Building Feature Extraction with Machine Learning: Geospatial Applications

145 Pages·2022·13.183 MB·English

by  Bharath H. Aithal, Prakash P.S.

Building Feature Extraction
with Machine Learning
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 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 fundamentals of feature extraction methods and
applications along with the fundamentals of machine learning
• Discusses in detail the application of machine learning techni-
ques 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 photo-
grammetric 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.

Building Feature
Extraction with Machine
Learning
Geospatial Applications
Bharath H. Aithal and Prakash P.S.
First edition published 2023
by CRC Press
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and by CRC Press
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© 2023 Bharath H. Aithal and Prakash P.S.
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ISBN: 978-1-032-25533-0 (hbk)
ISBN: 978-1-032-26383-0 (pbk)
ISBN: 978-1-003-28804-6 (ebk)
DOI: 10.1201/9781003288046
Typeset in Times
by MPS Limited, Dehradun
Dedication
To all geospatial application enthusiasts out there

Contents
Preface......................................................................................................xi
Acknowledgements................................................................................xiii
Authors....................................................................................................xv
Chapter 1 Introduction.........................................................................1
1.1 Geospatial Technologies............................................1
1.2 Feature Extraction......................................................2
1.3 Geospatial Machine Learning....................................3
1.4 Height Estimation......................................................5
1.5 Three-Dimensional Mapping.....................................6
References............................................................................7
Chapter 2 Geospatial Big Data for Machine Learning.......................9
2.1 Geospatial Big Data...................................................9
2.2 Machine Learning Framework for Geospatial
Big Data...................................................................11
2.3 Data Sources............................................................13
2.3.1 USGS – NASA’s Mission...........................13
2.3.2 Copernicus Missions....................................15
2.3.3 ISRO Missions.............................................16
2.3.4 Other Missions.............................................17
2.4 The Challenge with EO Data..................................18
2.5 GeoAI Platforms......................................................18
2.6 Choosing the Right Data.........................................20
References..........................................................................27
Chapter 3 Spatial Feature Extraction.................................................33
3.1 Feature Extraction....................................................33
3.2 Machine Learning Models.......................................33
3.2.1 Maximum Likelihood Classifiers................34
3.2.2 Random Forest.............................................35
3.2.3 Naïve Bayes.................................................36
3.2.4 The SVM.....................................................38
3.2.5 Neural Networks..........................................41
3.2.6 Convolutional Neural Networks..................44
3.3 Deep Learning Architecture....................................48
3.4 Model Architecture..................................................51
vii
viii Contents
3.4.1 Loss Function..............................................52
3.4.2 Data Augmentation......................................53
3.4.3 Hyperparameters..........................................53
3.4.4 Data Normalization......................................54
3.4.5 Transfer Learning........................................54
3.5 Methods....................................................................54
3.5.1 Image Pre-Processing..................................55
3.5.2 Model Training............................................55
3.5.3 Post-Processing............................................56
3.5.4 Accuracy Evaluation....................................56
3.6 Findings and Conclusions........................................58
References..........................................................................61
Chapter 4 Building Height Estimation..............................................67
4.1 Significance of Building Height..............................67
4.2 Background..............................................................68
4.3 Estimation of Height from Stereo Satellite
Images......................................................................70
4.3.1 Stereo Satellite Images................................71
4.3.2 Surface Model Preparation..........................72
4.3.3 DSM Quality Evaluation.............................73
4.3.4 Preparation of a Terrain Model..................74
4.3.4.1 MDS Filtering...............................74
4.3.4.2 Grid-Based Method......................74
4.3.4.3 Interpolation..................................75
4.3.4.4 Slope-Based Filter........................75
4.3.4.5 Road Buffers.................................75
4.4 Estimating the Height of a Building.......................76
4.4.1 DTM Method...............................................76
4.4.2 Buffer Polygons...........................................76
4.5 Height Estimations and Quality Evaluation............77
4.5.1 DSM Quality Evaluation.............................77
4.5.2 DTM Quality Evaluation.............................78
4.5.3 Building Height Values...............................78
4.6 Future Scope of Height Estimations.......................79
References..........................................................................80
Chapter 5 3D Feature Mapping.........................................................83
5.1 3D Mapping from Geospatial Data.........................83
5.2 History of 3D Mapping...........................................84
5.3 Data Standards and Interoperability........................85
Contents ix
5.4 Data Sources for 3D Mapping................................88
5.5 Software Tools for 3D Mapping.............................89
5.6 Experiments..............................................................90
References..........................................................................93
Chapter 6 Application Use Cases......................................................99
6.1 Potential Applications..............................................99
6.2 Case Study #1: Urban Structure Extraction –
An Indian Context.................................................100
6.2.1 Study Area.................................................102
6.2.2 Datasets......................................................103
6.2.3 Method.......................................................104
6.2.4 Results and Conclusions...........................104
6.3 Case Study #2: Rooftop Solar Potential
Estimation...............................................................106
6.3.1 Solar Radiation..........................................107
6.3.2 UAV or Drone-Captured Imagery............109
6.3.3 Building Roof Extraction..........................110
6.3.4 Shadow Removal.......................................110
6.3.5 Energy Estimations....................................112
6.4 Case Study #3: Assessment of Urban Built-Up
Volume...................................................................115
6.4.1 Study Area and Datasets...........................116
6.4.2 Method.......................................................116
6.4.3 DSM Generation........................................117
6.4.4 Built-Up Area Extraction..........................119
6.4.5 Built-Up Volume Estimation....................120
6.4.6 Inference and Conclusions........................121
References........................................................................122
Index.....................................................................................................125