A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities
Abstract
:1. Introduction
2. From Surveys to Remote Sensing Data in Slum Mapping
3. Temporal Growth of Slums
4. Slum Mapping from Remote Sensing
- Detection—this step includes methods that locate features of interest in an image and is usually the first stage in image classification.
- Delineation—this step involves identifying the spatial extent of features.
- Characterization—in this step, features of interest are labeled as belonging to a specific class.
4.1. Multi-Scale Approaches
4.2. Image Texture Analysis
4.3. Landscape Analysis
4.4. Object-Based Image Analysis
4.5. Building Feature Extraction
4.6. Data Mining
4.7. Remote Sensing Data for Supporting Socio-Economic Assessment
5. Challenges and Opportunities
5.1. Spatial and Temporal Distribution of Slum Studies Using H/VH-R Remote Sensing Imagery
5.2. Limitations of Remote Sensing in Slum Detection and Mapping
5.3. Data Fusion of Remote Sensing and Auxiliary Data
5.4. Emerging Sources of Data on Slums
5.5. Geosensor Networks
6. Discussion and Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Approach | Properties |
---|---|
Multi-scale (Section 4.1) | Description |
Discriminate objects based on properties at different scales. | |
Commonly exploited remote sensing attributes | |
Spatial, contextual and multi-scale. | |
Extraction approach | |
Detection and characterization. | |
Sample studies | |
Advantages | |
| |
Limitations | |
| |
Image texture analysis (Section 4.2) | Description |
Extract features in an image based on its shape, size and tonal variation within the image. Image texture analysis have widely been used as part of OBIA extraction strategies. | |
Commonly exploited remote sensing attributes | |
Spatial, spectral, contextual and multi-scale. | |
Extraction approach | |
Detection and characterization. | |
Sample studies | |
Advantages | |
| |
Limitations | |
| |
Landscape analysis (Section 4.3) | Description |
Use spatial metrics developed in the field of landscape ecology to quantitatively analyze the spatial patterns of land cover. These metrics describe spatial composition and configuration. | |
Commonly exploited remote sensing attributes | |
Spatial and spectral. | |
Extraction approach | |
Detection and characterization. | |
Sample studies | |
Advantages | |
Wide variety of metrics available.Generally easy to interpret. | |
Limitations | |
| |
Object-based image analysis (Section 4.4) | Description |
Treats images as a composition of objects. | |
Commonly exploited remote sensing attributes | |
Spatial, spectral, contextual and multi-scale. | |
Extraction approach | |
Detection and characterization. | |
Sample studies | |
Advantages | |
| |
Limitations | |
| |
Building feature extraction (Section 4.5) | Description |
Use computer generated algorithms and tools to help humans with extracting knowledge from large volumes of data. | |
Commonly exploited remote sensing attributes | |
Spatial, spectral and contextual. | |
Extraction approach | |
Detection, delineation and characterization. | |
Sample studies | |
Advantages | |
| |
Limitations | |
| |
Data mining (Section 4.6) | Description |
Use elevation data to extract individual slum dwellings. | |
Commonly exploited remote sensing attributes | |
Spatial, spectral and contextual. | |
Extraction approach | |
Detection and characterization. | |
Sample studies | |
Advantages | |
| |
Limitations | |
| |
Socio-economic measures (Section 4.7) | Description |
Estimate socio-economic information from remotely sensing imagery or link them with census or similar data. | |
Commonly exploited remote sensing attributes | |
Spatial and spectral. | |
Extraction approach | |
Detection and characterization. | |
Sample studies | |
Advantages | |
| |
Limitations | |
|
Studies | Approach | Administrative District | Local Area | ||||||
---|---|---|---|---|---|---|---|---|---|
A | B | C | D | * E | * F | G | |||
Kit et al. [51] | X | Andhra Pradesh | Hyderabad | ||||||
Kit et al. [150] | X | Andhra Pradesh | Hyderabad | ||||||
Kit and Ludeke [17] | X | Andhra Pradesh | Hyderabad | ||||||
Niebergall et al. [78] | X | X | Delhi | Delhi | |||||
Kohli et al. [19] | X | X | Gujarat | Ahmedabad | |||||
Kohli et al. [151] | X | X | Maharashtra | Pune | |||||
Yadav et al. [152] | X | Maharashtra | Mumbai | ||||||
Shekhar [153] | Maharashtra | Pune | |||||||
Baud et al. [34] | X | X | Delhi | Delhi | |||||
Kuffer et al. [67] | X | Delhi | Delhi | ||||||
Kuffer and Barros [154] | X | Delhi | Delhi | ||||||
Kuffer et al. [60] | X | X | Maharashtra | Mumbai | |||||
Bhangale et al. [155] | X | Maharashtra | Mumbai | ||||||
Total | 3 | 3 | 3 | 4 | 0 | 2 | 2 |
Studies | Approaches | Administrative District | Local Area | ||||||
---|---|---|---|---|---|---|---|---|---|
A | * B | * C | D | * E | F | G | |||
Filho and Sobreira [63] | X | Pernambuco | Racife | ||||||
Filho and Sobreira [81] | X | Sao Paulo | Campinas | ||||||
Amorim et al. [105] | X | Pernambuco | Racife | ||||||
De Melo and Conci [156] | X | Rio de Janiero | Rio de Janiero | ||||||
De Melo and Conci [156] | X | Sao Paulo | Campinas | ||||||
Hofmann [118] | X | Rio de Janeiro | Rio de Janeiro | ||||||
Leao and Leao [64] | X | X | Rio Grande do Sul | Canela | |||||
Novack and Kux [71] | X | Sao Paulo | Sao Paulo | ||||||
Ribeiro [157] | X | Sao Paulo | Embu | ||||||
Total | 6 | 0 | 0 | 2 | 0 | 1 | 1 |
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Mahabir, R.; Croitoru, A.; Crooks, A.T.; Agouris, P.; Stefanidis, A. A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities. Urban Sci. 2018, 2, 8. https://doi.org/10.3390/urbansci2010008
Mahabir R, Croitoru A, Crooks AT, Agouris P, Stefanidis A. A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities. Urban Science. 2018; 2(1):8. https://doi.org/10.3390/urbansci2010008
Chicago/Turabian StyleMahabir, Ron, Arie Croitoru, Andrew T. Crooks, Peggy Agouris, and Anthony Stefanidis. 2018. "A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities" Urban Science 2, no. 1: 8. https://doi.org/10.3390/urbansci2010008