Xiao Huang – författare

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.Provides comprehensive of the integration of AI, Big Data, and Volunteered Geographic Information (VGI) in Geoinformatics, providing a solid foundation of knowledge and practical insightsIncorportates practical examples and detailed case studies throughout the chapters, allowing readers to see successful implementations, understand the challenges faced, and identify opportunities for their own projectsIncludes considerations for ethics and responsible implementation of AI, Big Data, and VGI, fostering a deeper understanding of the implications of these technologies and tools needed to ensure responsible practices

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. - Provides comprehensive of the integration of AI, Big Data, and Volunteered Geographic Information (VGI) in Geoinformatics, providing a solid foundation of knowledge and practical insights- Incorportates practical examples and detailed case studies throughout the chapters, allowing readers to see successful implementations, understand the challenges faced, and identify opportunities for their own projects- Includes considerations for ethics and responsible implementation of AI, Big Data, and VGI, fostering a deeper understanding of the implications of these technologies and tools needed to ensure responsible practices

Data-Driven Earth Observation for Disaster Management: From Theory to Practical Applications delves into the critical role of Earth observation data and technologies in predicting, managing, and mitigating a spectrum of disasters. With a multidisciplinary approach encompassing geography and geospatial science, the book addresses the challenges of comprehending and managing disasters in our rapidly changing world. Offering solutions through insights into early detection, prediction, management, and prevention, it provides strategies for understanding Earth surface changes and the application of Earth Observation technologies. Readers will gain understanding on how to use Earth observation data and techniques in studying different domains of disaster management.Chapters follow a consistent format, featuring introductory summaries, key takeaways, and a focus on the role of Earth observation, enhancing both comprehensibility and searchability. The book also includes case studies and practical applications of Earth observation technologies, providing context and demonstrating the tangible impact of the techniques covered.Provides a systematic and holistic understanding of how to use Earth observation data and techniques in disaster managementIncludes empirical research examples and case studies to better emphasize theoretical concepts via real-world examplesUtilizes sharable data and workflows in analytical contexts so that techniques and examples presented can be practically replicated

Data-Driven Earth Observation for Disaster Management: From Theory to Practical Applications delves into the critical role of Earth observation data and technologies in predicting, managing, and mitigating a spectrum of disasters. With a multidisciplinary approach encompassing geography and geospatial science, the book addresses the challenges of comprehending and managing disasters in our rapidly changing world. Offering solutions through insights into early detection, prediction, management, and prevention, it provides strategies for understanding Earth surface changes and the application of Earth Observation technologies. Readers will gain understanding on how to use Earth observation data and techniques in studying different domains of disaster management.Chapters follow a consistent format, featuring introductory summaries, key takeaways, and a focus on the role of Earth observation, enhancing both comprehensibility and searchability. The book also includes case studies and practical applications of Earth observation technologies, providing context and demonstrating the tangible impact of the techniques covered. - Provides a systematic and holistic understanding of how to use Earth observation data and techniques in disaster management- Includes empirical research examples and case studies to better emphasize theoretical concepts via real-world examples- Utilizes sharable data and workflows in analytical contexts so that techniques and examples presented can be practically replicated

Fundamentals of Next-Generation Remote Sensing: Data-Driven Earth Observation, Artificial Intelligence, and Emerging Applications addresses the urgent need for a comprehensive, practice-oriented resource in the rapidly evolving field of Earth observation. As sensor technologies advance and data volumes grow exponentially, researchers and professionals require a cohesive guide that bridges traditional image processing with cutting-edge AI, cloud computing, and participatory paradigms. The book covers sourcing multi-sensor imagery-from satellites, drones, and in-situ networks-automating data harmonization, training state-of-the-art AI models, and deploying interactive dashboards for decision-makers. Its chapters span foundational data ecosystems, sensor technologies, pre-processing workflows, AI architectures, and high-impact applications such as disaster response, climate analysis, urban planning, and biodiversity monitoring. This volume offers a unique synthesis of technical workflows, open-source tools, and ethical considerations, making it invaluable for remote sensing scientists, geospatial data engineers, urban planners, and environmental policymakers. It empowers users to develop scalable, reproducible, and policy-relevant solutions, ensuring that remote sensing data transforms into actionable insights for societal benefit.Whether for research, operational deployment, or education, this book provides the essential pipelines, case studies, and best practices needed to keep pace with the advancing Earth observation landscape.Surveys cutting-edge sensors, AI breakthroughs, and geospatial workflowsExplains multi-paradigm AI techniques, from foundation models to low-code toolsEmbeds ethics, uncertainty, and participatory workflows throughout the contentProvides open-source code snippets, workflow diagrams, and case studiesEquips users with scalable, reproducible, and policy-relevant remote sensing methods

This comprehensive handbook covers human mobility within urban contexts, integrating academic theories with pragmatic insights and offering a detailed analysis of the diverse facets of human mobility and its substantial impact on the urban landscape, economy, and societal structures. It explains key fundamental concepts, methods, and models, presenting an in-depth exploration of predictive analytics, clustering patterns, advanced trajectory embedding techniques, artificial intelligence, machine learning, geographic information systems (GIS), Internet of Things (IoT), and smart city innovations. The authors include many case studies and examples of urban mobility in practice, making the content relatable and practical for educators, students, researchers, and practitioners.FeaturesProvides a multidisciplinary and holistic understanding of urban mobility with systematic introductions and discussions of theory, methods, technologies, tools, and applicationsCovers a wide range of real-world case studies of urban mobility in practice globally that include data, programming code, and toolsDiscusses cutting-edge technologies involved in mobility data analyticsAddresses practical challenges in data collection and the ethical implications of mobility research, which are crucial for professionals in the fieldOffers future directions of human mobility research under the big data and artificial intelligence (AI) revolutionUrban Human Mobility: Practices, Analytics, and Strategies for Smart Cities is for professionals, academics, and upper-level undergraduate and graduate students in the fields of urban planning/design, GIScience, data mining, and social sciences.

This comprehensive handbook covers human mobility within urban contexts, integrating academic theories with pragmatic insights and offering a detailed analysis of the diverse facets of human mobility and its substantial impact on the urban landscape, economy, and societal structures. It explains key fundamental concepts, methods, and models, presenting an in-depth exploration of predictive analytics, clustering patterns, advanced trajectory embedding techniques, artificial intelligence, machine learning, geographic information systems (GIS), Internet of Things (IoT), and smart city innovations. The authors include many case studies and examples of urban mobility in practice, making the content relatable and practical for educators, students, researchers, and practitioners.

Features

Provides a multidisciplinary and holistic understanding of urban mobility with systematic introductions and discussions of theory, methods, technologies, tools, and applications. Covers a wide range of real-world case studies of urban mobility in practice globally that include data, programming code, and tools. Discusses cutting-edge technologies involved in mobility data analytics. Addresses practical challenges in data collection and the ethical implications of mobility research, which are crucial for professionals in the field. Offers future directions of human mobility research under the big data and artificial intelligence (AI) revolution.

Urban Human Mobility: Practices, Analytics, and Strategies for Smart Cities is for professionals, academics, and upper-level undergraduate and graduate students in the fields of urban planning/design, GIScience, data mining, and social sciences.

This comprehensive handbook covers human mobility within urban contexts, integrating academic theories with pragmatic insights and offering a detailed analysis of the diverse facets of human mobility and its substantial impact on the urban landscape, economy, and societal structures. It explains key fundamental concepts, methods, and models, presenting an in-depth exploration of predictive analytics, clustering patterns, advanced trajectory embedding techniques, artificial intelligence, machine learning, geographic information systems (GIS), Internet of Things (IoT), and smart city innovations. The authors include many case studies and examples of urban mobility in practice, making the content relatable and practical for educators, students, researchers, and practitioners.

Features

Provides a multidisciplinary and holistic understanding of urban mobility with systematic introductions and discussions of theory, methods, technologies, tools, and applications. Covers a wide range of real-world case studies of urban mobility in practice globally that include data, programming code, and tools. Discusses cutting-edge technologies involved in mobility data analytics. Addresses practical challenges in data collection and the ethical implications of mobility research, which are crucial for professionals in the field. Offers future directions of human mobility research under the big data and artificial intelligence (AI) revolution.

Urban Human Mobility: Practices, Analytics, and Strategies for Smart Cities is for professionals, academics, and upper-level undergraduate and graduate students in the fields of urban planning/design, GIScience, data mining, and social sciences.

Wireless networking enables two or more computers to communicate using standard network protocols without network cables. Since their emergence in the 1970s, wireless networks have become increasingly pop­ ular in the computing industry. In the past decade, wireless networks have enabled true mobility. There are currently two versions of mobile wireless networks. An infrastructure network contains a wired backbone with the last hop being wireless. The cellular phone system is an exam­ ple of an infrastructure network. A multihop ad hoc wireless network has no infrastructure and is thus entirely wireless. A wireless sensor network is an example of a multihop ad hoc wireless network. Ad hoc wireless networking is a technique to support robust and ef­ ficient operation in mobile wireless networks by incorporating routing functionality into mobile hosts. This technique will be used to realize the dream of "anywhere and anytime computing", which is termed mo­ bile computing. Mobile computing is a new paradigm of computing in which users carrying portable devices have access to shared infrastruc­ ture in any location at any time. Mobile computing is a very challenging topic for scientists in computer science and electrical engineering. The representative system for ad hoc wireless networking is called MANET, an acronym for "Mobile Ad hoc NETworks". MANET is an autonomous system consisting of mobile hosts connected by wireless links which can be quickly deployed.

Wireless networking enables two or more computers to communicate using standard network protocols without network cables. Since their emergence in the 1970s, wireless networks have become increasingly pop­ ular in the computing industry. In the past decade, wireless networks have enabled true mobility. There are currently two versions of mobile wireless networks. An infrastructure network contains a wired backbone with the last hop being wireless. The cellular phone system is an exam­ ple of an infrastructure network. A multihop ad hoc wireless network has no infrastructure and is thus entirely wireless. A wireless sensor network is an example of a multihop ad hoc wireless network. Ad hoc wireless networking is a technique to support robust and ef­ ficient operation in mobile wireless networks by incorporating routing functionality into mobile hosts. This technique will be used to realize the dream of "anywhere and anytime computing", which is termed mo­ bile computing. Mobile computing is a new paradigm of computing in which users carrying portable devices have access to shared infrastruc­ ture in any location at any time. Mobile computing is a very challenging topic for scientists in computer science and electrical engineering. The representative system for ad hoc wireless networking is called MANET, an acronym for "Mobile Ad hoc NETworks". MANET is an autonomous system consisting of mobile hosts connected by wireless links which can be quickly deployed.

Wireless networking enables two or more computers to communicate using standard network protocols without network cables. Since their emergence in the 1970s, wireless networks have become increasingly pop­ ular in the computing industry. In the past decade, wireless networks have enabled true mobility. There are currently two versions of mobile wireless networks. An infrastructure network contains a wired backbone with the last hop being wireless. The cellular phone system is an exam­ ple of an infrastructure network. A multihop ad hoc wireless network has no infrastructure and is thus entirely wireless. A wireless sensor network is an example of a multihop ad hoc wireless network. Ad hoc wireless networking is a technique to support robust and ef­ ficient operation in mobile wireless networks by incorporating routing functionality into mobile hosts. This technique will be used to realize the dream of "anywhere and anytime computing", which is termed mo­ bile computing. Mobile computing is a new paradigm of computing in which users carrying portable devices have access to shared infrastruc­ ture in any location at any time. Mobile computing is a very challenging topic for scientists in computer science and electrical engineering. The representative system for ad hoc wireless networking is called MANET, an acronym for "Mobile Ad hoc NETworks". MANET is an autonomous system consisting of mobile hosts connected by wireless links which can be quickly deployed.

This volume outlines a comprehensive journey into how geospatial artificial intelligence (GeoAI) is reshaping our understanding of people and places. Merging traditional geographic inquiry with AI technologies, it offers a holistic view of digital tools and advanced algorithms that redefine human geography. Across twenty‐eight chapters, the book chronicles the evolution of geographic thought into the GeoAI era. Innovative methodologies—from explainable spatial analysis and natural language processing to human-centered computer vision and high-performance computing—reveal new patterns and relationships beyond conventional approaches. Each contribution highlights both technical strides in data processing and enriched perspectives on cultural, economic, political, health, and urban studies. Showcasing diverse applications in disaster management, climate change adaptation, and urban planning, the volume demonstrates GeoAI’s transformative potential. It also engages with ethical, sustainable, and social challenges, emphasizing that technological innovation must serve real-world impacts and inclusivity. Ideal for researchers, students, and practitioners alike, this volume invites you to explore new frontiers at the intersection of technology and human experience.

This volume outlines a comprehensive journey into how geospatial artificial intelligence (GeoAI) is reshaping our understanding of people and places. Merging traditional geographic inquiry with AI technologies, it offers a holistic view of digital tools and advanced algorithms that redefine human geography. Across twenty-eight chapters, the book chronicles the evolution of geographic thought into the GeoAI era. Innovative methodologies—from explainable spatial analysis and natural language processing to human-centered computer vision and high-performance computing—reveal new patterns and relationships beyond conventional approaches. Each contribution highlights both technical strides in data processing and enriched perspectives on cultural, economic, political, health, and urban studies. Showcasing diverse applications in disaster management, climate change adaptation, and urban planning, the volume demonstrates GeoAI’s transformative potential. It also engages with ethical, sustainable, and social challenges, emphasizing that technological innovation must serve real-world impacts and inclusivity. Ideal for researchers, students, and practitioners alike, this volume invites you to explore new frontiers at the intersection of technology and human experience.

This volume outlines a comprehensive journey into how geospatial artificial intelligence (GeoAI) is reshaping our understanding of people and places. Merging traditional geographic inquiry with AI technologies, it offers a holistic view of digital tools and advanced algorithms that redefine human geography. Across twenty‐eight chapters, the book chronicles the evolution of geographic thought into the GeoAI era. Innovative methodologies—from explainable spatial analysis and natural language processing to human-centered computer vision and high-performance computing—reveal new patterns and relationships beyond conventional approaches. Each contribution highlights both technical strides in data processing and enriched perspectives on cultural, economic, political, health, and urban studies. Showcasing diverse applications in disaster management, climate change adaptation, and urban planning, the volume demonstrates GeoAI’s transformative potential. It also engages with ethical, sustainable, and social challenges, emphasizing that technological innovation must serve real-world impacts and inclusivity. Ideal for researchers, students, and practitioners alike, this volume invites you to explore new frontiers at the intersection of technology and human experience.

The LNAI 16603 constitutes the proceedings of the following workshops that were held in conjunction with the 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2026, held in Hong Kong, China, during June 9–12, 2026:-Workshop on AI and Data Science for Digital Finance (AI4DF 2026): This workshop received 3 submissions and accepted 2 papers. 2 camera-ready papers were finally received for publication.- Workshop of Decision Making and Optimization in Financial Technologies (DMO-FinTech 2026): This workshop received 10 submissions and accepted 5 papers. 5 camera-ready papers were finally received for publication.- Workshop on Research and Applications of Foundation Models for Data Mining and Affective Computing (RAFDA 2026): This workshop received 21 submissions and accepted 13 papers. 13 camera-ready papers were finally received for publication.-Workshop on Autonomous Machine Intelligence (AMI-PAKDD 2026): This workshop received 23 submissions and accepted 9 papers. 9 camera-ready papers were finally received for publication.- Workshop on Resource-Efficient Foundation Models for Large-Scale Data Mining (EFMxDM 2026): This workshop received 8 submissions and accepted 7 papers. 7 camera-ready paper was finally received for publication.-Workshop on Generative AI and Computational Journalism (JEN-AI 2026): This workshop received 8 submissions and accepted 5 papers. 3 cameraready papers were finally received for publication.Furthermore, we include 8 papers from the Doctoral Consortium in the Appendix of this proceeding.