Leveraging machine learning to understand urban change with net construction

Ron-Ferguson, Nathan; Chin, Jae Teuk; Kwon, Youngsang

doi:10.1016/j.landurbplan.2021.104239

Cited by 12 publications

(2 citation statements)

References 55 publications

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“…The complex urban environment processes and challenges have been modeled using AI-based modeling frameworks [ 65 ]. The bulk of research attempts to do away with the Linear Regression model, since it is insufficient when dealing with a complex environmental issue [ 66 ]. The advantages of using AI-based modeling frameworks include the ability to model with several variables and the ability to identify the cluster influence of the variables [ 67 ].…”

Section: Methodsmentioning

confidence: 99%

Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique

et al. 2023

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Vegetation land fragmentation has had numerous negative repercussions on sustainable development around the world. Urban planners are currently avidly investigating vegetation land fragmentation due to its effects on sustainable development. The literature has identified a research gap in the development of Artificial Intelligence [AI]-based models to simulate vegetation land fragmentation in urban contexts with multiple affecting elements. As a result, the primary aim of this research is to create an AI-based simulation framework to simulate vegetation land fragmentation in metropolitan settings. The main objective is to use non-linear analysis to identify the factors that contribute to vegetation land fragmentation. The proposed methodology is applied for Western Province, Sri Lanka. Accessibility growth, initial vegetation large patch size, initial vegetation land fragmentation, initial built-up land fragmentation, initial vegetation shape irregularity, initial vegetation circularity, initial building density, and initial vegetation patch association are the main variables used to frame the model among the 20 variables related to patches, corridors, matrix and other. This study created a feed-forward Artificial Neural Network [ANN] using R statistical software to analyze non-linear interactions and their magnitudes. The study likewise utilized WEKA software to create a Decision Tree [DT] modeling framework to explain the effect of variables. According to the ANN olden algorithm, accessibility growth has the maximum importance level [44] between -50 and 50, while DT reveals accessibility growth as the root of the Level of Vegetation Land Fragmentation [LVLF]. Small, irregular, and dispersed vegetation patches are especially vulnerable to fragmentation. As a result, study contributes detech and managing vegetation land fragmentation patterns in urban environments, while opening up vegetation land fragmentation research topics to AI applications.

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Section: Methodsmentioning

confidence: 99%

Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique

et al. 2023

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“…Third, with the progression of wireless networks and intelligent terminal technology, machine-centered cities are meant to change into human-centered models. The advent of the mobile office era will help anyone control anything within their jurisdiction in real-time, anytime, and anywhere in the future [38][39][40]. Fourth, the digital system will no longer adhere to the general query, positioning, statistics, and analysis but will rely on IoT, sensor network, and other in-depth knowledge mining, support scientific decision-making and issue remote control commands to respond to the real world through the analysis and judgment of the digital world [41,42].…”

Section: The Main Content Of DC Construction In the Iot Environmentmentioning

confidence: 99%

Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives

Shang

Guizani

2022

Applied Sciences

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To promote the expansion and adoption of Digital Twins (DTs) in Smart Cities (SCs), a detailed review of the impact of DTs and digitalization on cities is made to assess the progression of cities and standardization of their management mode. Combined with the technical elements of DTs, the coupling effect of DTs technology and urban construction and the internal logic of DTs technology embedded in urban construction are discussed. Relevant literature covering the full range of DTs technologies and their applications is collected, evaluated, and collated, relevant studies are concatenated, and relevant accepted conclusions are summarized by modules. First, the historical process and construction content of a Digital City (DC) under modern demand are analyzed, and the main ideas of a DC design and construction are discussed in combination with the key technology of DTs. Then, the metaverse is the product of the combination of various technologies in different scenes. It is a key component to promote the integration of the real world and the digital world and can provide more advanced technical support in the construction of the DC. DTs urban technology architecture is composed of an infrastructure terminal information center terminal and application server end. Urban intelligent management is realized through physical urban data collection, transmission, processing, and digital urban visualization. The construction of DTs urban platform can improve the city’s perception and decision-making ability and bring a broader vision for future planning and progression. The interactive experience of the virtual world covered by the metaverse can effectively support and promote the integration of the virtual and real, and will also greatly promote the construction of SCs. In summary, this work is of important reference value for the overall development and practical adoption of DTs cities, which improves the overall operation efficiency and the governance level of cities.

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What drives urban redevelopment activity? Evidence from machine learning and econometric analysis in three American cities

Carruthers,

Wei

2024

J Geogr Syst

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Leveraging machine learning to understand urban change with net construction

Cited by 12 publications

References 55 publications

Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique

Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique

Impact of Digital Twins and Metaverse on Cities: History, Current Situation, and Application Perspectives

What drives urban redevelopment activity? Evidence from machine learning and econometric analysis in three American cities

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