Fuzzy inference guided cellular automata urban‐growth modelling using multi‐temporal satellite images

AlKheder, Sharaf; Wang, J.; Shan, Jie

doi:10.1080/13658810701617292

Cited by 67 publications

(43 citation statements)

References 23 publications

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“…Then, it was applied to two very different urban areas (i.e., San Francisco and Washington/Baltimore, both in the United States) and produced reasonable predictions as compared to other methods. In other work, it has been applied to long-term changes in land cover patterns classified from remote sensing data [18]. Unfortunately, the CA modeling approach only simulates the conversion of land use based on the characteristics of cells and their immediate spatial context.…”

Section: Cellular Automatamentioning

confidence: 99%

“…Research in urban modeling, even from a computer graphics perspective, must tie together the two areas of geometric and behavioral modeling in order to ensure that useful 3D modeling techniques are developed and are placed within their needed context. Their utility is to assist decision making of urban policies in current and future urban areas (e.g., [18,19]). …”

Section: State Of the Artmentioning

confidence: 99%

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3D Design and Modeling of Smart Cities from a Computer Graphics Perspective

Aliaga

2012

ISRN Computer Graphics

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.Modeling cities, and urban spaces in general, is a daring task for computer graphics, computer vision, and visualization. Understanding, describing, and modeling the geometry and behavior of cities are significant challenges that ultimately benefit urban planning and simulation, mapping and visualization, emergency response, and entertainment. In this paper, we have collected and organized research which addresses this multidisciplinary challenge. In particular, we divide research in modeling cities and urban spaces into the areas of geometrical modeling and of behavioral modeling. The first area overlaps significantly with computer graphics and computer vision-our focus is on algorithms that produce intricate geometry quickly from a compact set of specifications (i.e., procedural modeling). The second area of behavioral modeling centers on understanding the underlying socioeconomic, meteorological, and resource consumption/waste production processes occurring within an urban space. Research in urban modeling, even from a computer graphics perspective, must tie the two areas of geometric and behavioral modeling together in order to ensure that useful 3D modeling techniques are developed and are placed within their needed context. In addition, we discuss the growing trend of inverse procedural modeling and some sample urban applications.

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Section: Cellular Automatamentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

3D Design and Modeling of Smart Cities from a Computer Graphics Perspective

Aliaga

2012

ISRN Computer Graphics

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“…Also, it is considered as a powerful tool for modeling uncertainties associated with human cognition, thinking and perception (Gupta and Rao 1994). Fuzzy inference allows us to linguistically describe the concepts related to urban growth (Al-Kheder et al 2008) which flexibility is one of its advantages. The most important problem in fuzzy inference systems is finding proper membership function for each fuzzy variable which can be solved by training only.…”

Section: Anfismentioning

confidence: 99%

“…One of the most important disadvantages is that it is unable to deal with uncertainty. Uncertainty is an unavoidable part of spatial phenomena (Al-Kheder et al 2008) and needless to say its importance in so many concepts of this science like neighborhood, topology (relations between objects), buffer and other topics which clearly, 0 or 1 does not clarify and present them properly. In this condition, fuzzy logic has the ability to work with uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Urban sprawl assessment and modeling using landsat images and GIS

Mohammady

Delavar

2016

Model. Earth Syst. Environ.

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Nowadays, urban sprawl phenomenon have been seen in many of cities in the developing and developed countries. Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. Thus, monitoring, analyzing and modeling of this phenomenon seem to be unavoidable. This paper assess urban sprawl in Tehran Metropolis as the capital of Iran and models urban sprawl in this mega city utilizing artificial neural networks and adaptive neuro-based fuzzy inference system methods with remote sensing data and geospatial information systems spatial analyses and modeling capabilities to simulate Tehran urban growth. The results confirm that this city has experienced sprawl and sprawl has an increasing rate. Three Landsat imageries from TM and ETM? sensors taken in 1988, 1999 and 2010 and seven predictor variables include distance to road, distance to green space, slope, elevation, distance to fault, distance to developed area and number of urban cells in 3 by 3 neighborhoods have been used for urban sprawl assessment and modeling. Relative operating characteristics (ROC) and sensitivity analyses have been used to evaluate simulation results. In this research, two evaluation steps have been implemented using ROC and on the both of them, ANFIS presented the best performance vs two different proposed ANN structures.

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“…Many CA models have been developed using a diverse range of methods to define such variables and parameters; these methods include multi-criteria evaluation, logistic regression, principal component analysis, and partial least squares regression methods, to name a few. However, limitations of such methods in defining suitable transition rules, or the values of relevant parameters of the transition rules, or in constructing the architecture of the models have been identified and reported in the literature (Al-kheder, Wang and Shan 2008;Li and Yeh 2002a). As a result, there are significant differences between the simulation results and the actual patterns of urban growth, making such models less effective in simulating the actual process of urban growth (Li and Yeh 2002b;Liu and Phinn 2003).…”

Section: Introductionmentioning

confidence: 99%

An Optimised Cellular Automata Model Based on Adaptive Genetic Algorithm for Urban Growth Simulation

Feng

Liu

2012

Lecture Notes in Geoinformation and Cartography

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ABSTRACT:This paper presents an improved cellular automata (CA) model optimized using an adaptive genetic algorithm (AGA) to simulate the spatio-temporal process of urban growth. The AGA technique can be used to optimize the transition rules of the CA model defined through conventional methods such as logistic regression approach, resulting in higher simulation efficiency and improved results. Application of the AGA-CA model in Shanghai's Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics.

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Fuzzy inference guided cellular automata urban‐growth modelling using multi‐temporal satellite images

Cited by 67 publications

References 23 publications

3D Design and Modeling of Smart Cities from a Computer Graphics Perspective

3D Design and Modeling of Smart Cities from a Computer Graphics Perspective

Urban sprawl assessment and modeling using landsat images and GIS

An Optimised Cellular Automata Model Based on Adaptive Genetic Algorithm for Urban Growth Simulation

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