Building Feature Extraction with Machine Learning: Geospatial Applications
1. Provides the fundamentals of feature extraction methods and applications along with fundamentals of machine learning. 2. Discusses in detail the advantages of using machine learning in geospatial feature extraction. 3. Explains the methods for estimating object height from optical satellite remote sensing images using Python, R, QGIS, and GRASS GIS implementations. 4. Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetr…
Read More ↓Title Statements
- Full title :Building Feature Extraction with Machine Learning : Geospatial Applications / Prakash P.S., Bharath.H. Aithal.
Summary
1. Provides the fundamentals of feature extraction methods and applications along with fundamentals of machine learning. 2. Discusses in detail the advantages of using machine learning in geospatial feature extraction. 3. Explains the methods for estimating object height from optical satellite remote sensing images using Python, R, QGIS, and GRASS GIS implementations. 4. Includes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessment. 5. Highlights the potential of machine learning and geospatial technology for future project developments.
Read More ↓Edition Statements
- Publication Date:2022
Publication Statements
- Publisher(s):Taylor & Francis Ltd
- Place of Publication:London
- Language:eng
- Publication Date:2022
Notes
Introduction. Geospatial Big Data for Machine Learning. Spatial Feature Extraction. Building Height Estimation. 3D Feature Mapping. Applications and Case Studies.
The Physical Object
- Description:128 Seiten
Edition Identifiers
- ISBN:9781032255330, 1032255331
- oclc:on1357137793
- Dewey Decimal:910.020285361
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