Ongoing urbanization leads to steady growth of urban areas. In the case of highly dynamic change of municipalities, due to the rates of change, responsible administrations often are challenged or struggle with capturing present states of urban sites or accurately planning future urban development. An interest for urban planning lies on socio-economic conditions, as consumption and production of disposable goods are related to economic possibilities. Therefore, we developed an approach to generate relevant parameters for infrastructure planning by means of remote sensing and spatial analysis. In this study, the single building defines the spatial unit for the parameters. In the case city Belmopan (Belize), based on WorldView-1 data we manually define a city covering building dataset. Residential buildings are classified to eight building types which are locally adapted to Belmopan. A random forest (RF) classifier is trained with locally collected training data. Through household interviews focusing on household assets, income and educational level, a socio-economic point (SEP) scaling is defined, which correlates very well with the defined building typology. In order to assign socio-economic parameters to the single building, five socio-economic classes (SEC) are established based on SEP statistics for the building types. The RF building type classification resulted in high accuracies. Focusing on the three categories to describe residential socio-economic states allowed high correlations between the defined building and socio-economic points. Based on the SEP we projected a citywide residential socio-economic building classification to support supply and disposal infrastructure planning.

In the face of growing 21st-century urban challenges, this study emphasizes the role of remote sensing data in objectively defining urban structure types (USTs) based on morphology. While numerous UST delineation approaches exist, few are universally applicable due to data constraints or impractical class schemes. This article attempts to tackle this challenge by summarizing important approaches dealing with the computation of USTs and to condense their contributions to the field of research within a single comprehensive framework. Hereby, this framework not only serves as a conjunctive reference for currently existing implementations, but is also independent regarding the input data, spatial scale, or targeted purpose of the mapping. It consists of four major steps: (1) the collection of suitable data sources to describe the building morphology as a key input, (2) the definition of a spatial mapping unit, (3) the parameterization of the mapping units, and (4) the final classification of the mapping units into urban structure types. We outline how these tasks can lead to a UST classification which fits the users’ needs based on their available input data. At the same time, the framework can serve as a protocol for future studies where USTs are mapped, or new approaches are presented. This article closes with an application example for three different cities to underline the flexibility and applicability of the proposed framework while maintaining maximized objectivity and comparability. We recommend this framework as a guideline for the use-specific mapping of USTs and hope to contribute to past and future research on this topic by fostering the implementation of this concept for the spatial analysis and a better understanding of complex urban environments.

Rapidly urbanizing areas are challenged by a lack of information on urban growth in many parts of the earth. As the speed of building construction often exceeds traditional surveying methods, remote sensing can serve as a valuable input for the monitoring of urbanization processes. This study investigates changes in DaNang, Vietnam, between 2015 and 2017 identified based on photogrammetric analysis of surface elevations retrieved from Pléiades very high-resolution imagery. In contrast to traditional post-classification change detection approaches, we propose a time-efficient method solely based on digital surface model differencing to identify newly constructed buildings as well as demolitions. It is therefore easy to apply and suitable for the continuation of outdated base data available to local authorities. High importance is addressed to the vertical matching of both surface models to avoid misdetections. After differencing these surface elevations, thresholds based on field measurements are applied to identify areas of change. A total of 10,800 changes were detected between 2015 and 2017, of which 8,531 were to newly constructed buildings. The study proves that changes in rapidly urbanizing agglomerations can be reliably identified by a simple and transparent approach by using elevation changes and expert-based knowledge on floor numbers and building heights.

This study assesses the quantity and composition of household solid waste (HSW) in the City of Da Nang and proposes a transparent and standardised method for its assessment through a combination of very-high-resolution (VHR) satellite imagery, field surveys, questionnaires, and solid waste measurements on the ground. This was carried out in order to identify underutilised resources and to obtain discrete planning values at city level. The procedure proved to be a suitable method for reliable data gathering. To describe HSW generation, 818 valid datasets, subdivided into five building types, and their location were used. The average HSW generation rate was 297 g per capita per day. Within a total of 19 subcategories, organic waste had a share of 62.9%. The specific generation and composition of HSW correlates positively with both the building type and the spatial location within the city. The most HSW (509 g per capita per day), by far, was generated in the ‘villa-type’ building while in the ‘basic-type’ building, this was the least (167 g per capita per day). Taking into account the number of individual buildings, the total HSW generation in Da Nang in 2015 was estimated between 109,844 and 164,455 tonnes per year, which corresponds to about one-third to one-half of the total municipal solid waste.

This descriptor introduces a novel dataset, which contains the number and types of buildings in the city of Da Nang in Central Vietnam. The buildings were classified into nine distinct types and initially extracted from a satellite image of the year 2015. Secondly, changes were identified based on a visual interpretation of an image of the year 2017, so that new buildings, demolished buildings and building upgrades can be quantitatively analyzed. The data was aggregated by administrative wards and a hexagonal grid with a diameter of 250 m to protect personal rights and to avoid the misuse of a single building’s information. The dataset shows an increase of 19,391 buildings between October 2015 and August 2017, with a variety of interesting spatial patterns. The center of the city is mostly dominated by building changes and upgrades, while most of the new buildings were constructed within a distance of five to six kilometers from the city center.

Urban heat island effects are an increasing challenge for metropolitan areas, especially in regions where climate change increases the frequency of extreme temperatures. The consequences for human health are understood and must be addressed by urban planning. Although many studies currently exist regarding relationships of adaptation measures and urban heat amongst individual single cities, there is little general understanding on how urban development impacts temperature at large scales. In this study we analyze the correlation between urban heat and two indicators of urban morphology: the green spaces and the density of buildings. We hypothesize that the relation between building density, urban green space and urban surface temperature can be quantified openly available techniques of Earth observation. To grant an objective and consistent analysis over the entire country of Vietnam, we use information from different high-resolution imaging satellites. The computation of Pearson correlations across 58 Vietnamese districts confirms the hypothesis, indicating a negative impact of urban green space (-0.217), a positive impact of building density (+0.392) and an even stronger relationship with both indicators combined (+0.435). The findings are discussed with data of three research projects involving the cities of Hồ Chí Minh, Đà Nẵng, and Huế, and interpreted regarding regional differences and implications for urban planning.