Contents
1 Introduction 1
1.0.1 Research questions, objectives and outline of the thesis . . . . 5
1.1 Case studies . . . . 8
1.1.1 Ouagadougou, Burkina Faso . . . . 8
1.1.2 Dakar, Senegal . . . 10
1.1.3 Kampala, Uganda . . . 10
1.1.4 Dar es Salaam, Tanzania . . . 11
1.2 Extracting land cover from very-high-resolution satellite images . . . 11
1.2.1 Towards fully automated segmentation solutions . . . 13
1.2.2 Finding the operating spatial scale to optimize segmentation parameters . . . 14
1.2.3 On the parsimony and interpretability of land cover classification 16 1.2.4 Bringing the edge in classification performance . . . 17
1.3 Urban population, socio-economic and epidemiological models in sub- Saharan African cities using satellite information . . . 18
1.3.1 Population mapping . . . 18
1.3.2 Poverty through the lens of satellites . . . 20
1.3.3 Mapping of infectious diseases . . . 21
1.4 On transparency and reproducibility . . . 24
2 Improvements in land urban land cover classification 25 2.1 Towards automated segmentation solutions . . . 25
2.2 Spatially partitioned segmentation parameter optimization . . . 44
2.3 Reducing the computational burden and increasing parsimony . . . . 68
2.4 Extreme Gradient Boosting for image classification tasks . . . 92
2.5 Overview . . . 98
3 Applications 101 3.1 Population estimation . . . 101
3.2 Household wealth . . . 120
3.3 Malaria prevalence . . . 140 xiii
CONTENTS
3.4 Overview . . . 159
4 Conclusion 161 4.1 Summary of the outcomes . . . 161
4.1.1 Taking local specifications under account: a spatial miscro- scope for urban analysis . . . 162
4.1.2 On the automation, parsimony and accuracy of GEOBIA clas- sifications . . . 163
4.1.3 Social sensing from space . . . 164
4.2 Limitations and points of discussion . . . 164
4.2.1 Why not Deep Leaning? . . . 164
4.2.2 Who is to blame? The quantification of error . . . 165
4.2.3 On the appropriate reading of the secondary model outputs . 165 4.2.4 On the transferability potential . . . 166
4.3 Future Prospects . . . 166
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