Abstract
Updated and reliable data on slums’ location, extent, morphology, and living conditions is critical to implementing and evaluating the effectiveness of slum improvement programs. Object-based image analysis (OBIA) allows extracting such information from imagery using shape, texture, density, and context to resemble human recognition of image objects. Although slums share similar characteristics, such as density, locations, and building orientations, they may differ locally, which requires adaptations of the OBIA ruleset; this reduces its transferability. Also, the most common approach in measuring transferability is using accuracy assessment by comparing the OBIA result with reference data created by domain experts using visual indicators. However, image interpreters have various local and professional experiences. Consequently, their choice of indicators to conceptualise slums and how they are delineating objects may lead to ambiguous results regarding mapped slum existence and extent. Our research aims to understand how respondents’ backgrounds impact slum conceptualisations and transferability measurement. We used three subsets in Jakarta, Indonesia, with different morphological characteristics and asked respondents with varied backgrounds to indicate slums and their characteristics. Our research concludes that different sources of uncertainties come from different understandings of slums, and the inability of rule-based OBIA to handle all uncertainties limits the ability to create a transferable OBIA ruleset for slum detections. We also conclude that the usage of accuracy assessment in measuring the performance of the image classification algorithm might be misleading when we are unaware of uncertainties in reference data.
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Notes
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Sourcecode of our applications can be downloaded from https://github.com/djhathie/slum.
References
Albrecht F (2010) Uncertainty in image interpretation as reference for accuracy assessment in object-based image analysis. In: Accuracy 2010 Symposium. Leicester, pp 13–16
Anders NS, Seijmonsbergen AC, Bouten W (2015) Rule set transferability for object-based feature extraction: an example for cirque map**. Photogramm Eng Remote Sens 81:507–514. https://doi.org/10.14358/PERS.81.6.507
Baatz M, Schäpe A (2000) Multiresolution segmentation: an optimisation approach for high quality multi-scale image segmentation. In: Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT-Symposium Salzburg 2000, Karlsruhe. Herbert Wichmann Verlag, pp 12–23
Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Centre on Housing Rights and Evictions (2008) Women, slums and urbanisation: examining the causes and consequences. COHRE Secretariat, Geneva
Department of Building and Settlements DKI (2014) Pemetaan Direktori Kumuh [directory of slum settlements]. Department of Building and Settlements DKI Jakarta. Unpublished Manuscript, Jakarta
Drǎguţ L, Tiede D, Levick SR (2010) ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int J Geogr Inf Sci 24:859–871. https://doi.org/10.1080/13658810903174803
Ebert A, Kerle N, Stein A (2009) Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data. Nat Hazards 48:275–294. https://doi.org/10.1007/s11069-008-9264-0
Falco E, Zambrano-Verratti J, Kleinhans R (2019) Web-based participatory map** in informal settlements: the slums of Caracas. Venezuela Habitat International 94:102038. https://doi.org/10.1016/j.habitatint.2019.102038
Firman T (2009) The continuity and change in mega-urbanisation in Indonesia: a survey of Jakarta-Bandung Region (JBR) development. Habitat Int 33:327–339. https://doi.org/10.1016/j.habitatint.2008.08.005
Hofmann P (2014) Defining robustness measures for OBIA framework. In: Weng Q (ed) Global urban monitoring and assessment through earth observation. CRC Press, Taylor & Francis, pp 303–324
Hofmann P, Strobl J, Blaschke T, Kux H (2008) Detecting informal settlements from Quickbird data in Rio de Janeiro using an object based approach. In: Object-based image analysis. Springer, pp 531–553
Jain S (2007) Use of IKONOS satellite data to identify informal settlements in Dehradun, India. Int J Remote Sens 28:3227–3233. https://doi.org/10.1080/01431160600705122
Kohli D, Sliuzas R, Kerle N, Stein A (2012a) An ontology of slums for image-based classification. Comput Environ Urban Syst 36:154–163. https://doi.org/10.1016/j.compenvurbsys.2011.11.001
Kohli D, Kerle N, Sliuzas R (2012b) Local ontologies for object-based slum identification and classification. In: Proceedings of the 4th GEOBIA. Rio de Janeiro, p 201
Kohli D, Warwadekar P, Kerle N, Sliuzas R, Stein A (2013) Transferability of object-oriented image analysis methods for slum identification. Remote Sens 5:4209–4228. https://doi.org/10.3390/rs5094209
Kohli D, Stein A, Sliuzas R (2016) Uncertainty analysis for image interpretations of urban slums. Comput Environ Urban Syst 60:37–49. https://doi.org/10.1016/j.compenvurbsys.2016.07.010
Kraff NJ, Wurm M, Taubenbock H (2020) Uncertainties of human perception in visual image interpretation in complex urban environments. IEEE J Sel Top Appl Earth Observ Remote Sens 13:4229–4241. https://doi.org/10.1109/JSTARS.2020.3011543
Kuffer M, Barros J, Sliuzas RV (2014) The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery. Comput Environ Urban Syst 48:138–152. https://doi.org/10.1016/j.compenvurbsys.2014.07.012
Kuffer M, Thomson DR, Boo G, Mahabir R, Grippa T, Vanhuysse S, Engstrom R, Ndugwa R, Makau J, Darin E, de Albuquerque JP, Kabaria C (2020) The role of earth observation in an integrated deprived area map** “system” for low-to-middle income countries. Remote Sens 12:982. https://doi.org/10.3390/rs12060982
Lee DS, Shan J, Bethel JS (2003) Class-guided building extraction from Ikonos imagery. Photogramm Eng Remote Sens 69:143–150. https://doi.org/10.14358/PERS.69.2.143
Mathenge CW (2011) Application of object oriented image analysis in slum identification and map**-the case of Kisumu, Kenya. Master Thesis, University of Twente
Ministry of Public Works and Public Housing (2016) City without slum (Kotaku) program guide. Ministry of Public Work and Public Housing, Jakarta
Molenaar M (2000) Three conceptual uncertainty levels for spatial objects. GIS fundamentals and spatial databases. In: ISPRS IC WG IV/III.1 - GIS fundamentals and spatial databases. ISPRS, Amsterdam, pp 670–677
Ooi GL, Phua KH (2007) Urbanisation and slum formation. J Urban Health 84:27–34. https://doi.org/10.1007/s11524-007-9167-5
Powers DMW (2020) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. https://doi.org/10.48550/ARXIV.2010.16061
Pratomo J (2016) Transferability of the generic and local ontology of slum in multi-temporal imagery, case study: Jakarta. PhD Thesis, University of Twente
Pratomo J, Kuffer M, Martinez J, Kohli D (2017) Coupling uncertainties with accuracy assessment in object-based slum detections, case study: Jakarta, Indonesia. Remote Sens 9:1164. https://doi.org/10.3390/rs9111164
Schiewe J, Gähler M (2008) Modelling uncertainty in high resolution remotely sensed scenes using a fuzzy logic approach. In: Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications, pp 755–768
Trimble Germany GmbH (2015) User Guide – eCognition ® Developer, 9.1.1. Trimble, Munich
UN Habitat (2018) Module 1: adequate housing and slum upgrading. In: SDG indicator 11.1.1 training module: adequate housing and slum upgrading, Nairobi
United Nations (2014) Report of the open working group of the general assembly on sustainable development goals. United Nations
Whiteside TG, Boggs GS, Maier SW (2011) Comparing object-based and pixel-based classifications for map** savannas. Int J Appl Earth Obs Geoinf 13:884–893. https://doi.org/10.1016/j.jag.2011.06.008
Yu Q, Gong P, Clinton N, Biging G, Kelly M, Schirokauer D (2006) Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm Eng Remote Sens 72:799–811. https://doi.org/10.14358/PERS.72.7.799
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Pratomo, J., Pfeffer, K., Kuffer, M. (2024). The Impact of Respondents’ Background Towards Slum Conceptualisations and Transferability Measurement of Remote Sensing–Based Slum Detections. Case Study: Jakarta, Indonesia. In: Kuffer, M., Georganos, S. (eds) Urban Inequalities from Space. Remote Sensing and Digital Image Processing, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-031-49183-2_8
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