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

Abstract

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.

Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications

“…The synergistic Integration of Google Earth Engine (GEE), remote sensing technology, and Geographic Information System (GIS) facilitates the rapid and precise mapping of land use and land cover, among other Earth surface features (Praticò et al, 2021;dos Santos et al, 2023). Geospatial researchers have employed a diverse array of LULC mapping techniques, ranging from conventional methodologies such as Bayesian Maximum Likelihood to advanced machine and deep learning models, including Support Vector Machine (SVM) (Braun et al, 2023), Light Gradient Boosting Machine, Random Forest (RF) (Magidi et al, 2021), and Decision Trees, (Gazzinelli et al, 2017). Furthermore, recent studies have also highlighted the effectiveness of Recurrent Neural Networks (RNN) in LULC analysis (Jeyavathana).…”

Assessing Peri-Urbanisation and Urban Transitions between 2010 and 2020 in Ho Chi Minh City using an Urban Structure Type Approach

“…An increasing swell of academic discourse and international agendas including the United Nations' Sustainable Development Goals (SDGs), the Sendai agreement on Disaster Risk Reduction, the New Urban Agenda of Habitat III, and the Paris Climate Change Agreement all recognize cities and their built environments as critical elements in the transition towards sustainability and resilience building [15,16]. While it is well understood that the process of urbanisation results in profound changes to the size, density, and heterogeneity of settlements and functional land uses, much less is known about the finer-scale variations in the urban form and their environmental and socioeconomic implications [17][18][19][20][21][22][23][24][25][26][27].…”

“…understood that the process of urbanisation results in profound changes to density, and heterogeneity of settlements and functional land uses, much less i about the finer-scale variations in the urban form and their environmen socioeconomic implications [17][18][19][20][21][22][23][24][25][26][27].…”