Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers and more.Advances in Machine Learning and Image Analysis for GeoAI provides graduate students, researchers and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research. - Covers the latest machine learning and signal processing techniques that can effectively leverage multimodal geospatial imagery at scale - Chapters cover a variety of algorithmic frameworks pertaining to GeoAI, including superresolution, self-supervised learning, data fusion, explainable AI, among others - Presents cutting-edge deep learning architectures optimized for a wide array of geospatial imagery

This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.

The two-volume set LNCS 14365 and 14366 constitutes the papers of workshops hosted by the 22nd International Conference on Image Analysis and Processing, ICIAP 2023, held in Udine, Italy, in September 2023. In total, 72 workshop papers and 10 industrial poster session papers have been accepted for publication. Part II of the set, volume 14366, contains 41 papers from the following workshops:– Medical Imaging Hub:• Artificial Intelligence and Radiomics in Computer-Aided Diagnosis (AIR-CAD)• Multi-Modal Medical Imaging Processing (M3IP)• Federated Learning in Medical Imaging and Vision (FedMed)– Digital Humanities Hub:• Artificial Intelligence for Digital Humanities (AI4DH)• Fine Art Pattern Extraction and Recognition (FAPER)• Pattern Recognition for Cultural Heritage (PatReCH)• Visual Processing of Digital Manuscripts: Workflows, Pipelines, BestPractices (ViDiScript)

Forest Resources Resilience and Conflicts presents modern remote sensing and GIS techniques for Sustainable Livelihood. It provides an up-to-date critical analysis of the discourse surrounding forest resources and society, illustrating the relationship between forest resources and the livelihood of local people. The book is organized into four parts consisting of 31 chapters. Each chapter then reviews current understanding, present research, and future implications. Utilizing case studies and novel advances in geospatial technologies, Forest Resources Resilience and Conflicts provides a timely synthesis of a rapidly growing field and stimulates ideas for future work, especially considering sustainable development goals.In addition, the book presents the effective contribution of the forestry sector to populations' livelihoods through improved collection of forestry statistics that foster the understanding and integration of the forestry sector in poverty reduction processes and the national economy to enhance its integration in national planning. It is a valuable resource for researchers and students in environmental science, especially those interested in forestry, geography, and remote sensing. - Demonstrates tools and techniques for measurement, monitoring, mapping, and modeling of forest resources - Explores state-of-the-art techniques using open source software, statistical programming, and GIS, focusing on recent trends in data mining and machine learning - Addresses a wide range of issues with both environmental and societal implications - Provides a global review of the multiple roles of forest resources utilizing case studies to illustrate management strategies and techniques

Providing an authoritative assessment of the current landscape of spatial analysis in the social sciences, this cutting-edge Handbook covers the full range of standard and emerging methods across the social science domain areas in which these methods are typically applied. Accessible and comprehensive, it expertly answers the key questions regarding the dynamic intersection of spatial analysis and the social sciences.

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.

Big geospatial datasets created by large infrastructure projects require massive computing resources to process. Feature extraction is a process used to reduce the initial set of raw data for manageable image processing, and machine learning (ML) is the science that supports it. This book focuses on feature extraction methods for optical geospatial data using ML. It is a practical guide for professionals and graduate students who are starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies on how to collect height values for spatial features, how to develop 3D models in a map context, and others. Features Provides the basics of feature extraction methods and applications along with the fundamentals of machine learning Discusses in detail the application of machine learning techniques in geospatial building feature extraction Explains the methods for estimating object height from optical satellite remote sensing images using Python Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessment Highlights the potential of machine learning and geospatial technology for future project developments This book will be of interest to professionals, researchers, and graduate students in geoscience and earth observation, machine learning and data science, civil engineers, and urban planners.

This book aims to provide an international forum for scholarly researchers, practitioners and academic communities to explore the role of information and communication technologies and its applications in technical and scholarly development. The conference attracted a total of 464 submissions, of which 152 submissions (including 4 poster papers) have been selected after a double-blind review process. Academic pioneering researchers, scientists, industrial engineers and students will find this series useful to gain insight into the current research and next-generation information science and communication technologies. This book discusses the aspects of communication, data science, ambient intelligence, networking, computing, security and Internet of things, from classical to intelligent scope. The authors hope that readers find the volume interesting and valuable; it gathers chapters addressing state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research.

In geospatial analysis, navigating the complexities of data interpretation and analysis presents a formidable challenge. Traditional methods often need to efficiently handle vast volumes of geospatial data while providing insightful and actionable results. Scholars and practitioners grapple with manual or rule-based approaches, hindering progress in understanding and addressing pressing issues such as climate change, urbanization, and resource management. Ethics, Machine Learning, and Python in Geospatial Analysis offers a solution to the challenges faced by leveraging the extensive library support and user-friendly interface of Python and machine learning. The book’s meticulously crafted chapters guide readers through the intricacies of Python programming and its application in geospatial analysis, from fundamental concepts to advanced techniques.

This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces. Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless devices could play a vital role in assisting various security agencies. Security and safety are the major concerns for public and private places in every country. Some places need indoor surveillance, some need outdoor surveillance, and, in some places, both are needed. The goal of this book is to provide an efficient and optimal surveillance system using AI, ML, and DL-based image processing. The blend of machine vision technology and AI provides a more efficient surveillance system compared to traditional systems. Leading scholars and industry practitioners are expected to make significant contributions to the chapters. Their deep conversations and knowledge, which are based on references and research, will result in a wonderful book and a valuable source of information.