The Experts below are selected from a list of 44229 Experts worldwide ranked by ideXlab platform
P M A Sloot - One of the best experts on this subject based on the ideXlab platform.
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survey based socio economic data from Slums in bangalore india
Scientific Data, 2018Co-Authors: Bharath Palavalli, Niveditha Menon, Robin King, Karin Pfeffer, Michael Lees, P M A SlootAbstract:In 2010, an estimated 860 million people were living in Slums worldwide, with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in Slums. In order to address and create slum development programmes and poverty alleviation methods, it is necessary to understand the needs of these communities. Therefore, we require data with high granularity in the Indian context. Unfortunately, there is a paucity of highly granular data at the level of individual Slums. We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self reported data. Our survey of Bangalore covered 36 Slums across the city. The Slums were chosen based on stratification criteria, which included geographical location of the slum, whether the slum was resettled or rehabilitated, notification status of the slum, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified with the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1,107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries. Machine-accessible metadata file describing the reported data (ISA-Tab format)
Michael Wurm - One of the best experts on this subject based on the ideXlab platform.
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Misperceptions of Predominant Slum Locations? Spatial Analysis of Slum Locations in Terms of Topography Based on Earth Observation Data
Remote Sensing, 2020Co-Authors: Inken Müller, Monika Kuffer, Hannes Taubenböck, Michael WurmAbstract:Slums are a physical expression of poverty and inequality in cities. According to the UN definition, this inequality is, e.g., reflected in the fact that Slums are much more often located in hazardous zones. However, this has not yet been empirically investigated. In this study, we derive proxies from multi-sensoral high resolution remote sensing data to investigate both the location of Slums and the location of slopes. We do so for seven cities on three continents. Using a chi-squared test of homogeneity, we compare the locations of formal areas with that of Slums. Contrary to the perception indirectly stated in the literature, we find that Slums are in none of the sample cities predominantly located in these exposed areas. In five out of seven cities, the spatial share of Slums on hills steeper than 10° is even less than 5% of all Slums. However, we also find a higher likelihood of Slums occurring in these exposed areas than of formal settlements. In six out of seven sample cities, the probability that a slum is located in steep areas is higher than for a formal settlement. As Slums mostly feature higher population densities, these findings reveal a clear tendency that slum residents are more likely to settle in exposed areas.
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JURSE - Sensitivity of slum size distributions as a function of spatial parameters for slum classification
2019 Joint Urban Remote Sensing Event (JURSE), 2019Co-Authors: John Friesen, Christoph Knoche, Jakob Hartig, Peter F. Pelz, Hannes Taubenböck, Michael WurmAbstract:In a recent work it was shown that the size distribution of Slums, derived from remote sensing data, seems to be similar. Furthermore, the results seem to be independent of city, country and continent. However, the dependence on the definition of Slums, i.e. the distance between two Slums at which they are regarded independent of each other, has not been investigated so far. The present work analyzes the influence of the separating distance on the slum size distributions for six cities (three in South America and three in Southeast Asia) showing that the mean of the slum sizes is close to 104 m2 and nearly independent from the separation. However, in future works, not only the distance but the type of land use between Slums should be considered in identifying Slums from remote sensing data.
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semantic segmentation of Slums in satellite images using transfer learning on fully convolutional neural networks
Isprs Journal of Photogrammetry and Remote Sensing, 2019Co-Authors: Michael Wurm, Thomas Stark, Xiao Xiang Zhu, Matthias Weigand, Hannes TaubenböckAbstract:Abstract Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of Slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping Slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolutional networks (FCN) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of FCNs to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from QuickBird is transferred to Sentinel-2 and TerraSAR-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. TerraSAR-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of SAR compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped Slums in the optical data: QuickBird image obtains 86–88% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55% and from 79 to 85% for PPV and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution.
David Satterthwaite - One of the best experts on this subject based on the ideXlab platform.
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improving the health and welfare of people who live in Slums
The Lancet, 2017Co-Authors: Richard J Lilford, Oyinlola Oyebode, David Satterthwaite, G J Melendeztorres, Yenfu Chen, Blessing Mberu, Samuel I Watson, Jo Sartori, Robert Ndugwa, Waleska Teixeira CaiaffaAbstract:In the first paper in this Series we assessed theoretical and empirical evidence and concluded that the health of people living in Slums is a function not only of poverty but of intimately shared physical and social environments. In this paper we extend the theory of so-called neighbourhood effects. Slums offer high returns on investment because beneficial effects are shared across many people in densely populated neighbourhoods. Neighbourhood effects also help explain how and why the benefits of interventions vary between slum and non-slum spaces and between Slums. We build on this spatial concept of Slums to argue that, in all low-income and-middle-income countries, census tracts should henceforth be designated slum or non-slum both to inform local policy and as the basis for research surveys that build on censuses. We argue that slum health should be promoted as a topic of enquiry alongside poverty and health.
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the history geography and sociology of Slums and the health problems of people who live in Slums
The Lancet, 2017Co-Authors: Ale Ezeh, Oyinlola Oyebode, David Satterthwaite, G J Melendeztorres, Jo Sartori, Robert Ndugwa, Yenfu Che, Lessing Mberu, Tilahun Nigatu Haregu, Samuel I WatsoAbstract:Summary Massive Slums have become major features of cities in many low-income and middle-income countries. Here, in the first in a Series of two papers, we discuss why Slums are unhealthy places with especially high risks of infection and injury. We show that children are especially vulnerable, and that the combination of malnutrition and recurrent diarrhoea leads to stunted growth and longer-term effects on cognitive development. We find that the scientific literature on slum health is underdeveloped in comparison to urban health, and poverty and health. This shortcoming is important because health is affected by factors arising from the shared physical and social environment, which have effects beyond those of poverty alone. In the second paper we will consider what can be done to improve health and make recommendations for the development of slum health as a field of study.
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Upgrading Slums: With and For Slum-Dwellers
Economic and Political Weekly, 2010Co-Authors: David SatterthwaiteAbstract:Although informal settlements are proliferating in cities across low- and middle-income nations, there is 40 years of experience to draw on in upgrading these "slum" settlements. The concept of upgrading itself implies an acceptance by governments that the slum to be "upgraded" is legitimate and that the inhabitants have a right to live there. Yet the plan to redevelop Dharavi in Mumbai using commercial developers goes against what we have learnt about good practice in upgrading.
Hannes Taubenböck - One of the best experts on this subject based on the ideXlab platform.
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Misperceptions of Predominant Slum Locations? Spatial Analysis of Slum Locations in Terms of Topography Based on Earth Observation Data
Remote Sensing, 2020Co-Authors: Inken Müller, Monika Kuffer, Hannes Taubenböck, Michael WurmAbstract:Slums are a physical expression of poverty and inequality in cities. According to the UN definition, this inequality is, e.g., reflected in the fact that Slums are much more often located in hazardous zones. However, this has not yet been empirically investigated. In this study, we derive proxies from multi-sensoral high resolution remote sensing data to investigate both the location of Slums and the location of slopes. We do so for seven cities on three continents. Using a chi-squared test of homogeneity, we compare the locations of formal areas with that of Slums. Contrary to the perception indirectly stated in the literature, we find that Slums are in none of the sample cities predominantly located in these exposed areas. In five out of seven cities, the spatial share of Slums on hills steeper than 10° is even less than 5% of all Slums. However, we also find a higher likelihood of Slums occurring in these exposed areas than of formal settlements. In six out of seven sample cities, the probability that a slum is located in steep areas is higher than for a formal settlement. As Slums mostly feature higher population densities, these findings reveal a clear tendency that slum residents are more likely to settle in exposed areas.
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JURSE - Sensitivity of slum size distributions as a function of spatial parameters for slum classification
2019 Joint Urban Remote Sensing Event (JURSE), 2019Co-Authors: John Friesen, Christoph Knoche, Jakob Hartig, Peter F. Pelz, Hannes Taubenböck, Michael WurmAbstract:In a recent work it was shown that the size distribution of Slums, derived from remote sensing data, seems to be similar. Furthermore, the results seem to be independent of city, country and continent. However, the dependence on the definition of Slums, i.e. the distance between two Slums at which they are regarded independent of each other, has not been investigated so far. The present work analyzes the influence of the separating distance on the slum size distributions for six cities (three in South America and three in Southeast Asia) showing that the mean of the slum sizes is close to 104 m2 and nearly independent from the separation. However, in future works, not only the distance but the type of land use between Slums should be considered in identifying Slums from remote sensing data.
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semantic segmentation of Slums in satellite images using transfer learning on fully convolutional neural networks
Isprs Journal of Photogrammetry and Remote Sensing, 2019Co-Authors: Michael Wurm, Thomas Stark, Xiao Xiang Zhu, Matthias Weigand, Hannes TaubenböckAbstract:Abstract Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of Slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping Slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolutional networks (FCN) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of FCNs to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from QuickBird is transferred to Sentinel-2 and TerraSAR-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. TerraSAR-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of SAR compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped Slums in the optical data: QuickBird image obtains 86–88% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55% and from 79 to 85% for PPV and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution.
Karin Pfeffer - One of the best experts on this subject based on the ideXlab platform.
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image based classification of Slums built up and non built up areas in kalyan and bangalore india
European Journal of Remote Sensing, 2019Co-Authors: Elena Ranguelova, Karin Pfeffer, Monika Kuffer, Berend Weel, Michael LeesAbstract:Slums, characterized by sub-standard housing conditions, are a common in fast growing Asian cities. However, reliable and up-to-date information on their locations and development dynamics is scarce. Despite numerous studies, the task of delineating slum areas remains a challenge and no general agreement exists about the most suitable method for detecting or assessing detection performance. In this paper, standard computer vision methods – Bag of Visual Words framework and Speeded-Up Robust Features have been applied for image-based classification of slum and non-slum areas in Kalyan and Bangalore, India, using very high resolution RGB images. To delineate slum areas, image segmentation is performed as pixel-level classification for three classes: Slums, Built-up and Non-Built-up. For each of the three classes, image tiles were randomly selected using ground truth observations. A multi-class support vector machine classifier has been trained on 80% of the tiles and the remaining 20% were used for testing. The final image segmentation has been obtained by classification of every 10th pixel followed by a majority filtering assigning classes to all remaining pixels. The results demonstrate the ability of the method to map Slums with very different visual characteristics in two very different Indian cities.
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Shifting approaches to Slums in Chennai: Political coalitions, policy discourses and practices
Singapore Journal of Tropical Geography, 2018Co-Authors: Tara Saharan, Karin Pfeffer, Isa BaudAbstract:Slums pose a persistent challenge for fast growing urban areas in the global South, despite several decades of policy intervention. While Chennai has adopted several strategies ranging from upgrading to reconstruction, the city has been unable to deliver its target of ‘clearing’ slum settlements. Through an analysis of four enumeration reports and a look at the evolving political contexts and subsequent practices, we illustrate the evolution of slum policy approaches in Chennai since the 1970s. The analysis shows slum practices in Chennai continue to be characterized by an underlying continuity, with relocation as the dominant mode of operation since the nineties. However, approaches to Slums have also evolved from paternalistic socialism with in‐situ development in the seventies, to approaches characterized by affordability and cost recovery in the eighties, to the aesthetics of global cities in the nineties, to the technology driven, to slum‐free ‘smart city’ discourse currently in vogue.
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survey based socio economic data from Slums in bangalore india
Scientific Data, 2018Co-Authors: Bharath Palavalli, Niveditha Menon, Robin King, Karin Pfeffer, Michael Lees, P M A SlootAbstract:In 2010, an estimated 860 million people were living in Slums worldwide, with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in Slums. In order to address and create slum development programmes and poverty alleviation methods, it is necessary to understand the needs of these communities. Therefore, we require data with high granularity in the Indian context. Unfortunately, there is a paucity of highly granular data at the level of individual Slums. We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self reported data. Our survey of Bangalore covered 36 Slums across the city. The Slums were chosen based on stratification criteria, which included geographical location of the slum, whether the slum was resettled or rehabilitated, notification status of the slum, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified with the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1,107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries. Machine-accessible metadata file describing the reported data (ISA-Tab format)
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Slums from Space: 15 Years of Slum Mapping Using Remote Sensing
Remote Sensing, 2016Co-Authors: Monika Kuffer, Karin Pfeffer, Richard SliuzasAbstract: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.
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review the emergence of Slums a contemporary view on simulation models
Environmental Modelling and Software, 2014Co-Authors: Debraj Roy, Bharath Palavalli, Karin Pfeffer, Michael Lees, M Peter A SlootAbstract:The existence of Slums or informal settlements is common to most cities of developing countries. Its role as single housing delivery mechanism has seriously challenged the popular notion held by policy makers, planners and architects. Today informality is a paradigm of city making and economic growth in Africa, Asia and Latin America. This paper discusses the role of computer simulation models to understand the emergence and growth of Slums in developing countries. We have identified the key factors influencing the growth of Slums and formulated a standardized set of criteria for evaluating slum models. The review of existing computer simulation models designed to understand slum formation and expansion enabled us to define model requirements and to identify new research questions with respect to exploring the dynamics of Slums.
