The Geoinformatics Frontier: AI, Big Data, and Crowdsourced Technologies tackles the critical challenge of integrating Geoinformatics, AI, Big Data, and VGI; offering a comprehensive introduction to these pivotal concepts, the book elucidates their foundations and relevance to Geoinformatics. It approaches builds on the theory discussed with practical guidance, examples, and detailed case studies; equipping readers with the knowledge needed to effectively implement them. The book presents case studies spanning various sectors, showcasing how the technologies can be successfully employed to address intricate spatial issues and facilitate well-informed decision-making for the complexities of managing large-scale spatial datasets. It also provides indispensable insights into data collection, storage, quality control, and fusion techniques, offering practical solutions to the challenges of data storage, processing, and analysis. The Geoinformatics Frontier serves as an indispensable guide, bridging the gap in understanding and practice for geospatial scientists, empowering readers to harness the transformative potential of Geoinformatics and advanced computer technologies.
1. Introduction to the Geoinformatics Frontier - p. xvii 2. Section I Foundations of geoinformatics - p. 1 3. Chapter 1 The new era in geoinformatics - p. 3 4. Chapter 2 A geospatial moisture change detection after Ianos medicane using Sentinel-2 imagery in central Thessaly, Greece - p. 7 5. Chapter 3 Combining cartography and mythology: An educational approach via Web-GIS - p. 21 6. Chapter 4 Travel cartography in the age of geoinformatics: The Kazantzakis example - p. 33 7. Chapter 5 Archaeological surface survey and spatial analysis: Unlocking efficiency and accuracy using geographic information system technology - p. 43 8. Section II Artificial intelligence in spatial practice - p. 63 9. Chapter 6 Revealing the contribution of conditioning factors to landslide activity in terms of machine learning and fuzzy logic-based susceptibility assessment - p. 65 10. Chapter 7 UAVS in urban air pollution monitoring: State-of-the-art and future pathways - p. 81 11. Chapter 8 Study and implementation of visual SLAM algorithms in photogrammetry and computer vision - p. 95 12. Chapter 9 Heuristic and optimal viewshed algorithms for forest monitoring and observation post allocation - p. 107 13. Chapter 10 Radar remote sensing: Fundamentals, data, and AI-powered processing techniques - p. 119 14. Chapter 11 Study and evaluation of modern SLAM algorithms using LiDAR sensor data - p. 137 15. Chapter 12 A deep learning framework for building outline and footprint extraction in historical cartographic data - p. 155 16. Chapter 13 Digital heritage in action: Geoinformatics and photogrammetry for managing archaeological landscapes - p. 175 17. Chapter 14 GeoAI techniques in flood detection: A comprehensive review - p. 185 18. Chapter 15 GIS-based modeling techniques and Geospatial-Artificial Intelligence (Geo-AI) model in assessing the spatial-temporal variation of air pollution - p. 195 19. Chapter 16 Advancing unmanned aerial vehicles technology for a sustainable environment: The contribution of the ACCELERATE research project - p. 213 20. Chapter 17 Improving thunderstorm prediction with neural networks using numerical weather and satellite data: A novel data fusion and validation approach - p. 231 21. Chapter 18 Land use/cover mapping at high spatial resolution using unmanned aerial vehicle data and machine learning - p. 251 22. Chapter 19 The geometric shape of a spatial network - p. 263 23. Chapter 20 Delineation of site-specific soil management zones using multivariate analysis and geospatial techniques - p. 281 24. Chapter 21 Advancing earth observation applications: Synthetic aperture radar and artificial intelligence in the era of new space - p. 297 25. Chapter 22 LULC mapping from hyperspectral data using machine learning: State-of-the-art, challenges, and future outlook - p. 315 26. Chapter 23 Designing and creating a noise prediction model in urban and semi-urban areas using machine learning techniques - p. 337 27. Chapter 24 Use of unmanned aerial vehicles for retrieving key state variables of Earth's surface energy budget - p. 359 28. Section III Big earth data in geoinformatics - p. 381 29. Chapter 25 Assessment of vegetation moisture stress using EO-based spectral indices within a cloud computing framework - p. 383 30. Chapter 26 Spatiotemporal multidimensional data cubes for EO big data: Analysis and visualization - p. 403 31. Section IV Crowdsourced technologies in the new geoinformatics era - p. 421 32. Chapter 27 Volunteered geographic information and crowdsourcing in geographic practice: A unifying conceptual framework - p. 423 33. Chapter 28 Crowdsourced spatial data in human observation: Opportunities, challenges, and future directions - p. 435 34. Chapter 29 Conclusions—Geoinformatics in the 21st century - p. 445 35. Index - p. 447
The Geoinformatics Frontier: AI, Big Data, and Crowdsourced Technologies tackles the critical challenge of integrating Geoinformatics, AI, Big Data, and VGI; offering a comprehensive introduction to these pivotal concepts, the book elucidates their foundations and relevance to Geoinformatics. It approaches builds on the theory discussed with practical guidance, examples, and detailed case studies; equipping readers with the knowledge needed to effectively implement them. The book presents case studies spanning various sectors, showcasing how the technologies can be successfully employed to address intricate spatial issues and facilitate well-informed decision-making for the complexities of managing large-scale spatial datasets. It also provides indispensable insights into data collection, storage, quality control, and fusion techniques, offering practical solutions to the challenges of data storage, processing, and analysis. The Geoinformatics Frontier serves as an indispensable guide, bridging the gap in understanding and practice for geospatial scientists, empowering readers to harness the transformative potential of Geoinformatics and advanced computer technologies.
1. Introduction to the Geoinformatics Frontier - p. xvii 2. Section I Foundations of geoinformatics - p. 1 3. Chapter 1 The new era in geoinformatics - p. 3 4. Chapter 2 A geospatial moisture change detection after Ianos medicane using Sentinel-2 imagery in central Thessaly, Greece - p. 7 5. Chapter 3 Combining cartography and mythology: An educational approach via Web-GIS - p. 21 6. Chapter 4 Travel cartography in the age of geoinformatics: The Kazantzakis example - p. 33 7. Chapter 5 Archaeological surface survey and spatial analysis: Unlocking efficiency and accuracy using geographic information system technology - p. 43 8. Section II Artificial intelligence in spatial practice - p. 63 9. Chapter 6 Revealing the contribution of conditioning factors to landslide activity in terms of machine learning and fuzzy logic-based susceptibility assessment - p. 65 10. Chapter 7 UAVS in urban air pollution monitoring: State-of-the-art and future pathways - p. 81 11. Chapter 8 Study and implementation of visual SLAM algorithms in photogrammetry and computer vision - p. 95 12. Chapter 9 Heuristic and optimal viewshed algorithms for forest monitoring and observation post allocation - p. 107 13. Chapter 10 Radar remote sensing: Fundamentals, data, and AI-powered processing techniques - p. 119 14. Chapter 11 Study and evaluation of modern SLAM algorithms using LiDAR sensor data - p. 137 15. Chapter 12 A deep learning framework for building outline and footprint extraction in historical cartographic data - p. 155 16. Chapter 13 Digital heritage in action: Geoinformatics and photogrammetry for managing archaeological landscapes - p. 175 17. Chapter 14 GeoAI techniques in flood detection: A comprehensive review - p. 185 18. Chapter 15 GIS-based modeling techniques and Geospatial-Artificial Intelligence (Geo-AI) model in assessing the spatial-temporal variation of air pollution - p. 195 19. Chapter 16 Advancing unmanned aerial vehicles technology for a sustainable environment: The contribution of the ACCELERATE research project - p. 213 20. Chapter 17 Improving thunderstorm prediction with neural networks using numerical weather and satellite data: A novel data fusion and validation approach - p. 231 21. Chapter 18 Land use/cover mapping at high spatial resolution using unmanned aerial vehicle data and machine learning - p. 251 22. Chapter 19 The geometric shape of a spatial network - p. 263 23. Chapter 20 Delineation of site-specific soil management zones using multivariate analysis and geospatial techniques - p. 281 24. Chapter 21 Advancing earth observation applications: Synthetic aperture radar and artificial intelligence in the era of new space - p. 297 25. Chapter 22 LULC mapping from hyperspectral data using machine learning: State-of-the-art, challenges, and future outlook - p. 315 26. Chapter 23 Designing and creating a noise prediction model in urban and semi-urban areas using machine learning techniques - p. 337 27. Chapter 24 Use of unmanned aerial vehicles for retrieving key state variables of Earth's surface energy budget - p. 359 28. Section III Big earth data in geoinformatics - p. 381 29. Chapter 25 Assessment of vegetation moisture stress using EO-based spectral indices within a cloud computing framework - p. 383 30. Chapter 26 Spatiotemporal multidimensional data cubes for EO big data: Analysis and visualization - p. 403 31. Section IV Crowdsourced technologies in the new geoinformatics era - p. 421 32. Chapter 27 Volunteered geographic information and crowdsourcing in geographic practice: A unifying conceptual framework - p. 423 33. Chapter 28 Crowdsourced spatial data in human observation: Opportunities, challenges, and future directions - p. 435 34. Chapter 29 Conclusions—Geoinformatics in the 21st century - p. 445 35. Index - p. 447
