Divyani Kohli

An ontology of slums for image - based classification

Abstract Information about rapidly changing slum areas may support the development of appropriate interventions by concerned authorities. Often, however, traditional data collection methods lack information on the spatial distribution of slum-dwellers. Remote sensing based methods could be used for a rapid inventory of the location and physical composition of slums. (Semi-)automatic detection of slums in image data is challenging, owing to the high variability in appearance and definitions across different contexts. This paper develops an ontological framework to conceptualize slums using input from 50 domain-experts covering 16 different countries. This generic slum ontology (GSO) comprises concepts identified at three levels that refer to the morphology of the built environment: the environs level, the settlement level and the object level. It serves as a comprehensive basis for image-based classification of slums, in particular, using object-oriented image analysis (OOA) techniques. This is demonstrated by with an example of local adaptation of GSO and OOA parameterization for a study area in Kisumu, Kenya. At the object level, building and road characteristics are major components of the ontology. At the settlement level, texture measures can be potentially used to represent the contrast between planned and unplanned settlements. At the environs level, factors which extend beyond the site itself are important indicators, e.g. hazards due to floods plains and marshy conditions. The GSO provides a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification. These characteristics may be different for other study areas, but show the applicability of the developed framework.

Transferability of object - oriented image analysis methods for slum identification

Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed.

Uncertainty analysis for image interpretations of urban slums

Abstract Image interpretations are used to identify slums in object-oriented image analysis (OOA). Such interpretations, however, contain uncertainties which may negatively impact the accuracy of classification. In this paper, we study the spatial uncertainties related to the delineations of slums as observed from very high resolution (VHR) images in the contexts of Ahmedabad (India), Nairobi (Kenya) and Cape Town (South Africa). Nineteen image interpretations and supplementary data were acquired for each context by means of semi-structured questionnaires. Slum areas agreed upon by different experts were determined. Uncertainty was modelled using random sets, and boundary variation was quantified using the bootstrapping method. Results show a highly significant difference between slum identification and delineation for the three contexts, whereas the level of experience in slum-related studies of experts is not significant. Factors of the built environment used by experts to distinguish slums from non-slum areas or leading to deviations in slum identification are discussed. We conclude that uncertainties in slum delineations from VHR images can be quantified successfully using modern spatial statistical methods.

Urban slum detection using texture and spatial metrics derived from satellite imagery

Abstract Slum detection from satellite imagery is challenging due to the variability in slum types and definitions. This research aimed at developing a method for slum detection based on the morphology of the built environment. The method consists of segmentation followed by hierarchical classification using object-oriented image analysis and integrating expert knowledge in the form of a local slum ontology. Results show that textural feature contrast derived from a grey-level co-occurrence matrix was useful for delineating segments of slum areas or parts thereof. Spatial metrics such as the size of segments and proportions of vegetation and built-up were used for slum detection. The percentage of agreement between the reference layer and slum classification was 60 percent. This is lower than the accuracy achieved for land cover classification (80.8 percent), due to large variations. We conclude that the method produces useful results and has potential for successful application in contexts with similar morphology.

Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification.

Application of the trajectory error matrix for assessing the temporal transferability of OBIA for slum detection

ABSTRACT High temporal and spatial-resolution imageries are a valuable data source for slum monitoring. However, the transferability of OBIA methods across space and time remains problematic, due to the complexity of the term “slum”. Hence, transparency is important when analysing the transferability of OBIA methods for slum mapping. Our research developed a framework for measuring the temporal transferability of OBIA methods employing the trajectory error matrix (TEM). We found relatively low trajectory accuracies indicating low temporal transferability of OBIA methods for slum monitoring using point-based assessment methods. However, the analysis of change needs to be combined with an analysis of the certainty of this change by considering the context of the change to deal with common problems such as variations of the viewing angles and uncertainties in producing reference data on slums.

Uncertainties in analyzing the transferability of the generic slum ontology

The Generic Slum Ontology (GSO) was developed to assist the detection of slums using Geographic Object-Based Image Analysis (GEOBIA). When applying the GSO locally, uncertainties exist in slum detection and transferability. Slums often have fuzzy boundaries and different ways to conceptualise. This study focuses on inherent uncertainties when analysing the transferability of the GSO across space, time and conceptualizations in the city of Jakarta, Indonesia. To measure the transferability of the GSO, we developed quantitative and qualitative indicators in multi-temporal Pleiades imagery (2012-2015) of two purposely-selected subsets. This framework allows assessing whether the developed ruleset is transferable across different spatial and temporal images. We applied two classification stages: background removal with a low scale parameter (SP) followed by slum extraction with a coarser SP. Both quantitative and qualitative indicators showed limited spatial and temporal transferability. Three sources of uncertainties can explain this result. First, the static concept of the employed ruleset and dynamic changes of slums. Real-world objects evolve over time, but their description remains static. Second, the gap between the real world (subjective conceptualization of objects) and image domain (quantitative values). For instance, the roof materials of slums (i.e. asbestos) have a similar spectral property with parking lot (from concrete), which resulted in misclassification. Third, the use of references data from local experts and municipal data introduce uncertainties that related to local ground knowledge and politics of slum declarations. Thus, this research contributes to the development of transferability measurements for the GSO and the understanding of underlying uncertainties.

Robustness of rule sets using VHR imagery to detect informal settlements - a case of Mumbai, India

Robust monitoring approaches for informal settlements using very high-resolution (VHR) satellite imagery can deliver essential information for supporting the formulation of pro-poor policies. Such information can complement census methods or participatory approaches. With the increasing availability of VHR satellite imagery, detection of the informal settlements benefits from the conceptualization of location-specific knowledge in the form of a locally-adapted generic slum ontology (GSO). In this study, we developed the local slum ontology for Mumbai, India, by incorporating local knowledge with image-based proxies. Then, we translated the local ontology into a rule set using Object Based Image Analysis (OBIA) to identify informal settlements by using spectral, spatial, geometric and texture measures. The method was applied to three subsets of a Worldview-2 imagery. The robustness of the initial rule set was analysed with the help of membership functions. The results showed that the normalized difference ratio of near infrared (NIR) and blue band and grey level co-occurrence matrix (GLCM) features are most effective in all three subsets in the identification of informal settlements. The results suggest that the rule sets developed in this study can potentially be applied to other study areas of Worldview-2 imagery for informal settlements identification.

The exposure of slums to high temperature: Morphology-based local scale thermal patterns

Heat exposure has become a global threat to human health and life with increasing temperatures and frequency of extreme heat events. Considering risk as a function of both heat vulnerability and hazard intensity, this study examines whether poor urban dwellers residing in slums are exposed to higher temperature, adding to their vulnerable demographic and health conditions. Instead of being restricted by sampling size of pixels or other land surface zones, this study follows the intrinsic latent patterns of the heat phenomenon to examine the association between small clusters of slums and heat patterns. Remotely sensed land surface temperature (LST) datasets of moderate resolution are employed to derive the morphological features of the temperature patterns in the city of Ahmedabad, India at the local scale. The optimal representations of temperature pattern morphology are learnt automatically from temporally adjacent images without manually choosing model hyper-parameters. The morphological features are then evaluated to identify the local scale temperature pattern at slum locations. Results show that in particular locations with slums are exposed to a locally high temperature. More specifically, larger slums tend to be exposed to a more intense locally high temperature compared to smaller slums. Due to the small size of slums in Ahmedabad, it is hard to conclude whether slums are impacting the locally high temperature, or slums are more likely to be located in poorly built places already with a locally high temperature. This study complements the missing dimension of hazard investigation to heat-related risk analysis of slums. The study developed a workflow of exploring the temperature patterns at the local scale and examination of heat exposure of slums. It extends the conventional city scale urban temperature analysis into local scales and introduces morphological measurements as new parameters to quantify temperature patterns at a more detailed level.

Mapping informal settlement indicators using object-oriented analysis in the Middle East

ABSTRACT Mapping informal settlements is crucial for resource and utility management and planning. In 2003, the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals (SDGs). Informal settlement indicators are used as a framework to carry out image analysis, and include vegetation extent, lacunarity of housing structures / vacant land, road segment type and materials, texture measures of built-up areas, roofing extent of built-up areas and dwelling size. Object-based image analysis (OBIA) methods are recommended to identify informal settlements. This paper documents the application of OBIA to map informal settlements, drawing on the ontology of Kohli et al. (2012) and the indicators of Owen and Wong (2013) for a Middle Eastern city. Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah, Saudi Arabia. Vegetation extent was the most successful indicator detected, with 100% producer accuracy and over 84% user accuracy, followed by the road network, with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies. Lacunarity of housing structures / vacant land was detected well in informal settlements. The texture measure indicator was detected using with low producer accuracy across all case studies. The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures. The dwellings size indicator generally failed to distinguish formal from informal settlements. Informal and formal were distinguished with an overall accuracy of 83%. This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities. However, a generic ruleset for mapping informal settlements remains elusive, and each indicator requires significant localised ‘tuning’.