An automatic detection method for high density slums based on regularity pattern of housing using Gabor filter and GINI index

Praptono, Nursidik Heru; Sirait, Pahala; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

doi:10.1109/icacsis.2013.6761600

Cited by 9 publications

(8 citation statements)

References 16 publications

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“…In this study, the theme of economic and social class included sub‐themes of low education, low income, population density, smaller house size, lack of urban (official) water and electricity, and keeping birds and livestock at home. In this regard, Ompad et al and Praptono et al believed that houses built with unsuitable materials, small living space, high population density, lack of useful amenities, safe drinking water, unofficial source of water and electricity, low literacy, and low income affect the health of residents . Mahdi also points out that people at low social‐economic level, usually have low level of education compared with their peers, and the level of education in these families is often low.…”

Section: Discussionmentioning

confidence: 99%

Identifying the health problems of slum residents using social determinants of health: Kerman, Iran

Nekoei‐Moghadam

Heidari

Amiresmaeili

et al. 2019

Health Planning & Management

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Summary Introduction Although slum emerged in industrialized countries, today, all cities in the world are somehow facing this phenomenon. Slums create many problems in different areas, especially health for residents. Therefore, this research was conducted with the aim of identifying the health problems of the slum residents of Kerman city by using the social determinant of health approach. Method The present study is an applied study that was carried out qualitatively using semistructured interviews. Samples were selected using purposeful and snowball sampling methods. Data were analyzed using the Colaizy method. Results In this study, the health problems of residents of slums in the city of Kerman were identified by interviews. In total, seven main themes and 32 sub‐themes were extracted. The main concepts extracted include the economic and social class, the early childhood development, social exclusion and social support, employment and unemployment, addiction, food, transport, and traffic. Conclusion The problems of slums people are very widespread, and almost all of the problems affect the health of slums people. The place of living, job, income, the early childhood development and nutritional status are determinants that affect people's health.

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Section: Discussionmentioning

confidence: 99%

Identifying the health problems of slum residents using social determinants of health: Kerman, Iran

Nekoei‐Moghadam

Heidari

Amiresmaeili

et al. 2019

Health Planning & Management

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“…The analysis of the employed methods in the reviewed slum publications shows that most studies used commercial and rather expensive imagery. Only very few studies used free data sources such as GE image, mostly for visual image interpretation (e.g., [69,84,119]), visualization of slums [90] or combining GE with commercial imagery [72,80], whereas Praptono et al [98] used GE images to automatically detect slums employing a Gabor filter and GLCM with a promising accuracy of 74%. Many of the methods used commercial software solutions, but to some extent also open-source software.…”

Section: Methods Employed For Slum Mappingmentioning

confidence: 99%

“…On a metropolitan scale, researchers successfully correlated poverty rates with observed night-time lights (e.g., [187]). Alternative image sources for global slum mapping are, for instance, GE images that allow working with VHR imagery free of charge (democratizing data access), where texture-based image analysis showed promising accuracies [98]. To increase the classification accuracy, several studies have proposed the use of auxiliary data, such as the utility of DSM for built-up or roof extraction [153] or the usage of VGI (volunteered geographic information) [69].…”

Section: Access To Image Data and Contextual Factorsmentioning

confidence: 99%

Slums from Space—15 Years of Slum Mapping Using Remote Sensing

2016

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Abstract:The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000-2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical slum characteristics is required for the development of robust image-based proxies. Third, although the latest commercial sensor technologies provide image data of less than 0.5 m spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity in terms of extracted information levels (area or object based), implemented indicator sets (single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, OBIA approaches show good capabilities of extracting both area-and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.Keywords: slums; informal areas; urban remote sensing; Global South; VHR imagery Global Urbanization and Slum Dynamics: The ContextCurrently, about one-quarter of the world's urban population lives in slums, which are defined by UN-Habitat as informal settlements [1] or areas deprived of access to safe water, acceptable sanitation, and durable housing; in addition to being areas that are overcrowded and lack land tenure security [2]. Over the last 15 years, there has been renewed interest in slum improvement and eradication by local and international organizations dealing with development issues. During this period, slums became a more prominent subject of remote sensing (RS) image analysis. Supported by increased availability of very-high-resolution (VHR) data and methodological advances, many RS studies [3][4][5][6][7][8] aimed to produce information on the geography and dynamics of slums. ...

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“…Only very few studies used free data sources such as GE image, mostly for visual image interpretation (e.g., Gunter, 2009;Ward & Peters, 2007), visualization of slums (e.g., Marghany & van Genderen, 2014) or combining GE with commercial imagery (e.g., X. Huang et al, 2015;, whereas Praptono et al (2013) used GE images to automatically detect slums employing a Gabor filter and GLCM with a promising accuracy of 74%. Many of the methods used commercial software solutions, but to some extent also open-source software.…”

Section: Methods Employed For Slum Mappingmentioning

confidence: 99%

“…An alternative is employing the GLCM, which calculates several textural measures within a user-defined window size and shift (Haralick et al, 1973). Previous studies employing GLCM-derived texture measures for mapping deprived areas include contrast (e.g., Pesaresi et al, 2008), entropy (e.g., Praptono et al, 2013;, and variance (e.g., . Spatial metrics are increasingly used to analyze and quantify the urban morphology (e.g., Herold et al, 2005;Taubenböck, Wegmann, et al, 2009), where showed the utility of combining both texture and spatial metrics for extracting slums in Pune (India), but also illustrated uncertainties in slum identification .…”

Section: Introductionmentioning

confidence: 99%

Spatial patterns of deprivation in cities of the global south in very high resolution imagery

Kuffer

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An automatic detection method for high density slums based on regularity pattern of housing using Gabor filter and GINI index

Cited by 9 publications

References 16 publications

Identifying the health problems of slum residents using social determinants of health: Kerman, Iran

Identifying the health problems of slum residents using social determinants of health: Kerman, Iran

Slums from Space—15 Years of Slum Mapping Using Remote Sensing

Spatial patterns of deprivation in cities of the global south in very high resolution imagery

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