Use of IKONOS satellite data to identify informal settlements in Dehradun, India

Jain, Srishti

doi:10.1080/01431160600705122

Cited by 41 publications

(22 citation statements)

References 8 publications

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“…Slum identification and classification pose an additional challenge, because of differences in definition and appearance within and among contexts. The relatively low number of international publications on RS-based slum/informal settlement identification in India, despite the acknowledged scale and importance of the problem, substantiates the challenges involved in this area [11,38]. Our research is an attempt to take a step forward in this direction by using a systematic classification approach using an OOA guided by locally adapted generic slum ontology [10].…”

Section: Discussionmentioning

confidence: 99%

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Transferability of Object-Oriented Image Analysis Methods for Slum Identification

et al. 2013

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Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed.

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Section: Discussionmentioning

confidence: 99%

“…This, consequently, leads to challenges in image-based detection and characterization [10]. Jain [11] studied the identification of slums in fused IKONOS images. She found that the heterogeneity of an urban environment could not be represented with a pixel-based classification approach using only spectral values.…”

Section: Introductionmentioning

confidence: 99%

Transferability of Object-Oriented Image Analysis Methods for Slum Identification

et al. 2013

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“…For instance, in Dehradun, slums are characterized by tone differences due to different roofing materials (e.g., plastic, wood etc.) [75], but in Guangzhou [97] or Ahmedabad [5], spectrally similar roofing material characterizes slums. Regarding physical site characteristics, there is also no general agreement; however, slums are often located in areas that are not suitable for constructions (e.g., on a flood plain, steep slope or other hazardous locations) [157].…”

Section: Characterization Of Slum Areasmentioning

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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“…Visual interpretation performed by interpreters familiar with local conditions provides a flexible and useful approach to slum mapping, though it does have shortcomings for repetitive surveys of very large cities due to difficulties in controlling quality over time and between interpreters. Later pixel based image classification is widely used in slum analysis and it also helped to understand the patterns over time and space (Jain, Sokhi and Sur, 2005;Jain, 2007;Weeks et al, 2007). But pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments.…”

Section: Related Work On Detecting Slumsmentioning

confidence: 99%

“…Source: Environmental Status Report, 2006-2007 The image classification in e Cognition is based on userdefined fuzzy class descriptions of spectral, spatial and contextual features. The classification started with assigning image objects to roads and water bodies.…”

Section: Classificationmentioning

confidence: 99%

Detecting Slums From Quick Bird Data in Pune Using an Object Oriented Approach

Shekhar

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:We have been witnessing a gradual and steady transformation from a pre dominantly rural society to an urban society in India and by 2030, it will have more people living in urban than rural areas. Slums formed an integral part of Indian urbanisation as most of the Indian cities lack in basic needs of an acceptable life. Many efforts are being taken to improve their conditions. To car ry out slum renewal programs and monitor its implementation, slum settlements should be recorded to obtain an adequate spatial data base. This can be only achieved through the analysis of remote sensing data with very high spatial resolution. Regarding the occurrences of settlement areas in the remote sensing data pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments. Hence there is a need for sophisticated method and data for slum analysis. An attempt has been made to detect and discriminate the slums of Pune city by describing typical characteristics of these settlements, by using eCognition software from quick bird data on the basis of object oriented approach. Based on multi resolution segmentation, initial objects were created and further depend on texture, geometry and contextual characteristics of the image objects, they were classified into slums and non-slums. The developed rule base allowed the description of knowledge about phenomena clearly and easily using fuzzy membership functions and the described knowledge stored in the cl assification rule base led to the best classification with more than 80% accuracy.

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Use of IKONOS satellite data to identify informal settlements in Dehradun, India

Cited by 41 publications

References 8 publications

Transferability of Object-Oriented Image Analysis Methods for Slum Identification

Transferability of Object-Oriented Image Analysis Methods for Slum Identification

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

Detecting Slums From Quick Bird Data in Pune Using an Object Oriented Approach

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