Explainable Machine Learning For Geospatial Data Analysis
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Explainable Machine Learning For Geospatial Data Analysis
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Author : Courage Kamusoko
language : en
Publisher:
Release Date : 2024-12
Explainable Machine Learning For Geospatial Data Analysis written by Courage Kamusoko and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12 with Travel categories.
"Explainable AI (XAI), a subfield of AI, is focused on providing complex AI models that are understandable to humans. This book highlights and explains details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric explainable machine learning approach for obtaining new insights from geospatial data. It presents the opportunities, challenges, and gaps in the machine and deep learning approaches for geospatial data analysis and how they are applied to solve various environmental problems in land cover changes. The author includes guidelines and code scripts (R, Python) valuable for practical readers"--
Explainable Machine Learning For Geospatial Data Analysis
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Author : Courage Kamusoko
language : en
Publisher: CRC Press
Release Date : 2024-12-06
Explainable Machine Learning For Geospatial Data Analysis written by Courage Kamusoko and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-06 with Technology & Engineering categories.
Explainable machine learning (XML), a subfield of AI, is focused on making complex AI models understandable to humans. This book highlights and explains the details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric, explainable machine learning approach to obtain new insights from geospatial data. It presents the opportunities, challenges, and gaps in the machine and deep learning approaches for geospatial data analysis and how they are applied to solve various environmental problems in land cover changes and in modeling forest canopy height and aboveground biomass density. The author also includes guidelines and code scripts (R, Python) valuable for practical readers. Features Data-centric explainable machine learning (ML) approaches for geospatial data analysis. The foundations and approaches to explainable ML and deep learning. Several case studies from urban land cover and forestry where existing explainable machine learning methods are applied. Descriptions of the opportunities, challenges, and gaps in data-centric explainable ML approaches for geospatial data analysis. Scripts in R and python to perform geospatial data analysis, available upon request. This book is an essential resource for graduate students, researchers, and academics working in and studying data science and machine learning, as well as geospatial data science professionals using GIS and remote sensing in environmental fields.
Ethics Machine Learning And Python In Geospatial Analysis
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Author : Galety, Mohammad Gouse
language : en
Publisher: IGI Global
Release Date : 2024-04-29
Ethics Machine Learning And Python In Geospatial Analysis written by Galety, Mohammad Gouse and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-29 with Technology & Engineering categories.
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.
Applied Geospatial Data Science With Python
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Author : David S. Jordan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-02-28
Applied Geospatial Data Science With Python written by David S. Jordan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-28 with Computers categories.
Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in Python The book includes colored images of important concepts Key Features Learn how to integrate spatial data and spatial thinking into traditional data science workflows Develop a spatial perspective and learn to avoid common pitfalls along the way Gain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expanded Book DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python. Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries. By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learn Understand the fundamentals needed to work with geospatial data Transition from tabular to geo-enabled data in your workflows Develop an introductory portfolio of spatial data science work using Python Gain hands-on skills with case studies relevant to different industries Discover best practices focusing on geospatial data to bring a positive change in your environment Explore solving use cases, such as traveling salesperson and vehicle routing problems Who this book is for This book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You’ll need to have a foundational knowledge of Python for data analysis and/or data science.
Hydrological Processes Modelling And Data Analysis
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Author : Vijay P. Singh
language : en
Publisher: Springer Nature
Release Date : 2024-04-01
Hydrological Processes Modelling And Data Analysis written by Vijay P. Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-01 with Technology & Engineering categories.
This book provides a state-of-the-art overview of the concepts and methodologies of data and modelling-driven hydrological analyses and their wide range of practical applications. The book is driven by the realisation that science, technology, engineering, and mathematics (STEM) concepts are essential in engineering hydrology to produce well-trained hydrologists. Such hydrologists will be equipped to face future societal challenges that require enhanced information and communication technology tools and integration of technical and non-technical areas. The book contains 12 chapters that introduce the principles of hydrological data analysis and highlight the current and emerging tools and techniques for analysing hydrologic data. The book describes the types of data typically used in hydrological analyses. It highlights the revolutionary technological advancements made toward hydrological data collection, including the use of drones and smartphones. The foremost objective of the book is to present the hydrological data analysis procedures. It explains the steps involved in data analysis for easy understanding of the reader, including students and professionals. This book presents case studies that demonstrate step-by-step procedures involved in typical analysis problems and may guide students and professionals in planning and executing steps to analyse the problem at hand. Case study examples will guide them to understand the intricacies of hydrological data analysis. It provides the readers with a complete package to enrich their understanding of the hydrological data analysis tools and techniques. Subsequently, as well-trained hydrologists, they could execute their learning to meet any specific grand challenge of the twenty-first century.
Recent Developments In Geospatial Information Sciences
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Author : Hugo Carlos-Martinez
language : en
Publisher: Springer Nature
Release Date : 2024-07-30
Recent Developments In Geospatial Information Sciences written by Hugo Carlos-Martinez and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-30 with Science categories.
This book presents a selection of manuscripts submitted to the 3rd International Conference on Geospatial Information Sciences (iGISc) 2023, a hybrid conference held in November 2023. These papers were selected by the Scientific Program Committee of the Conference after a rigorous peer-reviewed process. They represent a sample of the wide range of applications that characterize the interdisciplinary research areas of the Geospatial Information Sciences. It especially represents a fabulous opportunity to exhibit research carried out by young researchers and showcase it to the rest of the world and enhance the growth of the sciences while, at the same time, enforces them to level up with other research at the international level.
Recent Trends In Engineering Science And Technology
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Author : Jyoti Sekhar Banerjee
language : en
Publisher: CRC Press
Release Date : 2025-08-19
Recent Trends In Engineering Science And Technology written by Jyoti Sekhar Banerjee and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Technology & Engineering categories.
AIEST is a leading conference focused on providing a platform to researchers, scholars, engineers, scientists and industrial professionals to gather knowledge and bridge the gap between academia and its industrial aspects, around the world. This conference will be an immersive experience primarily focusing on the latest advancements and researchers in various fields of engineering, including but not limited to Mechanical Engineering, Civil Engineering, Electrical Engineering, Electronics and Communications Engineering, Computer Science Engineering, Information Technology and other interdisciplinary areas. AIEST will cater to the transitional practices where industrial knowledge would be conveyed to academia regarding real-time scenarios and practical findings, thus fostering collaboration and the development of innovative solutions to counter contemporary challenges in engineering and technology.
Advances In Machine Learning And Image Analysis For Geoai
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Author : Saurabh Prasad
language : en
Publisher: Elsevier
Release Date : 2024-04-26
Advances In Machine Learning And Image Analysis For Geoai written by Saurabh Prasad and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-26 with Science categories.
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
Data Driven Decision Making
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Author : Aadinath Pothuvaal
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Data Driven Decision Making written by Aadinath Pothuvaal and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
"Data-Driven Decision Making" explores the dynamic world of analytics, technology, and strategic decision-making. Authored by leading experts, this comprehensive guide serves as a beacon for individuals and organizations navigating the evolving landscape of data-driven decisions. We delve into harnessing data's power to inform and transform decisions across various domains. Through an interdisciplinary lens, the book integrates philosophy, technology, and real-world applications, guiding readers toward making informed, strategic choices in an era of data abundance. Key features include foundational principles, cutting-edge technologies, practical applications, ethical considerations, and global perspectives. Readers gain insights into AI, machine learning, advanced analytics, and data visualization. Real-world case studies illustrate how organizations leverage data for competitive advantage and innovation. Ethical dimensions are addressed, focusing on privacy, bias, and responsible use of emerging technologies. The book also provides actionable strategies for implementing data-driven approaches, optimizing decision support systems, and fostering a data-driven culture. "Data-Driven Decision Making" equips readers with knowledge and tools to navigate the intricate intersection of data, technology, and strategy.
Geospatial Ai
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Author : DR. VINAY KUMAR GADDAM
language : en
Publisher: AQUA PUBLICATIONS
Release Date :
Geospatial Ai written by DR. VINAY KUMAR GADDAM and has been published by AQUA PUBLICATIONS this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
In an era defined by massive spatial data and rapid advancements in artificial intelligence (AI), the confluence of geospatial technologies and AI has emerged as a critical frontier for both scientific inquiry and real-world applications. This textbook, Geospatial AI: Principles, Technologies, and Research Applications, aims to equip readers with a comprehensive understanding of this interdisciplinary domain. Designed to serve undergraduate students, postgraduate scholars, and early-career researchers, the book begins with foundational knowledge of geospatial sciences and progresses to advanced AI-driven applications and research methods. It covers the entire spectrum of topics including GIS, remote sensing, machine learning, deep learning, big earth data, cloud computing platforms, and domain-specific applications such as agriculture, urban analytics, environmental monitoring, and disaster management. Each chapter integrates theoretical concepts, real-world case studies, and hands-on coding examples using Python, Google Earth Engine, and open-source tools. This structure ensures that learners not only understand the "what" and "why" but also the "how" of Geospatial AI. We hope this book serves as a vital resource for academic learning, practical implementation, and future research. It is our sincere belief that Geospatial AI will be instrumental in addressing some of the most pressing challenges facing humanity today— from climate change to food security to sustainable urbanization.