Spatiotemporal modelling of urban quality of life (UQoL) using satellite images and GIS

Kazemzadeh-Zow, Ali; Boloorani, Ali Darvishi; Samany‬, Najmeh Neysani; Toomanian, Ara; Ahmad, Pourahmad

doi:10.1080/01431161.2018.1447160

Cited by 19 publications

(12 citation statements)

References 31 publications

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“…In contrast, lowest levels of overall QoL was revealed in communities with poor access to public services and infrastructures, and to cultural and recreational facilities. This finding is in line with previous studies where access to health care centers, to educational facilities, to sport centers, and to cultural and leisure facilities are significant predictors of QoL [12,18,45,48]. Furthermore, the criterion of housing conditions revealed to be of great importance for a better overall QoL.…”

Section: Discussionsupporting

confidence: 91%

“…According to Murgaš and Klobučník [9] (p. 225) "when trying to evaluate a place, we examine its quality, which can be measured by means of a wide scope of demographic, social, economic, and environmental indicators as well as according to the amenities that can be found there". Several authors have developed methods to measure QoL and evaluate various geographic areas, throughout a range of factors and indicators [8,[10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Quality of Life Inequalities. A Geographical Approach

Faka

2020

IJGI

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This study proposes an integrated methodology for evaluating and mapping quality of life (QoL) and the quality of a place as residence area, at local level. The QoL assessment was based on the development of composite criteria, using geographical variables that evaluate QoL, and geographic information systems. The composite criteria are related to the natural and the socioeconomic environment, the housing conditions, the infrastructure and services, and the cultural and recreational facilities. Each criterion was evaluated by a set of variables and each variable was weighted based on the residents’ preferences and the analytical hierarchy process. The criteria were also weighted and combined to assess overall QoL. The methodology was implemented in the Municipality of Katerini, Greece, and QoL mapping led to the zoning of the study area and the identification of areas with low and high QoL. The results revealed the highest level of overall QoL in three out of twenty-nine communities, which provide better housing conditions and access to public services and infrastructures, combining also qualitative natural environment, whereas five mountainous and remote communities scored the lowest level. Mapping QoL may support decision making strategies that target to improve human well-being, increase QoL levels and upgrade living conditions.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Assessing Quality of Life Inequalities. A Geographical Approach

Faka

2020

IJGI

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“…On the contrary, the north and southeastern zones seem to have fewer public services and infrastructures, with poor access to medical services and sports facilities [23,69]. Those most distant from the center municipalities are also characterized by lower levels of QoL regarding the cultural and recreational facilities, whereas the central AMA seems to benefit due to the concentration of theaters, libraries, museums, and archaeological sites in the Athens municipality [23,63,69,75].…”

Section: Discussionmentioning

confidence: 99%

“…Many studies have reported the spatial dimension of QoL, underlying the importance of a "good place" as a place where people can experience a good life [7,18,19]. The spatial aspect of QoL has been examined in terms of (a) health-influencing factors [20][21][22][23][24][25][26], (b) house choice [27,28], (c) accessibility to transportation infrastructures [29,30], (d) availability of sustainable public space, (e) environmental conditions and climate change [31], and (e) urban inequalities [7,32].…”

Section: Introductionmentioning

confidence: 99%

Urban Quality of Life: Spatial Modeling and Indexing in Athens Metropolitan Area, Greece

Faka

Kalogeropoulos

Μαλούτας

et al. 2021

IJGI

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The purpose of this study is to assess and visualize the Quality of Life provided by urban space as a place of residence. The proposed methodology, after its theoretical documentation, is implemented in Athens Metropolitan Area, Greece. For the evaluation of Urban Quality of Life, a complex index is constructed by using multicriteria analysis. For this purpose, Quality of Life controlling factors such as built space, natural, socioeconomic, and cultural environment, infrastructure and services, and the quality of housing were analyzed within a GIS environment. The mapping of this index led to the identification of areas with different levels of Quality of Life. The results of the research can lead to more effective decision making regarding the planning of targeted actions and the distribution of financial resources to improve the Quality of Life of the residents in urban areas.

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“…With the recent advancement in remote sensing technologies, such as satellites, drones, and airborne vehicles, etc., high-resolution satellite images are easy to acquire [1]. This opens up new paradigms and research directions for the remote sensing community that offer different applications in diverse fields, for example, land cover segmentation [2][3][4], smart agriculture [5,6], traffic monitoring [7,8], disaster management [9], geo-localization [10], and urban planning [11,12]. Among these applications, land cover classification and segmentation is an important application that extracts useful information about the type of land covered by agriculture, water, forest, urban, etc., which is crucial for land resource managers.…”

Section: Introductionmentioning

confidence: 99%

Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

2021

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Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin.

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Spatiotemporal modelling of urban quality of life (UQoL) using satellite images and GIS

Cited by 19 publications

References 31 publications

Assessing Quality of Life Inequalities. A Geographical Approach

Assessing Quality of Life Inequalities. A Geographical Approach

Urban Quality of Life: Spatial Modeling and Indexing in Athens Metropolitan Area, Greece

Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images

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