Does rated visual landscape quality match visual features? An analysis for renewable energy landscapes

Spielhofer, Reto; Hunziker, Marcel; Kienast, Félix; Hayek, Ulrike Wissen; Grêt‐Regamey, Adrienne

doi:10.1016/j.landurbplan.2020.104000

Cited by 41 publications

(17 citation statements)

References 70 publications

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“…e study in [9] proposed that the Guidelines for Greenway Planning and Design were introduced at the national level, but confined to the differences in local characteristics, cities have introduced more detailed and more geographically distinctive standards for greenway landscape construction. For example, the study in [10] evaluated the current situation of greenways in Zengcheng, Guangdong, and divided the composition of greenways into six parts: slow walking system, greening system, service facility system, traffic interchange system, signage system, and lighting system. e study in [11] classified the greenway elements in Shenyang into three categories, namely, mountain elements, water elements, and urban park elements.…”

Section: Related Workmentioning

confidence: 99%

Visual Feature Evaluation of Shenyang Greenway Landscape Based on Deep Learning

Pan

Min

2022

Security and Communication Networks

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At present, the research on the visual characteristics of greenway landscape is mostly focused on the evaluation of beauty degree, landscape preference, and recreation preference and mainly on the overall macroscopic evaluation of a single greenway or typical spatial research in some areas, while less attention is paid to the overall characteristics of urban greenway landscape and the differences in visual characteristics of different types of greenway landscape. The emergence of new data and new technologies such as street view, Baidu map, and deep learning provides the possibility to study the visual characteristics of greenway landscapes in large scale and full sample. The study of greenway landscape based on large deep learning can effectively refine the overall pattern of visual characteristics of greenway landscape and reveal the differences of visual characteristics of different types of greenways and their key factors, so as to provide support for the planning and design of greenway landscape and landscape appearance enhancement.

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Section: Related Workmentioning

confidence: 99%

Visual Feature Evaluation of Shenyang Greenway Landscape Based on Deep Learning

Pan

Min

2022

Security and Communication Networks

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“…An integration of eye-tracking simulation, especially in the phase of feasibility studies, may support the evaluation of negative impacts on landscape perceptions in addition to studies on economic, social and ecological effects. Impacts may originate, for example, from new infrastructure such as wind turbines and photovoltaic panels to increase energy production from renewable resources [ 99 ]. It could be tested with eye-tracking simulation software, which colors or shapes of such infrastructure will be less visible (i.e., by reducing edges and color contrasts).…”

Section: Resultsmentioning

confidence: 99%

Potential of eye-tracking simulation software for analyzing landscape preferences

Schirpke

Tasser²,

Lavdas³

2022

PLoS ONE

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Profound knowledge about landscape preferences is of high importance to support decision-making, in particular, in the context of emerging socio-economic developments to foster a sustainable spatial development and the maintenance of attractive landscapes. Eye-tracking experiments are increasingly used to examine how respondents observe landscapes, but such studies are very time-consuming and costly. For the first time, this study explored the potential of using eye-tracking simulation software in a mountain landscape by (1) identifying the type of information that can be obtained through eye-tracking simulation and (2) examining how this information contributes to the explanation of landscape preferences. Based on 78 panoramic landscape photographs, representing major landscape types of the Central European Alps, this study collected 19 indicators describing the characteristics of the hotspots that were identified by the Visual Attention Software by 3M (3M-VAS). Indicators included quantitative and spatial information (e.g., number of hotspots, probabilities of initially viewing the hotspots) as well variables indicating natural and artificial features within the hotspots (e.g., clouds, lighting conditions, natural and anthropogenic features). In addition, we estimated 18 variables describing the photo content and calculated 12 landscape metrics to quantify spatial patterns. Our results indicate that on average 3.3 hotspots were identified per photograph, mostly containing single trees and tree trunks, buildings and horizon transitions. Using backward stepwise linear regression models, the hotspot indicators increased the model explanatory power by 24%. Thus, our findings indicate that the analysis of eye-tracking hotspots can support the identification of important elements and areas of a landscape, but it is limited in explaining preferences across different landscape types. Future research should therefore focus on specific landscape characteristics such as complexity, structure or visual appearance of specific elements to increase the depth of information obtained from eye-tracking simulation software.

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“…Betakova et al ( 2015 ) found that high-devices occupied landscapes are considered as lower-quality compared with low-occupied landscapes. Spielhofer et al ( 2021a ) confirmed that people are more physiologically aroused while noticing energy devices occupying landscapes compared to low-occupied landscapes where renewable energy installations are less obvious. Also, it was determined (Spielhofer et al, 2021b ) that the aesthetic background and aesthetic matching between energy devices and landscape represent the essential factors of human aesthetic tastes (and acceptance) for energy-scape elements.…”

Section: The Application Of Energy-scape Concept: Literature Reviewmentioning

confidence: 99%

“…Also, non-landscape elements play an important part for public acceptance. The perception and opinion about renewable energy (Saeporsdottir & Olafsdottir, 2020 ), political affiliation (Lintz & Leibenath, 2020 ), assessment of personal or societal costs due to new infrastructure and also psychological response to the landscape view (Spielhofer et al, 2021a ), and demographical characteristics of people (Westerlund, 2020 ) can have impact on the public acceptance for the development of new renewable infrastructure (Cohen et al, 2014 ; van der Horst, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Energy-space concept for the transition to a low-carbon energy society

Siksnelyte-Butkiene

Štreimikienė

Agnusdei

et al. 2022

Environ Dev Sustain

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Urban and regional development is indispensable from energy systems that function in a sustainable and resilient manner. The generation of renewable energy has seen significant upturn in the last decade. Renewables can be exploited to meet the global energy needs and climate change mitigation goals. Therefore, development of urban and regional energy systems must take into account the possibilities and challenges posed by the increasing penetration of the renewable. The achievement of goals of the energy system in the context of transition to low-carbon energy society rises numerous complex decisions over development of infrastructure and technologies. Although renewable energy devices generate clean energy, they also change landscape. The term of energy-scape encourages discussion about interactions between the energy system and the environment, and other ecosystem services at local level. The paper reviews previous studies analysing energy-scape elements and provides the insights towards the transition to low-carbon energy and low-carbon society in general. The systematic literature review allows clearly outlining the problem and gives reliable background for further studies. The main contribution of the article to the existing literature is the creation of framework for the analysis and assessment of new renewable energy technologies installation and infrastructure. The presented framework can be applied for all renewable energy technologies and infrastructure in both urban and rural areas and allows to attain a decision acceptable to the local community.

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Does rated visual landscape quality match visual features? An analysis for renewable energy landscapes

Cited by 41 publications

References 70 publications

Visual Feature Evaluation of Shenyang Greenway Landscape Based on Deep Learning

Visual Feature Evaluation of Shenyang Greenway Landscape Based on Deep Learning

Potential of eye-tracking simulation software for analyzing landscape preferences

Energy-space concept for the transition to a low-carbon energy society

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