Building Feature Extraction with Machine Learning : Geospatial Applications
Book Details
Format
Hardback or Cased Book
ISBN-10
1032255331
ISBN-13
9781032255330
Publisher
Taylor & Francis Ltd
Imprint
CRC Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Dec 29th, 2022
Print length
128 Pages
Weight
362 grams
Dimensions
16.30 x 24.10 x 1.40 cms
Ksh 15,650.00
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This book focuses on feature extraction methods for optical geospatial data using Machine Learning (ML). It is a practical guide for professionals and graduate students starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies.
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.
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