Mapping Informal Settlements Using Machine Learning Techniques, Object-Based Image Analysis and local Knowledge

The existence of informal settlements in Riyadh City, the Kingdom of Saudi Arabia (KSA), has given rise to some urban planning issues. To provide improvements to mapping and planning processes, the current study aims to evaluate and characterize informal settlements within the city using object-based machine learning (ML) techniques (specifically, Random Forest (RF) and Support Vector Machine (SVM)), expert knowledge (EK) and satellite data. An examination of four defined locales has produced a comprehensive, local, informal settlement ontology. Four main categories (shape, geometry, texture, and pattern) were used to build the ontological framework. Expert local knowledge was employed to produce a ruleset to accurately identify and map these areas. Specific indicators identified by the specialists were used in a combined object-based ML and image analysis (OBIA) approach, with high-resolution worldview-3 imagery used as input data. Results demonstrated that combining EK and ML with remotely sensed data can efficiently, effectively and accurately distinguish informal settlement areas. This work has shown that an object-based ML technique (RF), in combination with EK about important local environment indicators, provides a useful method for mapping informal settlements

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

Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe identification and monitoring of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims at integrating OSM data and sentinel-2 imagery for classifying and monitoring the growth of informal settlements methods to map informal areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth in Kampala. Three building feature characteristics of size, shape and Distance to nearest Neighbour were derived and used to cluster and classify informal areas using Hotspot Cluster analysis and ML approach on OSM buildings data. The resultant informal regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal changes in informal areas using Convolutional Neural Networks (CNNs). Results from Optimized Hot Spot Analysis and Random Forest Classification show that Informal regions can be mapped based on building outline characteristics. An accuracy of 90.3% was achieved when an optimally trained CNN was executed on a test set of 2019 satellite image tiles. Predictions of informality from new datasets for the years 2016 and 2017 provided promising results on combining different open source geospatial datasets to identify, classify and monitor informal settlements

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

Monitoring sustainable urban development using builtup area indicators: a case study of Stellenbosch, South Africa

Abstract: Rapid urbanisation in many developing countries causes land transformation from agricultural, rural, and natural landscapes into urban areas. Data to monitor this transformation is often out of date, unreliable, not in standard format, cumbersome and expensive to collect or simply unavailable. This inhibits local authorities and other stakeholders’ capacity to monitor and leverage resources toward sustainable urban development. This paper investigates the use of earth observation (EO) data for supporting sustainable urban development planning. The study demonstrates that EO adds value to sustainable urban development by providing area-wide and up-to-date thematic and geometric characterisation of the urban built-up area, which would be difficult to obtain from other data sources. This helps local planning authorities to monitor urban growth and sustainability, facilitate evidence-based decision making and an array of other practical uses

Research Reports from the Programme for Belize Archaeological Project, Volume Six

Table of Contents : Background and Introduction to the 2011 Season of the Programme for Belize Archaeological Project / by Fred Valdez, Jr. and Marisol Cortes-Rincon (p.1-4) -- Investigations at Structure 3, La Milpa: The 2011 Field Season / by Debora Trein (p.5-32) -- Report of the 2011 Excavations at the South Ballcourt of La Milpa, Op A6 / by David Chatelain (p.33-44) -- Summary of the 2011 Activities of the La Milpa Core Project / by Brett A. Houk and Gregory Zaro (p.45-54) -- Summary of 2011 Field Season: Examination of Extended Lineages Associated with Courtyards 135 & 149 at La Milpa, Belize / by Brandon S. Lewis (p.55-58) -- Report on a Northern Residential Complex at La Milpa, Belize: Operation LM4 / by Deanna M. Riddick (p.59-62) -- Preliminary Notes on a Chultun Burial at La Milpa – LM-4 / by Stacy Drake (p.63-68) -- Excavations at Groups B and C, Say Kah, Belize, 2011 / by Sarah E. Jackson and Linda A. Brown (p.69-102) -- Hun Tun Archaeology: Report on the 2011 Field Season / by Robyn L. Dodge (p.103-114) -- Aguada Lagunita Elusiva (RB Lagunita), La Milpa East (RB LME) and Results of the 2011 Explorations along the LaMap East Transect Extension / by Estella Weiss-Krejci and Michael Brandl (p.115-126) -- Towards a Biography Of Place: The 2011 Season of Survey and Excavation at La Milpa North / by Eric J. Heller (p.127-144) -- Phase 2 Research at Wari Camp (RB-56): Summer 2011 / by Laura Levi (p.145-152) -- Summary Report of Investigations at the Site of Dos Hombres: Summer 2011 / by Rissa M. Trachman and Katherine MacDonald (p.153-158) -- Preliminary Report on the 2011 Activities of the Mount Allison University Archaeological Field School in Belize / by Grant R. Aylesworth (p.159-162) -- Tree Species Composition at Medicinal Trail Group A / by Nicholas Brokaw and Sheila Ward (p.163-164) -- Report on Some Stone Tools from RB 18, Northwest Belize: Guijarral and the Chispas Group / by David M. Hyde (p.165)Texas Archeological Research Laborator

An integrative approach using remote sensing and social analysis to identify different settlement types and the specific living conditions of its inhabitants

Someday in 2007, the world population reached a historical landmark: for the first time in human history, more than half of the world´s population was urban. A stagnation of this urbanization process is not in sight, so that by 2050, already 70 percent of humankind is projected to live in urban settlements. Over the last few decades, enormous migrations from rural hinterlands to steadily growing cities could be witnessed coming along with a dramatic growth of the world’s urban population. The speed and the scale of this growth, particularly in the so called less developed regions, are posing tremendous challenges to the countries concerned as well as to the world community. Within mega cities the strongest trends and the most extreme dimensions of the urbanization process can be observed. Their rapid growth results in uncontrolled processes of fragmentation which is often associated with pronounced poverty, social inequality, socio-spatial and political fragmentation, environmental degradation as well as population demands that outstrip environmental service capacity. For the majority of the mega cities a tremendous increase of informal structures and processes has to be observed. Consequentially informal settlements are growing, which represent those characteristic municipal areas being subject to particularly high population density, dynamics as well as marginalization. They have quickly become the most visible expression of urban poverty in developing world cities. Due to the extreme dynamics, the high complexity and huge spatial dimension of mega cities, urban administrations often only have an obsolete or not even existing data basis available to be at all informed about developments, trends and dimensions of urban growth and change. The knowledge about the living conditions of the residents is correspondingly very limited, incomplete and not up to date. Traditional methods such as statistical and regional analyses or fieldwork are no longer capable to capture such urban process. New data sources and monitoring methodologies are required in order to provide an up to date information basis as well as planning strate¬gies to enable sustainable developments and to simplify planning processes in complex urban structures. This research shall seize the described problem and aims to make a contribution to the requirements of monitoring fast developing mega cities. Against this background a methodology is developed to compensate the lack of socio-economic data and to deduce meaningful information on the living conditions of the inhabitants of mega cities. Neither social science methods alone nor the exclusive analysis of remote sensing data can solve the problem of the poor quality and outdated data base. Conventional social science methods cannot cope with the enormous developments and the tremendous growth as they are too labor-, as well as too time- and too cost-intensive. On the other hand, the physical discipline of remote sensing does not allow for direct conclusions on social parameters out of remote sensing images. The prime objective of this research is therefore the development of an integrative approach − bridging remote sensing and social analysis – in order to derive useful information about the living conditions in this specific case of the mega city Delhi and its inhabitants. Hence, this work is established in the overlapping range of the research topics remote sensing, urban areas and social science. Delhi, as India’s fast growing capital, meanwhile with almost 25 million residents the second largest city of the world, represents a prime example of a mega city. Since the second half of the 20th century, Delhi has been transformed from a modest town with mainly administrative and trade-related functions to a complex metropolis with a steep socio-economic gradient. The quality and amount of administrative and socio-economic data are poor and the knowledge about the circumstances of Delhi’s residents is correspondingly insufficient and outdated. Delhi represents therefore a perfectly suited study area for this research. In order to gather information about the living conditions within the different settlement types a methodology was developed and conducted to analyze the urban environment of the mega city Delhi. To identify different settlement types within the urban area, regarding the complex and heterogeneous appearance of the Delhi area, a semi-automated, object-oriented classification approach, based on segmentation derived image objects, was implemented. As the complete conceptual framework of this research, the classification methodology was developed based on a smaller representative training area at first and applied to larger test sites within Delhi afterwards. The object-oriented classification of VHR satellite imagery of the QuickBird sensor allowed for the identification of five different urban land cover classes within the municipal area of Delhi. In the focus of the image analysis is yet the identification of different settlement types and amongst these of informal settlements in particular. The results presented within this study demonstrate, that, based on density classes, the developed methodology is suitable to identify different settlement types and to detect informal settlements which are mega urban risk areas and thus potential residential zones of vulnerable population groups. The remote sensing derived land cover maps form the foundation for the integrative analysis concept and deliver there¬fore the general basis for the derivation of social attributes out of remote sensing data. For this purpose settlement characteristics (e.g., area of the settlement, average building size, and number of houses) are estimated from the classified QuickBird data and used to derive spatial information about the population distribution. In a next step, the derived information is combined with in-situ information on socio-economic conditions (e.g., family size, mean water consumption per capita/family) extracted from georeferenced questionnaires conducted during two field trips in Delhi. This combined data is used to characterize a given settlement type in terms of specific population and water related variables (e.g., population density, total water consumption). With this integrative methodology a catalogue can be compiled, comprising the living conditions of Delhi’s inhabitants living in specific settlement structures – and this in a quick, large-scaled, cost effective, by random or regularly repeatable way with a relatively small required data basis.The combined application of remotely sensed imagery and socio-economic data allows for the mapping, capturing and characterizing the socio-economic structures and dynamics within the mega city of Delhi, as well as it establishes a basis for the monitoring of the mega city of Delhi or certain areas within the city respectively by remote sensing. The opportunity to capture the condition of a mega city and to monitor its development in general enables the persons in charge to identify unbeneficial trends and to intervene accordingly from an urban planning perspective and to countersteer against a non-adequate supply of the inhabitants of different urban districts, primarily of those of informal settlements. This study is understood to be a first step to the development of methods which will help to identify and understand the different forms, actors and processes of urbanization in mega cities. It could support a more proactive and sustainable urban planning and land management – which in turn will increase the importance of urban remote sensing techniques. In this regard, the most obvious and direct beneficiaries are on the one hand the governmental agencies and urban planners and on the other hand, and which is possibly the most important goal, the inhabitants of the affected areas, whose living conditions can be monitored and improved as required. Only if the urban monitoring is quickly, inexpensively and easily available, it will be accepted and applied by the authorities, which in turn enables for the poorest to get the support they need. All in all, the listed benefits are very convincing and corroborate the combined use of remotely sensed and socio-economic data in mega city research