Smartphone-based volunteered geographic information (VGI) for slum mapping in Pokhara City of Nepal

Informal settlements in urban areas are increasing rapidly throughout the world and regularisation of these settlements is being one of the challenging issues. Various study results have shown that conventional cadastral based information system approach and government managed institutional arrangements do not appropriately address land management issues of slum settlements. The aim of this study is to explore application of smartphone based Volunteered Geographic Information (VGI) and open spatial tools for slum mapping in developing countries such as in Nepal. A case of Pokhara Metropolitan city has been considered to explore the potential of utilization of smartphone based VGI and open spatial tools for slum mapping. Attribute and spatial data were collected using Smartphones and community-driven approach. Spatial and attribute data collected from 229 respondents of household’s surveys are integrated, analysed and interpreted and presented in this paper. Open Street Map (OSM) platforms and QGIS open source software have been used for slum mapping. These maps could play an important role in providing spatial information to the local government and planning authority in Nepal. This research paper concludes that smartphone based VGI and open portals such OSM have great potential to contribute to develop slum database and in providing information to plan various strategies, which aims at understanding, regularisation and upgrading slums

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

Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesUpdated spatial information on the dynamics of slums can be helpful to measure and evaluate the progress of urban upgrading projects and policies. Earlier studies have shown that remote sensing techniques, with the help of very-high resolution imagery, can play a significant role in detecting slums, and providing timely spatial information. The main objective of this thesis is to develop a reliable object-oriented slum identification technique that enables the provision of timely spatial information about slum settlements in Addis Ababa city. It compares the one-class support vector machines algorithm with the expert defined classification rule set in the discrimination of slums, using GeoEye-1 imagery. Two different approaches, called manual and automatic fine-tuning, were deployed to determine the best value of parameters in one-class support vector machines algorithm. The manual fine-tuning of the parameters is done using extensive manual trial. The automatic tuning is done using cross-validation grid search with the overall accuracy as the performance metric. Two regions of study were defined with different landscape compositions, providing different classification scenarios to compare the classification approaches. After image segmentation, twenty predictive variables were computed to characterize the objects in both study areas. An image analyst collected one hundred sample objects of a slum to be used as training for the single-class learner. In parallel, an image analyst has defined a hierarchical rule set to discriminate the class of interest. Results in both study areas indicate that the one-class support vector machine with manual tuning yields higher overall accuracy (97.7% in subset 1, and 92% in subset 2) and requiring much less application effort and computing time than the expert system

Slum health mapping as catalyst for a collaborative agenda for research, practice, local citizens and volunteers

Background and purpose. Following the paradigmatic examples of the use of OSM for crisis mapping, there have been sustained efforts to use OSM for mapping preventively vulnerable communities in the global South. This includes, for instance, participatory mapping in the slums of sub-Saharan Africa (Hagen, 2017) and the Missing Maps project. Researchers have also started to study these mapping activities (e.g. Albuquerque et al. 2016; Herfort et al. 2017). However, a collaborative agenda in this area is missing that is able to reflect views and needs of researchers, OSM volunteers, humanitarian organisation practitioners and local communities. Methodology and Findings. After a brief review of existing methods used for mapping disadvantaged communities and slums, we introduce the approach and report on preliminary results from an ongoing large-scale project (NIHR Global Health Unit on Improving Health in Slums), which uses OSM for mapping slums in five cities: Dhaka (Bangladesh), Karachi (Pakistan), Nairobi (Kenya), Ibadan and Lagos (Nigeria). Our methods are based on the combination of satellite imagery digitisation with ground-truthing and participatory mapping. The maps produced will result in enhanced information regarding environmental features of the slums and the location of healthcare facilities, which will also be used as a basis for the health-science surveys of the project. In this manner, our approach is aimed at achieving a threefold goal: (a) participation and inclusion of local stakeholders as a strategy to build resilience; (b) worldwide collaboration, connecting to the global Humanitarian OSM network and student mapping societies; (c) quality evaluation mechanisms for generating high-quality data that can also be used for scientific research. Final discussion/Impact. We would like to discuss the approach and results of our project as a basis to invite OSM researchers, practitioners and volunteers to join us in defining a collaborative agenda towards improving methods and practice for mapping vulnerable communities in OSM. This should include challenges from an interdisciplinary perspective that account for technical, methodological, social and ethical issues. As a result, we would like to contribute to the emergence of an OSM research agenda that goes beyond solely using OSM geographic data for research, but also includes ways of engaging the OSM community and local communities in the research process

Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Slums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796

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

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

The challenges of making Indian cities slum-free (Part 1)

Amitabh Kundu argues that the vision of slum-free Indian cities is hampered by big city bias and the failure to prescribe a framework for identifying non-tenable slums and legitimate slum households that must not be evicted