The Role of Earth Observation in an Integrated Deprived Area Mapping "System" for Low-to-Middle Income Countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.

[1]  Jiong Wang,et al.  The role of spatial heterogeneity in detecting urban slums , 2019, Comput. Environ. Urban Syst..

[2]  C. Linard,et al.  Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs , 2019, Journal of Urban Health.

[3]  Bo Du,et al.  Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.

[4]  Andrew J. Tatem,et al.  Identifying residential neighbourhood types from settlement points in a machine learning approach , 2018, Comput. Environ. Urban Syst..

[5]  Stan Openshaw,et al.  Modifiable Areal Unit Problem , 2008, Encyclopedia of GIS.

[6]  David Rain,et al.  Connecting the Dots Between Health, Poverty and Place in Accra, Ghana , 2012, Annals of the Association of American Geographers. Association of American Geographers.

[7]  João Porto de Albuquerque,et al.  Towards a Participatory Methodology for Community Data Generation to Analyse Urban Health Inequalities: A Multi-Country Case Study , 2019, HICSS.

[8]  Paula Lucci,et al.  Are we underestimating urban poverty , 2018 .

[9]  Richard Sliuzas,et al.  The risk of impoverishment in urban development-induced displacement and resettlement in Ahmedabad , 2015 .

[10]  Helbert Arenas,et al.  Recognition of Urban Patterns Related to Leptospirosis Contamination Risks Using Object Based Classification of Aerial Photography. Test Areas: Informal Settlements of the Railroad Suburb of Salvador, Brazil. , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Yekaterina Chzhen,et al.  Sustainable Development Goal 1.2 , 2017 .

[12]  Durairaju Kumaran Raju,et al.  Predicting the distribution of informal camps established by the displaced after a catastrophic disaster, Port-au-Prince, Haiti , 2013 .

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

[14]  Sabine Vanhuysse,et al.  SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas , 2017 .

[15]  Martino Pesaresi,et al.  The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use , 2019, Earth System Science Data.

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

[17]  Andrew Crooks,et al.  A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities , 2018 .

[18]  O. Csillik,et al.  Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[19]  Sabine Vanhuysse,et al.  Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images , 2018, Remote. Sens..

[20]  David Rain,et al.  Defining neighborhood boundaries for urban health research in developing countries: a case study of Accra, Ghana , 2013, Journal of maps.

[21]  John Abbott,et al.  The use of GIS in informal settlement upgrading: its role and impact on the community and on local government , 2003 .

[22]  R. Prabhu,et al.  Urban Slum Detection Approaches from High-Resolution Satellite Data Using Statistical and Spectral Based Approaches , 2018, Journal of the Indian Society of Remote Sensing.

[23]  Monika Kuffer,et al.  The Spatial and Temporal Nature of Urban Objects , 2010 .

[24]  Rafig Azzam,et al.  Slums and informal housing in India: a critical look at official statistics with regard to water and sanitation , 2016 .

[25]  Roy Carr-Hill,et al.  Missing Millions and Measuring Development Progress , 2013 .

[26]  Jason Corburn,et al.  A Comparison of Social and Spatial Determinants of Health Between Formal and Informal Settlements in a Large Metropolitan Setting in Brazil , 2014, Journal of Urban Health.

[27]  Jiong Wang,et al.  Deprivation pockets through the lens of convolutional neural networks , 2019, Remote Sensing of Environment.

[28]  Jason Merchant,et al.  Some Definitions , 2019, Iterative Optimizers.

[29]  Anthony Stefanidis,et al.  Detecting and mapping slums using open data: a case study in Kenya , 2018, Int. J. Digit. Earth.

[30]  Gang Fu,et al.  Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network , 2017, Remote. Sens..

[31]  Forrest R. Stevens,et al.  GridSample: an R package to generate household survey primary sampling units (PSUs) from gridded population data , 2017, International Journal of Health Geographics.

[32]  Hartono,et al.  A Spatial Approach to Identify Slum Areas in East Wara Sub-Districts, South Sulawesi , 2017 .

[33]  Monika Kuffer,et al.  Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia , 2018, Remote. Sens..

[34]  Alfred Stein,et al.  Recurrent Multiresolution Convolutional Networks for VHR Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Monika Kuffer,et al.  The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator , 2018, ISPRS Int. J. Geo Inf..

[36]  Klaus Greve,et al.  Urban Development in West Africa - Monitoring and Intensity Analysis of Slum Growth in Lagos: Linking Pattern and Process , 2018, Remote. Sens..

[37]  Anthony Capon,et al.  Improving the health and welfare of people who live in slums , 2017, The Lancet.

[38]  Richard Sliuzas,et al.  GROWTH AND EVICTION OF INFORMAL SETTLEMENTS IN NAIROBI , 2016 .

[39]  Anthony Capon,et al.  The history, geography, and sociology of slums and the health problems of people who live in slums , 2017, The Lancet.

[40]  Krishna Mohan Buddhiraju,et al.  Textural segmentation of remotely sensed images using multiresolution analysis for slum area identification , 2019, European Journal of Remote Sensing.

[41]  Alfred Stein,et al.  Urban slum detection using texture and spatial metrics derived from satellite imagery , 2016 .

[42]  Pierre Alliez,et al.  Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Oleksandr Kit,et al.  Defining the Bull'S Eye: Satellite Imagery-Assisted Slum Population Assessment in Hyderabad, India , 2013 .

[44]  Catherine Linard,et al.  Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data , 2015, PloS one.

[45]  P. Agouris,et al.  The study of slums as social and physical constructs: challenges and emerging research opportunities , 2016 .

[46]  Anil M. Cheriyadat,et al.  Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[48]  S. Morse,et al.  Translation of Earth observation data into sustainable development indicators: An analytical framework , 2018, Sustainable Development.

[49]  Susheela Dahiya,et al.  Automated Extraction of Slum Built-up Areas from Multispectral Imageries , 2019, Journal of the Indian Society of Remote Sensing.

[50]  Monika Kuffer,et al.  Because space matters: conceptual framework to help distinguish slum from non-slum urban areas , 2019, BMJ Global Health.

[51]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Sabine Vanhuysse,et al.  An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification , 2017, Remote. Sens..

[53]  Jason Corburn,et al.  Why We Need Urban Health Equity Indicators: Integrating Science, Policy, and Community , 2012, PLoS medicine.

[54]  Jack Makau,et al.  The five-city enumeration: the role of participatory enumerations in developing community capacity and partnerships with government in Uganda , 2012 .

[55]  Monika Kuffer,et al.  Extraction of Slum Areas From VHR Imagery Using GLCM Variance , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[56]  Sulochana Shekhar Improving the Slum Planning Through Geospatial Decision Support System , 2014 .

[57]  K. Moffett,et al.  Remote Sens , 2015 .

[58]  D. Stow,et al.  Object‐based classification of residential land use within Accra, Ghana based on QuickBird satellite data , 2007, International journal of remote sensing.

[59]  Monika Kuffer,et al.  Understanding heterogeneity in metropolitan India: The added value of remote sensing data for analyzing sub-standard residential areas , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[60]  Monika Kuffer,et al.  A participatory approach to monitoring slum conditions : an example from Ethiopia , 2006 .

[61]  Patrick Hostert,et al.  Mapping the Slums of Dhaka from 2006 to 2010 , 2014 .

[62]  Xin Huang,et al.  Unsupervised Deep Feature Learning for Urban Village Detection from High-Resolution Remote Sensing Images , 2017 .

[63]  Trias Aditya,et al.  Channelling participation into useful representation: combining digital survey app and collaborative mapping for national slum-upgrading programme , 2020 .

[64]  Ryan Engstrom,et al.  Poverty from Space: Using High-Resolution Satellite Imagery for Estimating Economic Well-Being , 2017, The World Bank Economic Review.

[65]  George Vosselman,et al.  Context-Based Filtering of Noisy Labels for Automatic Basemap Updating From UAV Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[66]  Jonathan Hersh,et al.  Evaluating the relationship between spatial and spectral features derived from high spatial resolution satellite data and urban poverty in Colombo, Sri Lanka , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[67]  Alejandro Betancourt,et al.  Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery , 2017, Remote. Sens..

[68]  Alfred Stein,et al.  Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks , 2017, Remote. Sens..

[69]  Karin Pfeffer,et al.  Matching deprivation mapping to urban governance in three Indian mega-cities , 2009 .

[70]  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.

[71]  Ryan Engstrom,et al.  Evaluating the Relationship Between Contextual Features Derived from Very High Spatial Resolution Imagery and Urban Attributes: A Case Study in Sri Lanka , 2019, 2019 Joint Urban Remote Sensing Event (JURSE).

[72]  Catherine Linard,et al.  Critical Commentary: Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs , 2019 .

[73]  Simon Jones,et al.  Object-based random forest classification for informal settlements identification in the Middle East: Jeddah a case study , 2020, International Journal of Remote Sensing.

[74]  Monika Kuffer,et al.  The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery , 2014, Comput. Environ. Urban Syst..

[75]  Monika Kuffer,et al.  The Temporal Dynamics of Slums Employing a CNN-Based Change Detection Approach , 2019, Remote. Sens..

[76]  Krithi Ramamritham,et al.  Transfer learning approach to map urban slums using high and medium resolution satellite imagery , 2019, Habitat International.

[77]  Alfred Stein,et al.  Uncertainty analysis for image interpretations of urban slums , 2016, Comput. Environ. Urban Syst..

[78]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[79]  Sabine Vanhuysse,et al.  Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics , 2018, ISPRS Int. J. Geo Inf..

[80]  Peter M. A. Sloot,et al.  Spatial segregation, inequality, and opportunity bias in the slums of Bengaluru , 2017 .

[81]  Hannes Taubenböck,et al.  Investigation on the separability of slums by multi-aspect TerraSAR-X dual-co-polarized high resolution spotlight images based on the multi-scale evaluation of local distributions , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[82]  Monika Kuffer,et al.  Identifying a Slums' Degree of Deprivation from VHR Images Using Convolutional Neural Networks , 2019, Remote. Sens..

[83]  Kenneth Hill,et al.  Slum Residence and Child Health in Developing Countries , 2014, Demography.

[84]  Hannes Taubenböck,et al.  Spatial patterns of slums: Comparing African and Asian cities , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[85]  Jason Madan,et al.  A protocol for a multi-site, spatially-referenced household survey in slum settings: methods for access, sampling frame construction, sampling, and field data collection , 2019, BMC Medical Research Methodology.

[86]  A. Crooks,et al.  Measuring slum severity in Mumbai and Kolkata: A household-based approach , 2014 .

[87]  H. Elsey,et al.  Addressing Inequities in Urban Health: Do Decision-Makers Have the Data They Need? Report from the Urban Health Data Special Session at International Conference on Urban Health Dhaka 2015 , 2016, Journal of Urban Health.

[88]  Peijun Du,et al.  A review of supervised object-based land-cover image classification , 2017 .

[89]  Nazrul Islam,et al.  International Journal of Health Geographics Open Access the 2005 Census and Mapping of Slums in Bangladesh: Design, Select Results and Application , 2022 .

[90]  Qin Yu,et al.  Mapping slums using spatial features in Accra, Ghana , 2015, 2015 Joint Urban Remote Sensing Event (JURSE).

[91]  Monika Kuffer,et al.  Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai , 2017, Remote. Sens..

[92]  R. Sliuzas,et al.  The exposure of slums to high temperature: Morphology-based local scale thermal patterns. , 2019, The Science of the total environment.

[93]  Alfred Stein,et al.  Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images , 2017, IEEE Geoscience and Remote Sensing Letters.

[94]  Javier Martinez,et al.  An Exploration of Environmental Quality in the Context of Multiple Deprivations , 2018, GIS in Sustainable Urban Planning and Management.

[95]  Alfredo Pereira de Queiroz,et al.  Slum: Comparing municipal and census basemaps , 2019, Habitat International.

[96]  Warren C. Jochem,et al.  Spatially disaggregated population estimates in the absence of national population and housing census data , 2018, Proceedings of the National Academy of Sciences.

[97]  Monika Kuffer,et al.  Application of the trajectory error matrix for assessing the temporal transferability of OBIA for slum detection , 2018 .

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

[99]  Catherine Linard,et al.  Modelling the Wealth Index of Demographic and Health Surveys within Cities Using Very High-Resolution Remotely Sensed Information , 2019, Remote. Sens..

[100]  Patience Mudimu,et al.  Developing an informal settlement upgrading protocol in Zimbabwe – the Epworth story , 2012 .

[101]  Monika Kuffer,et al.  Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India , 2018, European Journal of Remote Sensing.

[102]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[103]  Monika Kuffer,et al.  Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia , 2017, Remote. Sens..

[104]  D. Heinrichs,et al.  Slums: perspectives on the definition, the appraisal and the management of an urban phenomenon , 2013 .

[105]  Phil Wood Confirmatory Factor Analysis for Applied Research , 2008 .

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