While megacities are a reality, so too are the environmental disturbances that they cause, including air and water pollution. These disturbances can be modeled with technology and data obtained by modern methods, such as by drone, to monitor cities in near real-time as well as help to simulate risk situations and propose future solutions. These solutions can be inspired by the theoretical principles of sustainable urbanism. Methods and Applications of Geospatial Technology in Sustainable Urbanism is a collection of innovative research that combines theory and practice on analyzing urban environments and applying sustainability principles to them. Highlighting a wide range of topics including geographic information systems, internet mapping technologies, and green urbanism, this book is ideally designed for urban planners, public administration officials, landscape analysts, geographers, engineers, entrepreneurs, academicians, researchers, and students.

This book advances the scientific understanding and application of space-based technologies to address a variety of areas related to sustainable development; including environmental systems analysis, environmental management, clean processes, green chemistry, and green engineering. Geo-spatial techniques have gained considerable interest in recent decades among the earth and environmental science communities for solving and understanding various complex problems and approaches towards sustainable technologies. The book encompasses several scopes of interests on sustainable technologies in areas such as water resources, forestry, remote sensing, meteorology, atmospheric and oceanic modeling, environmental engineering and management, civil engineering, air and environmental pollution, water quality problems, etc. The book will appeal to people with an interest in geo-spatial techniques, sustainable development and other diverse backgrounds within earth and environmental sciences field.

With the emergence of smart technology and automated systems in today’s world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.

Academics and researchers currently grapple with a pressing issue; the demand for precise and insightful geographical information has surged across various fields, encompassing urban planning, environmental monitoring, agriculture, and disaster management. This surge has revealed a substantial knowledge gap, underscoring the need for effective applications that can bridge the gap between cutting-edge technologies and practical usage. Geospatial Application Development Using Python Programming emerges as the definitive solution to this challenge. This comprehensive book equips academics, researchers, and professionals with the essential tools and insights required to leverage the capabilities of Python programming in the realm of spatial analysis. It goes beyond merely connecting these two realms; it actively fosters their collaboration. By advancing knowledge in spatial sciences and highlighting Python's pivotal role in data analysis and application development, this book plays a crucial part in addressing the challenge of effectively harnessing geographical data.

Today, the relentless depletion of natural resources has reached a critical juncture, demanding innovative solutions. Advanced Geospatial Practices in Natural Environment Resource Management dives into the intricate tapestry of issues jeopardizing ecosystems. This book systematically dissects the fundamental drivers, traces the historical evolution, and elucidates the underlying causes that have led to this precarious point. From deforestation to pollution, from climate change to habitat destruction, these challenges are multifaceted. By harnessing the power of data-driven decision-making and predictive modeling, this book advocates for a profound shift in the approach to environmental issues. This book also explores the complexities of water, soil, and air-related challenges, offering technically sound solutions that strike a balance between legislative, economic, social, and political considerations. The holistic approach championed in these pages holds the potential to benefit governments, businesses, and communities alike. This book is ideal for a diverse audience, including students, scholars, and researchers, as well as environmental science enthusiasts, geospatial technology professionals, and AI/ML practitioners. Policymakers, environmental engineers, and professionals engaged in governmental and corporate sustainability initiatives will also discover practical solutions to address the pressing challenges.

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.