How dynamic are slums? EO-based assessment of Kibera’s morphologic transformation

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.

[1]  H. Taubenböck,et al.  The physical face of slums: a structural comparison of slums in Mumbai, India, based on remotely sensed data , 2014 .

[2]  H. Taubenböck,et al.  Detecting social groups from space – Assessment of remote sensing-based mapped morphological slums using income data , 2018 .

[3]  Hannes Taubenböck,et al.  Monitoring and modelling of informal settlements - A review on recent developments and challenges , 2015, 2015 Joint Urban Remote Sensing Event (JURSE).

[4]  Ryan N. Engstrom,et al.  Determining the Relationship Between Census Data and Spatial Features Derived From High-Resolution Imagery in Accra, Ghana , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[6]  Francesca Bovolo,et al.  A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images , 2013, Proceedings of the IEEE.

[7]  Hannes Taubenböck,et al.  Slum mapping in polarimetric SAR data using spatial features , 2017 .

[8]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[9]  H. Taubenböck,et al.  The morphology of the Arrival City - A global categorization based on literature surveys and remotely sensed data , 2018 .

[10]  H. Taubenböck,et al.  The similar size of slums , 2018 .

[11]  M. Davis.  Planet of Slums , 2004 .

[12]  Monika Kuffer,et al.  Slums from Space - 15 Years of Slum Mapping Using Remote Sensing , 2016, Remote. Sens..

[13]  Franklin Obeng-Odoom,et al.  Arrival City: How the Largest Migration in History Is Reshaping Our World , 2013 .

[14]  K. Oštir,et al.  Object-Based Image Analysis of VHR Satellite Imagery for Population Estimation in Informal Settlement Kibera-Nairobi, Kenya , 2012 .

[15]  Stephen Karekezi,et al.  Energy access among the urban poor in Kenya , 2008 .

[16]  Xiao Xiang Zhu,et al.  Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[17]  B. Crow,et al.  Access to water in a Nairobi slum: women's work and institutional learning , 2010 .

[18]  Hannes Taubenböck,et al.  Unsupervised change detection in VHR remote sensing imagery - an object-based clustering approach in a dynamic urban environment , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Alfred Stein,et al.  An ontology of slums for image-based classification , 2012, Comput. Environ. Urban Syst..