Explainable Machine Learning for Geospatial Data Analysis PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Explainable Machine Learning for Geospatial Data Analysis PDF full book. Access full book title Explainable Machine Learning for Geospatial Data Analysis by Courage Kamusoko. Download full books in PDF and EPUB format.
Author: Courage Kamusoko
Publisher: CRC Press
ISBN: 104025246X
Category : Technology & Engineering
Languages : en
Pages : 280
Get Book Here
Book Description
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.
Author: Courage Kamusoko
Publisher: CRC Press
ISBN: 104025246X
Category : Technology & Engineering
Languages : en
Pages : 280
Get Book Here
Book Description
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.
Author: Courage Kamusoko
Publisher:
ISBN: 9781032503813
Category : Artificial intelligence
Languages : en
Pages : 0
Get Book Here
Book Description
"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"--
Author: Galety, Mohammad Gouse
Publisher: IGI Global
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 359
Get Book Here
Book Description
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.
Author: David S. Jordan
Publisher: Packt Publishing Ltd
ISBN: 1803240342
Category : Computers
Languages : en
Pages : 308
Get Book Here
Book Description
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.
Author: Vijay P. Singh
Publisher: Springer Nature
ISBN: 9819713161
Category :
Languages : en
Pages : 298
Get Book Here
Book Description
Author: Saurabh Prasad
Publisher: Elsevier
ISBN: 044319078X
Category : Science
Languages : en
Pages : 366
Get Book Here
Book Description
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
Author: Hugo Carlos-Martinez
Publisher: Springer Nature
ISBN: 3031614402
Category :
Languages : en
Pages : 259
Get Book Here
Book Description
Author: Song Gao
Publisher: CRC Press
ISBN: 1003814956
Category : Technology & Engineering
Languages : en
Pages : 508
Get Book Here
Book Description
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.
Author: Bhisham Sharma
Publisher: CRC Press
ISBN: 1040095909
Category : Computers
Languages : en
Pages : 301
Get Book Here
Book Description
This book aims to provide a comprehensive overview of the various services that are available to help cities develop their smart communities. It includes a variety of topics such as artificial intelligence, blockchain, advanced computing, and the Internet of Everything. Smart Cities: Blockchain, AI, and Advanced Computing is structured with independent chapters, each highlighting the current and future state-of-the-art technologies addressing smart city challenges. The book covers a variety of application areas, including healthcare, transportation, smart grids, supply chain management, and financial systems. There are both theoretical and empirical investigations in this book; they cover a wide range of topics related to smart city development and implementation, among others, all of which have a significant impact on the creation of smart cities. This book then examines the state‐of‐the‐art blockchain technology for smart city challenges and programs that might enhance the quality of life in urban areas and encourage cultural and economic growth. This book is written especially for the students, researchers, academicians, and industry professionals looking for initiatives and advancements in technologies with a primary focus on their implications for smart cities.
Author: Steven L. Brunton
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
Get Book Here
Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
