Building Feature Extraction with Machine Learning: Geospatial Applications, 1st Edition eBook
$30.00
. Big geospatial datasets created by large infrastructure projects require massive computing resources to process.
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About this eTextbook
. Big geospatial datasets created by large infrastructure projects require massive computing resources to process. Feature extraction is a process used to reduce the initial set of raw data for manageable image processing, and machine learning (ML) is the science that supports it.
This book focuses on feature extraction methods for optical geospatial data using ML. It is a practical guide for professionals and graduate students who are starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies on how to collect height values for spatial features, how to develop 3D models in a map context, and others.
Features Provides the basics of feature extraction methods and applications along with the fundamentals of machine learning Discusses in detail the application of machine learning techniques in geospatial building feature extraction Explains the methods for estimating object height from optical satellite remote sensing images using Python Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessment Highlights the potential of machine learning and geospatial technology for future project developments This book will be of interest to professionals, researchers, and graduate students in geoscience and earth observation, machine learning and data science, civil engineers, and urban planners. ISBNs for Building Feature Extraction with Machine Learning are 9781000817201, 1000817202 and the print ISBNs are 9781032255330, 1032255331. Additional ISBNs include 9781003288046, 9781032263830, 9781000817195.
Publication details
- Author: Bharath.H. Aithal; Prakash P.S.
- Publisher: CRC Press
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About this eTextbook
. Big geospatial datasets created by large infrastructure projects require massive computing resources to process. Feature extraction is a process used to reduce the initial set of raw data for manageable image processing, and machine learning (ML) is the science that supports it.
This book focuses on feature extraction methods for optical geospatial data using ML. It is a practical guide for professionals and graduate students who are starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies on how to collect height values for spatial features, how to develop 3D models in a map context, and others.
Features Provides the basics of feature extraction methods and applications along with the fundamentals of machine learning Discusses in detail the application of machine learning techniques in geospatial building feature extraction Explains the methods for estimating object height from optical satellite remote sensing images using Python Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessment Highlights the potential of machine learning and geospatial technology for future project developments This book will be of interest to professionals, researchers, and graduate students in geoscience and earth observation, machine learning and data science, civil engineers, and urban planners. ISBNs for Building Feature Extraction with Machine Learning are 9781000817201, 1000817202 and the print ISBNs are 9781032255330, 1032255331. Additional ISBNs include 9781003288046, 9781032263830, 9781000817195.
Publication details
- Author: Bharath.H. Aithal; Prakash P.S.
- Publisher: CRC Press
What you receive
- A licensed digital edition of this title, supplied as PDF or EPUB. The format is set by the publisher, so email us the ISBN before ordering and we will confirm which one it is.
- Delivered to the email address on your order as a secure download link, usually within minutes and always within 24 hours of payment.
- Download links do not expire and there is no cap on re-downloads. Readable on any computer, tablet or phone, online or offline.
- This is a digital edition. Nothing is printed, packed or posted, and there is no delivery charge.
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