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Urbanization processes are manifested by the change in the physical morphology of cities. Gaining knowledge about cities through their morphology is naturally linked to the capability of remote sensing (RS) that can monitor city forms with a synoptic view. Yet, our knowledge of the urban form does not linearly increase with the increase of image data. Thus, the role, challenges and potentials of RS in deriving knowledge about urban morphology are worth investigating. We argue that ongoing efforts of mapping urban elements in RS are only marginally contributing to the understanding of cities in terms of urban morphology. We further reason that magnifying the role of RS depends on a suggested workflow involving steps that are external to RS, mainly including characterizing urban morphology through meaningful measurements of mapped elements, and interpreting the measured physical forms as proxies of the socioeconomic status. To exemplify the major steps, we focus on urban poverty (deprivation), and examine its manifestation through the morphology of buildings. Our findings show that challenges appear as soon as the collection of building information from RS images starts. This is mainly caused by inconsistent, incomplete and inaccurate GIS based representation of buildings on images, as well as low quality predictions, hidden from accuracy metrics. Although the potential of deriving meaningful urban morphological patterns from building maps for explaining socioeconomic patterns still holds, several uncertainties remain unsolved, such as the way urban processes are manifested morphologically and how the morphology is captured with the influence of building map quality. Our main conclusion is that as RS image-based morphological information propagates and fluctuates along the process of knowledge derivation, causing difficulties in quantifying the exact amount of urban knowledge derived. Nonetheless, useful knowledge could already be obtained even with suboptimal data sources and model performances, which opens the opportunity to facilitate transferable and reproducible urban morphology studies by using widely accessible data despite their suboptimal quality.

Many cities in low- and medium-income countries (LMICs) are facing rapid unplanned growth of built-up areas, while detailed information on these deprived urban areas (DUAs) is lacking. There exist visible differences in housing conditions and urban spaces, and these differences are linked to urban deprivation. However, the appropriate geospatial information for unravelling urban deprivation is typically not available for DUAs in LMICs, constituting an urgent knowledge gap. The objective of this study is to apply deep learning techniques and morphological analysis to identify degrees of deprivation in DUAs. To this end, we first generate a reference dataset of building footprints using a participatory community-based crowd-sourcing approach. Secondly, we adapt a deep learning model based on the U-Net architecture for the semantic segmentation of satellite imagery (WorldView 3) to generate building footprints. Lastly, we compute multi-level morphological features from building footprints for identifying the deprivation variation within DUAs. Our results show that deep learning techniques perform satisfactorily for predicting building footprints in DUAs, yielding an accuracy of F1 score = 0.84 and Jaccard Index = 0.73. The resulting building footprints (predicted buildings) are useful for the computation of morphology metrics at the grid cell level, as, in high-density areas, buildings cannot be detected individually but in clumps. Morphological features capture physical differences of deprivation within DUAs. Four indicators are used to define the morphology in DUAs, i.e., two related to building form (building size and inner irregularity) and two covering the form of open spaces (proximity and directionality). The degree of deprivation can be evaluated from the analysis of morphological features extracted from the predicted buildings, resulting in three categories: high, medium, and low deprivation. The outcome of this study contributes to the advancement of methods for producing up-to-date and disaggregated morphological spatial data on urban DUAs (often referred to as ‘slums’) which are essential for understanding the physical dimensions of deprivation, and hence planning targeted interventions accordingly.

This study aims to explore the travel behavior of bike-sharing users in Zhongshan, China. To this end, we use 5 months of trip data, which included origin and destination locations, and pickup and return time of each used bike in the system. To get a complete picture of the behavior, we distinguished between trips, trip chains, and transition activities. We categorized different trip chains and constructed transition matrices between activities. We found that almost all trips have different origin and destination stations. Two thirds of the trips are part of a trip chain consisting of multiple trips. Although users often use another station to start their next trip, a clear picture emerges in which public bikes are used as a single mode to hop from one destination to another, and at the end return more or less to the same location where the trip chain was started. Moreover, based on the trip chain matrices and transition matrices between activities, we conclude that users mainly used public bikes for commuting, and some of users went home during lunch break, while the system was also used or after-work shopping activities.

Urban growth may intensify local flooding problems. Understanding the spatially explicit flood consequences of possible future land cover patterns contributes to inform policy for mitigating these impacts. A cellular automata model has been coupled with the openLISEM integrated flood modeling tool to simulate scenarios of urban growth and their consequent flood; the urban growth model makes use of a continuous response variable (the percentage of built-up area) and a spatially explicit simulation of supply for urban development. The models were calibrated for Upper Lubigi (Kampala, Uganda), a sub-catchment that experienced rapid urban growth during 2004–2010; this data scarce environment was chosen in part to test the model's performance with data inputs that introduced important uncertainty. The cellular automata model was validated in Nalukolongo (Kampala, Uganda). The calibrated modeling ensemble was then used to simulate urban growth scenarios of Upper Lubigi for 2020. Two scenarios, trend conditions and a policy of strict protection of existing wetlands, were simulated. The results of simulated scenarios for Upper Lubigi show how a policy of only protecting wetlands is ineffective; further, a substantial increase of flood impacts, attributable to urban growth, should be expected by 2020. The coupled models are operational with regard to the simulation of dynamic feedbacks between flood and suitability for urban growth. The tool proved useful in generating meaningful scenarios of land cover change and comparing their policy drivers as flood mitigation measures in a data scarce environment.

Spatial metrics combined with spectral information extracted from very-high-resolution (VHR) imagery allow quantification of the general spatial characteristics of urban areas, as well as specific morphological features (i.e., density, size, and pattern) of unplanned settlements. Such morphological features are visible in VHR imagery, but they are challenging to quantify. Still, quantification of the morphological differences between planned and unplanned areas is an important step towards automatic extraction of unplanned areas from VHR imagery. In this work, we discuss how image segmentation assists in the extraction of homogenous urban patches (HUPs), and use spatial metrics to quantify the morphological differences between planned and unplanned HUPs. A set of spatial metrics meaningful to describe morphological features of unplanned areas is selected and combined into an unplanned settlement index (USI) using a multi-criteria evaluation approach. Two case study areas are used to test the USI, i.e., Dar es Salaam, Tanzania, and New Delhi, India. The ability of the developed USI to extract unplanned areas is confirmed via visual comparison with existing land use data, and a quantitative accuracy assessment shows that areas of high USI coincide well with unplanned areas in the reference data. The quantitative accuracy assessment presents an accuracy of greater than 70% for five selected test areas in both cities.

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.

Brownfields are real property, the expansion, redevelopment, or reuse of which may be complicated by the presence or potential presence of a hazardous substance, pollutant, or contaminant (USEPA, 2002). In recent years, there have been a rising number of brownfield redevelopment practices in Chinese cities. However, some redevelopment practices have been unsuccessful in spite of cautious planning whereas others have been successful in the absence of any planning. It is largely due to China’s rapid urbanization on one hand, and inadequate information on the locations and conditions of brownfield on the other. To address the gaps, an integrative methodology is devised based on two frameworks, one for identifying potential instead of actual brownfields, and one for establishing priorities for redevelopment. The first framework identifies potential brownfields through four steps: (1) define input sites; (2) verify environmental liability; (3) confirm tax delinquency; and (4) cross-check with industrial classification code. The second framework prioritize the identified potential brownfield sites with a set of criteria which are selected and weighed based on key interviews and the study of local reference cases. The utility of this methodology is exemplified with the case study of Futian in Shenzhen. Local data including land use data, tax and environmental records of 2005 and the development plan for 2006–2010 are utilized. We conclude that this methodology properly responds to the increasing need of urban planners in making proactive plans for brownfield redevelopment in Chinese cities.