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.
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.
Introduction. Geospatial Big Data for Machine Learning. Spatial Feature Extraction. Building Height Estimation. 3D Feature Mapping. Applications and Case Studies.
Dr. Bharath H. Aithal, is currently an assistant professor at Ranbir and Chitra Gupta School of Infrastructure Design and Management at Indian Institute of Technology Kharagpur. He obtained his Ph.D. from Indian Institute of Science. His areas of interest are spatial pattern analysis, Urban growth modelling, natural disasters, geoinformatics, landscape modelling, urban planning, open-source GIS, and digital image processing. He has published over 50 research papers in reputed peer reviewed journals and has presented over 100 papers in international and national conferences and symposiums. In 2020 he published with CRC Press, Urban Growth Patterns in India: Spatial Analysis for Sustainable Development and has contributed 6 book chapters to other publications.
Dr. Prakash P.S. is a postdoctoral researcher at Irish Centre of High-End Computing, Galway, Ireland, working on geo-spatial technologies. Prakash has substantial experience with earth observation datasets, including remote sensing, drone-based imagery, surveying, spatial libraries, machine learning, and artificial intelligence technologies. He has worked in geospatial technology and the renewable energy industry for over four years. His other qualifications include a Master of Technology in Geoinformatics from Bangalore's Karnataka State Remote Sensing Application Center and a Bachelor of Civil Engineering from Bangalore's Rashtreeya Vidyalaya College of Engineering.
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