Feature Management and Feature Platform
			2.6.1 Why Do We Need a Feature Platform?
			2.6.2 Components of a ML Feature Platform
			2.6.3 Design Considerations for a ML Feature Platform
		2.7 Label Management and Label Platform
			2.7.1 Components of a Label Platform
				2.7.1.1 Label Generation and Editing
				2.7.1.2 Label Visualization, Analysis, and Validation
				2.7.1.3 Label Metadata and Catalog
				2.7.1.4 Stratification
				2.7.1.5 Active Learning
			2.7.2 Design Considerations for a Label Platform
		2.8 Machine Learning Infrastructure Components
			2.8.1 Data Processing Framework
			2.8.2 Storage, Compute, and Metadata Handling
		2.9 Machine Learning Modeling Kernel
			2.9.1 Serving and Deployment of Trained Models
		2.10 Trinity Experiment Lifecycle
			2.10.1 Project and Experiment Setup via the User Interface
			2.10.2 Data Preparation and Training
			2.10.3 Scalable Distributed Inference
			2.10.4 Visualization and Evaluation of Predictions
			2.10.5 Product Types and Sample Applications
		2.11 Conclusions
		Note
		References
	Chapter 3 Temporal Dynamics of Place and Mobility
		3.1 Introduction
			3.1.1 Social Norms and Historical Contexts
			3.1.2 Environmental Influences and Mobility Disruptions
			3.1.3 Built Environment and Points of Interest
		3.2 Data Types for Temporal Research
			3.2.1 Probe Data
			3.2.2 Stationary Sensor Data
			3.2.3 Place-Based Data
			3.2.4 Human-Centric Data
		3.3 What’s Going On
		3.4 Discussion, Conclusion, and Opportunities
			3.4.1 Common Grounds
			3.4.2 Data Privacy
			3.4.3 Data Ownership
			3.4.4 Data Quality and Transparency
		References
	Chapter 4 Geospatial Knowledge Graph Construction Workflow for Semantics-Enabled Remote Sensing Scene Understanding
		4.1 Introduction and Motivation
			4.1.1 Image Information Mining for Earth Observation (EO)
			4.1.2 Semantic Web
			4.1.3 Ontologies and Reasoning
			4.1.4 Geospatial Knowledge Representation
				4.1.4.1 Ontology-Based Remote Sensing Image Analysis
				4.1.4.2 Ontology-Based Approaches for Disaster Applications
				4.1.4.3 Knowledge Graphs
		4.2 Geospatial Knowledge Graphs Construction
			4.2.1 Knowledge Graph Construction Workflow
				4.2.1.1 Deep Learning-Based Multi-Class Segmentation
				4.2.1.2 Geometry Shape Extraction
				4.2.1.3 Resource Description Framework (RDF) Based Serialization
				4.2.1.4 Semantic Enrichment of Geospatial KG
		4.3 Applications and Use Cases
		4.4 Summary
		Notes
		References
	Chapter 5 Geosemantic Standards-Driven Intelligent Information Retrieval Framework for 3D LiDAR Point Clouds
		5.1 Introduction and Motivation
		5.2 LiDAR—Light Detection and Ranging
			5.2.1 Types of LiDAR Data Sources
			5.2.2 List of Remote Sensing-Based Open LiDAR Datasets
		5.3 Interoperability and Geosemantics Standardization for LiDAR
			5.3.1 Need for Interoperability in LiDAR
			5.3.2 Geospatial Standardization
				5.3.2.1 International Bodies for Geospatial Standardization
				5.3.2.2 Geospatial Standards for 3D LiDAR Data
			5.3.3 Designing a Geosemantic Standards-Driven Framework for 3D LiDAR Data
				5.3.3.1 LiDAR Markup Language (LiDARML)—Toward Interoperability for LiDAR
		5.4 Development of a Scalable LiDAR Information Mining Framework: A Systems Perspective
			5.4.1 Geo-Artificial Intelligence (GeoAI) Module for 3D LiDAR Point Cloud Processing
			5.4.2 GeoSemantics Module: Toward Semantically Enriched LiDAR Knowledge Graph
		5.5 Case Study: Knowledge Base Question-Answering (KBQA) Framework for LiDAR
			5.5.1 Dataset Details
			5.5.2 Problem Formulation: Knowledge Base Question-Answering (KBQA) for LiDAR
			5.5.3 Generating LiDAR Scene Knowledge Graph—LiSKG
			5.5.4 Natural Language to GeoSPARQL in Knowledge Base Question-Answering (KBQA)
		5.6 Summary and Future Trends
		5.7 Where to Look for Further Information
		Acknowledgments
		Notes
		References
	Chapter 6 Geospatial Analytics Using Natural Language Processing
		6.1 Introduction
			6.1.1 Geospatial Analytics
				6.1.1.3 Sources of Geotext
			6.1.2 Introduction to Natural Language Processing
			6.1.3 Geospatial Data Meets NLP
				6.1.3.1 Researchers Interest in Geospatial Data from Text Using NLP
		6.2 Overview of NLP Techniques in Geospatial Analytics
			6.2.1 Event Extraction
			6.2.2 Parts-of-Speech (POS) Tagging
			6.2.3 Temporal Information Extraction
			6.2.4 Spatial-Temporal Relationship Extractions
			6.2.5 Named Entity Recognition (NER)
		6.3 Applications of NLP in Geospatial Analytics
			6.3.1 Geoparsing and Toponym Disambiguation
				6.3.1.1 Geoparsing
			6.3.2 Geospatial Geosemantic in Natural Language
				6.3.2.1 Role of NLP in Geosemantic
				6.3.2.2 Geosemantic Similarity Using NLP
			6.3.3 Geospatial Information Analysis
				6.3.3.1 Geospatial Information Extraction (GIE)
				6.3.3.2 Geospatial Information Retrieval
				6.3.3.3 Geospatial Question Answering
			6.3.4 Spatiotemporal/Geospatial Text Analysis from Social Media
		6.4 Future Scope of NLP in Geospatial Analytics
		6.5 Summary and Conclusions
		Notes
		References
Section III Scalable Geospatial Analytics
	Chapter 7 A Scalable Automated Satellite Data Downloading and Processing Pipeline Developed on AWS Cloud for Agricultural Applications
		7.1 Introduction
		7.2 Satellite Imagery Resolutions
		7.3 Application of Technology in Monitoring Crop Health
		7.4 High-Level Solution—Crop Health Monitoring Using Satellite Data
		7.5 AWS Components Used in the Solution
		7.6 Some of the Key Advantages of Having AWS Based Data Pipeline Were
		7.7 Detailed Solution
			7.7.1 Key Steps of the ADDPro Pipeline
		7.8 Sample Analysis for Field-Level Crop Health
		7.9 Time Series of Satellite Data and Crop Condition Information
		7.10 Conclusion
		Acknowledgment
		References
	Chapter 8 Providing Geospatial Intelligence through a Scalable Imagery Pipeline
		8.1 Geospatial Intelligence R&D Challenges
			8.1.1 Challenges to Advancing Geospatial Intelligence
				8.1.1.1 Compute Power and Startup Costs
				8.1.1.2 Scalability
				8.1.1.3 Speed and Resolution
				8.1.1.4 Data Privacy and Security
			8.1.2 ORNL High-Performance Computing Resources
		8.2 Pushing the Boundaries of Geospatial Intelligence
			8.2.1 Enabling Research
			8.2.2 Mapping
			8.2.3 Large-Scale Modeling
		8.3 Building the Imagery Pipeline
			8.3.1 Imagery Ingest
			8.3.2 Orthorectification
			8.3.3 Pan-Sharpening
			8.3.4 Cloud Detection
			8.3.5 Postprocessing and Output
		8.4 Future Considerations
			8.4.1 Adding Atmospheric Compensation to the Pipeline
			8.4.2 Leveraging Cloud Computing to Advance Our Imagery Processing Capabilities
			8.4.3 Adapting Pipe to Other Applications
		Acknowledgments
		Notes
		References
	Chapter 9 Distributed Deep Learning and Its Application in Geo-spatial Analytics
		9.1 Introduction
		9.2 High-performance Computing (HPC)
			9.2.1 Need for High-performance Computing
			9.2.2 Parallel Computing
			9.2.3 Distributed Computing
				9.2.3.1 Distributed Computing for Geo-Spatial Data
			9.2.4 Challenges in High-performance Computing
		9.3 Distributed Deep Learning for Geo-Spatial Analytics
			9.3.1 Distributed Deep Learning
		9.4 Apache Spark for Distributed Deep Learning
			9.4.1 Distributed Hyper-Parameter Optimization
			9.4.2 Deep Learning Pipelines
			9.4.3 Apache Spark on Deep Learning Pipeline
		9.5 Applications of Distributed Deep Learning in Real World
		9.6 Conclusion Summary and Perspectives
		References
	Chapter 10 High-Performance Computing for Processing Big Geospatial Disaster Data
		10.1 Introduction
		10.2 Recent Advances in High-Performance Computing for Geospatial Analysis
		10.3 Damage Assessment and Sources of Disaster Data
			10.3.1 Images
				10.3.1.1 Airborne
				10.3.1.2 Satellite
			10.3.2 LiDAR
		10.4 Key Components of High-Performance Computing
			10.4.1 Domain Decomposition
			10.4.2 Spatial Indexing
			10.4.3 Task Scheduling
			10.4.4 Evaluation Metrics
		10.5 Hardware and Its Programming Model
			10.5.1 Graphics Processing Unit
			10.5.2 General Architecture of GPU
			10.5.3 Jetson Nano—Embedded HPC
		10.6 HPC for Building Damage Detection for Earthquake-Affected Area
			10.6.1 Point Cloud Outlier Removal
			10.6.2 Buildings Extraction
			10.6.3 Iterative Closest Point
			10.6.4 Classification Results
		10.7 Summary and Future Work
		References
Section IV Geovisualization: Innovative Approaches for Geovisualization and Geovisual Analytics for Big Geospatial Data
	Chapter 11 Dashboard for Earth Observation
		11.1 Introduction
		11.2 Canonical Use Cases and High-Level Requirements (Science)
			11.2.1 COVID-19 Dashboard
			11.2.2 MAAP Dashboard
			11.2.3 Community Workshops, Tutorials, and Hackathons
		11.3 Technology Landscape Analysis
			11.3.1 Data Stores
			11.3.2 Data Processing
			11.3.3 Data Services
				11.3.3.1 Discovery Services
				11.3.3.2 Data Access Services
				11.3.3.3 Mapping Services
			11.3.4 Visualization Front End
				11.3.4.1 Visualization Libraries
				11.3.4.2 Technical Considerations
				11.3.4.3 Dynamic Tilers
				11.3.4.4 User Interactivity and Engaging End User Experience
				11.3.4.5 Map Projections
				11.3.4.6 Analysis Clients
		11.4 VEDA
			11.4.1 Overview
			11.4.2 Implementation
				11.4.2.1 Federated Data Stores
				11.4.2.2 Data Processing (Extract, Transform, and Load)
				11.4.2.3 Data Services
				11.4.2.4 APIs
				11.4.2.5 Data Visualization, Exploration, and Analysis Clients
		11.5 Summary
		References
	Chapter 12 Visual Exploration of LiDAR Point Clouds
		12.1 Introduction
		12.2 Visualization Systems for Airborne LiDAR Point Clouds
		12.3 Distributed System Architecture for Visual Analytics Tool
			12.3.1 Browser-based Visualization Tool
				12.3.1.1 Canvas Rendering Using BufferGeometry
				12.3.1.2 Asynchronous and Parallel Processing
				12.3.1.3 Service Interface
			12.3.2 Distributed System for Semantic Classification
			12.3.3 Backend Services
		12.4 System Implementation
			12.4.1 Visualization Tasks
			12.4.2 Graphical User Interface Design
				12.4.2.1 Navigation Controls on the Canvas
				12.4.2.2 Classification Tool
				12.4.2.3 Analytics Widgets
				12.4.2.4 Selection and Exploration of Regions of Interest
			12.4.3 Subsampling for Efficient Rendering
			12.4.4 Custom Partitioning in Spark
			12.4.5 Distributed Data Model in Cassandra
			12.4.6 System Specifications
		12.5 Case Study: Visual Analytics of Airborne LiDAR Point Clouds
		12.6 Conclusions
		Acknowledgment
		Note
		References
Section V other Advances in Geospatial Domain
	Chapter 13 Toward a Smart Metaverse City: Immersive Realism and 3D Visualization of Digital Twin Cities
		13.1 Introduction
		13.2 Metaverse for Digital Twin Cities
			13.2.1 Digital Twin Cities
			13.2.2 Metaverse
			13.2.3 Smart Metaverse City
				13.2.3.1 Immersive Realism
				13.2.3.2 Scientific Virtual Object Creation
				13.2.3.3 Public Engagement through Avatars
			13.2.4 Potential Applications
		13.3 Geospatial Framework for Metaverse Cities
			13.3.1 Overall Framework Design
			13.3.2 Geospatial Data Acquisition
			13.3.3 Digital Twin City Construction
			13.3.4 Immersive 3D Geovisualization
		13.4 Use Cases
			13.4.1 Public Engagement, Education, and Training
			13.4.2 Training Computer Vision Applications
			13.4.3 Spatial Co-simulation
		13.5 Future Opportunities
		Disclaimer
		Acknowledgement
		References
	Chapter 14 Current UAS Capabilities for Geospatial Spectral Solutions
		14.1 History
		14.2 Current State of the Art
			14.2.1 Types of Platforms
				14.2.1.1 Multirotor
				14.2.1.2 Fixed-Wing
				14.2.1.3 Hybrid Airframes
			14.2.2 Propulsion Systems
			14.2.3 Sensor Payloads
				14.2.3.1 RGB
				14.2.3.2 Multispectral
				14.2.3.3 Hyperspectral
				14.2.3.4 Thermal/LWIR
				14.2.3.5 LiDAR
				14.2.3.6 SAR
				14.2.3.7 Sensor Precautions
			14.2.4 Use Cases/Literature Review
				14.2.4.1 Statistics
				14.2.4.2 Agricultural
				14.2.4.3 Forestry
				14.2.4.4 Riparian Zones
				14.2.4.5 Wetlands and Coastal Systems
				14.2.4.6 Land Surface Temperature
				14.2.4.7 Animals
				14.2.4.8 Archeology
				14.2.4.9 Atmospheric Dynamics
				14.2.4.10 Optimization
				14.2.4.11 Automation
		14.3 Communications
			14.3.1 Ground Control
			14.3.2 Networked UAS
		14.4 Processing Techniques
			14.4.1 Onboard Processing
			14.4.2 Postprocessing
		14.5 Current Issues
		14.6 Future Directions
			14.6.1 Sensors
			14.6.2 Processing
			14.6.3 Communications
		14.7 Conclusion
		References
	Chapter 15 Flood Mapping and Damage Assessment Using Sentinel – 1 & 2 in Google Earth Engine of Port Berge & Mampikony Districts, Sophia Region, Madagascar
		15.1 Introduction
			15.1.1 Background
		15.2 Study Area
		15.3 Data Used and Methodology
			15.3.1 Data Used
			15.3.2 Methodology
		15.4 Results and Discussions
			15.4.1 Flood Inundation Map
			15.4.2 Land Use/Land Cover Map
			15.4.3 Flood Damage Assessment
		15.5 Conclusions
		Acknowledgments
		References
Section VI case Studies from the Geospatial Domain
	Chapter 16 Fuzzy-Based Meta-Heuristic and Bi-Variate Geo-Statistical Modelling for Spatial Prediction of Landslides
		16.1 Introduction
		16.2 LSZ Mapping and Associated Modeling Approaches
			16.2.1 Fuzzy Set Theory and FAHP
				16.2.1.1 Extent Analysis on FAHP
				16.2.1.2 Triangular Fuzzy MF
				16.2.1.3 Fuzzy Operational Laws
			16.2.2 Yule Coefficient
		16.3 Application of RS and GIS in LSZ Studies
		16.4 Application of FAHP and YC Models for LSZ Mapping in Parts of Kalimpong Region of Darjeeling Himalaya
			16.4.1 Description of the Area
			16.4.2 ThemSatic Layer Development
				16.4.2.1 Landslide Inventory
				16.4.2.2 Landslide Causative Factors
			16.4.3 Model Implementation
				16.4.3.1 Factors Weights Determination Using FHAP Model
				16.4.3.2 Factors Subclasses Weights Determination Using YC Model
			16.4.4 Landslide Susceptibility Zonation (LSZ) and Validation
		16.5 Discussion and Conclusion
		Acknowledgments
		Funding
			Availability of Data and Material
			Code Availability
		Declarations
			Conflicts of Interest
		References
	Chapter 17 Understanding the Dynamics of the City through Crowdsourced Datasets: A Case Study of Indore City
		17.1 Introduction, Background and Need of Study
		17.2 Literature Review
			17.2.1 Location Intelligence
			17.2.2 Rise of Social Media and Urban Datasets
			17.2.3 Using the Social Media/Urban Datasets for Various Urban Studies
			17.2.4 Different Approaches and Clustering-Based Algorithms
			17.2.5 Incorporation of Text-Based Classification
		17.3 Framework and Methodology
			17.3.1 Identification of Platform for Extraction of Data
			17.3.2 Case Study – Indore City
			17.3.3 Data Collection/Extraction
			17.3.4 Data Analysis
			17.3.5 Limitations of the Study
		17.4 Observation and Inferences
			17.4.1 Activity Mapping
			17.4.2 Landuse Change Detection
			17.4.3 Point of Interest (POI)
			17.4.4 Sentiment Mapping
		17.5 Conclusion and Way Forward
		References
	Chapter 18 A Hybrid Model for the Prediction of Land Use/Land Cover Pattern in Kurunegala City, Sri Lanka
		18.1 Introduction
		18.2 Method and Materials
			18.2.1 The Study Area
			18.2.2 Data Source
			18.2.3 Data Pre-Processing
			18.2.4 Data Analysis
				18.2.4.1 Supervised Classification
				18.2.4.2 Selection of Drivers Variables
				18.2.4.3 Multi-Layer Perceptron Neural Network and CA-Markov Model
		18.3 Results and Discussion
			18.3.1 Land Use/Land Cover Pattern
			18.3.2 Land Use/Land Cover Changes
			18.3.3 Land Use/Land Cover Simulation and Prediction
		18.4 Conclusion
		References
	Chapter 19 Spatio-Temporal Dynamics of Tropical Deciduous Forests under Climate Change Scenarios in India
		19.1 Introduction
		19.2 Materials and Methods
			19.2.1 Study Area
			19.2.2 Data Used
				19.2.2.1 Forest Cover Data
				19.2.2.2 Predictors Used
			19.2.3 Data Processing
			19.2.4 Random Forest Model Building
			19.2.5 Model Evaluation and Spatial Prediction
		19.3 Results
			19.3.1 Pearson Correlation
			19.3.2 Accuracy and Predictors Importance
			19.3.3 Spatial Prediction of Tropical Deciduous Forest Cover
		19.4 Discussion
		19.5 Conclusion
		Acknowledgment
		References
	Chapter 20 A Survey of Machine Learning Techniques in Forestry Applications Using SAR Data
		20.1 Introduction
		20.2 SAR and Machine Learning
		20.3 Forest Classification
		20.4 Forest Degradation/Deforestation Mapping
		20.5 Forest Tree Height Estimation
		20.6 Forest Biomass Estimation
		20.7 Future Perspective and Conclusion
		References
Index