An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran

Pijanowski, Bryan C.; Tayyebi, Amin

doi:10.1016/j.landurbplan.2010.10.007

Cited by 210 publications

(106 citation statements)

References 43 publications

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“…For LULC change simulation and land policy resolutions (Haase & Nuissl 2007;Jokar Arsanjani, Helbich & Vaz 2013), more accurate, higher resolution products are needed. As noted from recent land change modelling experiences, namely Tayyebi et al (2011) and Arsanjani et al (2014), while exploring the magnitude and type of land change such as expansion, shrinkage or intensification, the positional accuracy of temporal land cover maps cause major sensitivities across modelling approaches. Urbanization will see the population of many cities in the developing world grow (United Nations, Department of Economic and Social Affairs 2014), while shrinking cities in parts of the developed world are of particular economic concern, especially when changes are persistent and accompanied by an aging population (The Economist 2015b).…”

Section: Introductionmentioning

confidence: 99%

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Arsanjani

See

Tayyebi

2016

International Journal of Digital Earth

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Global land cover maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection and environmental planning. Due to the importance of land cover, there is a pressing need to increase the temporal and spatial resolution of global land cover maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, i.e. CORINE, Urban Atlas, OpenStreetMap and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large disagreements for certain classes are evident, in particular wetlands. However, overall, GlobeLand30 is shown to be a useful product for characterizing land cover in Germany, and paves the way for further regional and national validation efforts.

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Section: Introductionmentioning

confidence: 99%

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Arsanjani

See

Tayyebi

2016

International Journal of Digital Earth

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“…Artificial Neural network due to the possibility of learning, is an appropriate tool for environmental modelling. This method has been used in land use change modelling by (Pijanowski et al, 2002, Tayyebi et al, 2011, Li and Yeh, 2001, Weng, 2002, Pijanowski et al, 2009. These networks are composed of input layer, intermediate layers and an output layer.…”

Section: Methodsmentioning

confidence: 99%

Urban Growth Modeling Using an Artificial Neural Network a Case Study of Sanandaj City, Iran

Mohammady

Delavar

Pahlavani

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Land use activity is a major issue and challenge for town and country planners. Modelling and managing urban growth is a complex problem. Cities are now recognized as complex, non-linear and dynamic process systems. The design of a system that can handle these complexities is a challenging prospect. Local governments that implement urban growth models need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban uses within the boundary. There are so many negative implications related with the type of inappropriate urban development such as increased traffic and demand for mobility, reduced landscape attractively, land use fragmentation, loss of biodiversity and alterations of the hydrological cycle. The aim of this study is to use the Artificial Neural Network (ANN) to make a powerful tool for simulating urban growth patterns. Our study area is Sanandaj city located in the west of Iran. Landsat imageries acquired at 2000 and 2006 are used. Dataset were used include distance to principle roads, distance to residential areas, elevation, slope, distance to green spaces and distance to region centers. In this study an appropriate methodology for urban growth modelling using satellite remotely sensed data is presented and evaluated. Percent Correct Match (PCM) and Figure of Merit were used to evaluate ANN results.

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“…With the development of geographical information science, more geographical information techniques have been applied to construct the UGB delineation model [7,31,32]. Low-cost, high-precision urban boundary extraction models were developed using the geographical simulation method, remote sensing technologies, and land-use information entropy models [33,34].…”

Section: Conceptual Review Of Boundary Growth and Delineationmentioning

confidence: 99%

China’s New Urban Space Regulation Policies: A Study of Urban Development Boundary Delineations

Zhuang

Liu

et al. 2016

Sustainability

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China's rapid urbanisation has led to ecological deterioration and reduced the land available for agricultural production. The purpose of this study is to develop an urban development boundary delineation (UDBD) model using the high-tech manufacturing area of Xinbei in the District of Changzhou as a case study, and by applying remote sensing, GIS, and other technologies. China's UDBD policies are reviewed, spatiotemporal changes since 1985 are documented, and future expansion is modelled to 2020. The simulated urban-growth patterns are analysed in relation to China's policies for farmland preservation, ecological redlines protection areas, and housing developments. The UDBD model developed in this study satisfies regional farmland and ecological space protection constraints, while being consistent with urban development strategies. This study provides theoretical references and technological support for the implementation of land management policies that will optimize land allocations for urban growth, agriculture, and ecological protection.

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An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran

Cited by 210 publications

References 43 publications

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Urban Growth Modeling Using an Artificial Neural Network a Case Study of Sanandaj City, Iran

China’s New Urban Space Regulation Policies: A Study of Urban Development Boundary Delineations

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