PhD Project

Housing forms of poverty are often associated with the Global South, especially through the depiction of slums. In this study, we systemize physical housing forms representing poverty in Europe. The continent features a huge diversity of such forms, rooting in different politics, cultures, histories and lifestyles. We discover and categorize these unindexed housing morphologies to enlarge the scientific ontological portfolio for Europe. An extensive literature research with more than 1,000 screened items builds the fundament. We use satellite data to capture physical morphologies of housing forms and geographic indicators on location, structure and formal status. Beyond, we research socio-cultural backgrounds described by terms such as ‘ghetto’ or ‘trailer park’. We find a huge variety in physical forms in our sample and develop a categorization of six major classes ranging from rough shelters (e.g., tents) to multi-storey buildings as general taxonomy. The research reveals diverse living forms (e.g., underground-, or deteriorated housing). Beyond the housing morphology, we describe these classes by the structural pattern and their legal status. Geographically, we find urban as well as rural locations, with a concentration in Southern Europe. The majority of morphologies relates to refugees, ethnic minorities and socioeconomically prone people – the underprivileged.

The urban environment is in constant motion, mostly through construction but also through destruction of urban elements. While formal development is a process with long planning periods and thus the built landscape appears static, informal or spontaneous settlements seem to be subject to high dynamics in their ever unfinished urban form. However, the dynamics and morphological characteristics of physical transformation in such settlements of urban poverty have been hardly empirically studied on a global scale or temporal consistent foundation. This paper aims at filling this gap by using Earth observation data to provide a temporal analysis of built-up transformation over a period of ~7 years in 16 documented manifestations of urban poverty. This work applies visual image interpretation using very high resolution optical satellite data in combination with in-situ- and Google Street View images to derive 3D city models. We measure physical spatial structures through six spatial morphologic variables - number of buildings, size, height, orientation, heterogeneity and density. Our temporal assessment reveals inter- as well intra-urban differences and we find different, yet generally high morphologic dynamic across study sites. This is expressed in manifold ways: from demolished and reconstructed areas to such where changes appeared within the given structures. Geographically, we find advanced dynamics among our sample specifically in areas of the global south. At the same time, we observe a high spatial variability of morphological transformations within the studied areas. Despite partly high morphologic dynamics, spatial patterns of building alignments, streets and open spaces remain predominantly constant.

Today satellite images are mostly exploited automatically due to advances in image classification methods. Manual visual image interpretation (MVII), however, still plays a significant role e.g. to generate training data for machine-learning algorithms or for validation purposes. In certain urban environments, however, of e.g. highest densities and structural complexity, textural and spectral complications in overlapping roof-structures still demand the human interpreter if one aims to capture individual building structures. The cognitive perception and real-world experience are still inevitable. Against these backgrounds, this paper aims at quantifying and interpreting the uncertainties of mapping rooftop footprints of such areas. We focus on the agreement among interpreters and which aspects of perception and elements of image interpretation affect mapping. Ten test persons digitized six complex built-up areas. Hereby, we receive quantitative information about spatial variables of buildings to systematically check the consistency and congruence of results. An additional questionnaire reveals qualitative information about obstacles. Generally, we find large differences among interpreters mapping results and a high consistency of results for the same interpreter. We measure rising deviations correlate with a rising morphologic complexity. High degrees of individuality are expressed e.g. in time consumption, in-situ- or GIS-precognition whereas data source mostly influences the mapping procedure. By this study, we aim to fill a gap as prior research using MVII often does not implement an uncertainty analysis or quantify mapping aberrations. We conclude that remote sensing studies should not only rely unquestioned on MVII for validation; further data and methods are needed to suspend uncertainty.

Urban morphologies change over time. The dynamics and nature of morphological changes in informal settlements or slums have largely not been scientifically investigated. Consequently, it is necessary to fill the gap of the international demand for timeline analysis. In this work, earth observation (EO) is used to discover morphologic changes within eight years (2006-2014) in Nairobi’s major slum Kibera. Research mostly handles automated detection but in this study the classical visual image interpretation is applied on a very high level of detail capturing buildings in three dimensions. Consistencies and deviations in time are measured according to morphological variables. We find dynamics in the slum area high in terms of a 77% rise in number of buildings due to arising, splitting, upgrading or demolishing; at the same time, density increases only by 10%. Overall, the general pattern of complex, organic structure remains mostly unchanged.

Urban morphologies change over time. The dynamics and nature of morphological changes in informal settlements or slums have largely not been scientifically investigated. Consequently, it is necessary to fill the gap of the international demand for timeline analysis. In this work, earth observation (EO) is used to discover morphologic changes within eight years (2006-2014) in Nairobi’s major slum Kibera. Research mostly handles automated detection but in this study the classical visual image interpretation is applied on a very high level of detail capturing buildings in three dimensions. Consistencies and deviations in time are measured according to morphological variables. We find dynamics in the slum area high in terms of a 77% rise in number of buildings due to arising, splitting, upgrading or demolishing; at the same time, density increases only by 10%. Overall, the general pattern of complex, organic structure remains mostly unchanged.