Author: Dilan Thomas
Publisher: Murphy & Moore Publishing
Published: 2023-09-26
Total Pages: 0
ISBN-13: 9781639877454
DOWNLOAD EBOOKMachine learning (ML) refers to an artificial intelligence (AI) technique that teaches computers to learn from experiences. The algorithms of ML utilize computational techniques to learn information directly from data rather than using a preconceived equation as a model. ML is divided into two main categories, which include supervised learning and unsupervised learning. Each of them has diverse uses in geographic information system (GIS) and remote sensing (RS). ML is a key component of spatial analysis in GIS. It is extremely helpful for analyzing data in a variety of domains, including processing of satellite images. ML tools are primarily used in the processing of remote sensing data for interpretation, filtering and prediction. This book unravels the recent studies on machine learning tools and techniques for GIS and RS. As machine learning is emerging at a rapid pace, its contents will help the readers understand the modern concepts and applications of the subject. The book will serve as a valuable source of reference for graduate and postgraduate students.
Book Synopsis GIS and Rs: Practical Machine Learning Tools and Techniques by : Dilan Thomas
Download or read book GIS and Rs: Practical Machine Learning Tools and Techniques written by Dilan Thomas and published by Murphy & Moore Publishing. This book was released on 2023-09-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) refers to an artificial intelligence (AI) technique that teaches computers to learn from experiences. The algorithms of ML utilize computational techniques to learn information directly from data rather than using a preconceived equation as a model. ML is divided into two main categories, which include supervised learning and unsupervised learning. Each of them has diverse uses in geographic information system (GIS) and remote sensing (RS). ML is a key component of spatial analysis in GIS. It is extremely helpful for analyzing data in a variety of domains, including processing of satellite images. ML tools are primarily used in the processing of remote sensing data for interpretation, filtering and prediction. This book unravels the recent studies on machine learning tools and techniques for GIS and RS. As machine learning is emerging at a rapid pace, its contents will help the readers understand the modern concepts and applications of the subject. The book will serve as a valuable source of reference for graduate and postgraduate students.