000			01882nam a2200217 4500
005			20250401133023.0
008			250401b |||||||| |||| 00| 0 eng d
020			_a9781639877454
037			_cTextual
040			_aRTL _cRTL
084			_aU:(D6,9(B) R3 _qRTL
245			_aGIS and RS: Practical Machine Learning Tools and Techniques
260			_aNew York _bMurphy & Moore Publishing _c2023
300			_a244p. _bIncludes list of contributors and index
520			_aMachine 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.
650			_aGeographic information systems _9751517
650			_aRemote sensing
650			_aGeographic information systems - equipments and supplies _9751672
700			_aThomas, Dilan _eEditor _9751673
942			_2CC _n0 _cTB _hU:(D6,9(B) R3
999			_c1308329 _d1308329