Changes in the Building Stock of Da Nang between 2015 and 2017

Abstract

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.

Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities

“…In addition, the actual size of the building shape can be a valuable proxy for the complexity of built-up structures. As reported by Warth et al (2020), including the number of corners in the analysis of buildings in the city of Belmopan effectively increased the detection of dwellings of low, middle, and high socio-economic status [111]. But shape can be defined in many more ways, for instance by the perimeter of the building footprint and its relation to the size (perimeter-area-ratio [150]).…”

“…In addition to density measures, distances are a suitable measure for urban morphology, not only between the buildings themselves, but also to the closest facilities or infrastructures [83]. Warth et al (2020) showed that the distance to the administrative city center was among the variables providing the highest contribution to the prediction of socio-economic conditions within the city of Belmopan [111]. Similarly, Jiang et al (2021) used the distance to the nearest urban center and the distance to the nearest railway station as a measure of human well-being [35].…”

“…Also, Walde et al (2013) used the city center for the definition of a distinct class of urban structures [45]. Similarly, the distance to the historic city center helped to distinguish between urban and rural residential areas in the city of Da Nang, Vietnam [176].…”

Prediction of Socio-Economic Indicators for Urban Planning Using VHR Satellite Imagery and Spatial Analysis

“…Related to the building types, socio-economic projections can be assigned to the single buildings, as described in the previous section. To avoid the impression of precision of the 15-point scale (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18), and a source of errors in the prediction of socio-economy, socio-economic points are aggregated into socio-economic classes (SEC). Besides reducing errors in predicting socio-economic information, it can be critical to publish detailed sensitive information regarding resident's socio-economic status at a single building level.…”

“…To increase the spatial resolution of the information and to retrieve qualitative information on the change that occurred between different acquisitions, Warth et al proposed a method to retrieve information on dynamics in building stock on a single building scale by differencing urban digital surface models (DSM) [16]. Braun et al [17] refined these results publishing a refined urban change dataset on single building scale for Da Nang, VN.…”