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Given the observable increase in images of nature in advertising on a global scale, this study aims to ask whether such creative strategies should adapt imagery to the natural environment of the target audiences or could it be standardised globally.

Two samples of respondents living in different geographic locations with contrasted climates and natural environments were exposed to a set of experimental green advertisements visually featuring different categories of natural scenery and one urban landscape. Attitude towards each advertisement and emotional responses evoked by it were measured and compared across the range of stimuli and the two samples.

Results did not support either the hypothesis that individuals prefer advertisements showing the natural habitat in which they had grown up or the hypothesized universal preference for advertising imagery displaying savannah type landscapes. However, the observed preferences across both samples for advertisements featuring natural landscapes with abundant green vegetation and clear water are consistent with evolutionary psychology, which proposes that perception of beauty in a landscape is universal and hypothesizes preferences for landscapes with those characteristics.

The study was experimental in nature and based on only two specific geographic locations, as well as on one product and experimental brand. Findings and conclusions must therefore be adopted with due caution. Future research should be conducted on a broader geographic scale, across a wider range of natural and cultural environments, and with a greater variety of products and brands.

Results discourage the adaptation of nature imagery to the geographic location of a target audience, and support a global strategy based on the standardised use of landscapes universally perceived to be beautiful, following the principles of environmental aesthetics.

This is the first study to apply evolutionary and environmental psychology to an investigation of the influence of a target audience's natural environment on its behavioural responses to natural imagery in advertising.

Analyses of cultural landscapes need to combine natural and social-cultural components to promote discussions on landscape planning and heritage management. This qualitative research explores the integrated case study of ten municipalities in the “Vineyard Landscape of Piedmont: Langhe-Roero and Monferrato”, Italy, a UNESCO World Heritage cultural landscape. The research aims to raise awareness of its aesthetic-perceptive features, the importance of effective identification of visual impacts and to promote mitigation strategies/actions for updating the current Management Plan.

Two rounds of interviews and focus groups with mayors were performed in 2015 and 2020 to identify trends and drivers of change affecting the territories. Potential mitigation strategies and actions were voted on and selected in response to five critical themes that emerged from the survey, mainly related to real estate and its supplies.

The results suggest tools and policies in the fields of landscape architecture and landscape design that could benefit planning and management at different levels. They support the design of sustainable scenarios, improving mayors' understanding of the significance of cultural landscapes and promoting them as heritage managers. Furthermore, they intend to preserve the authenticity of the landscape by supporting its attributes for long-term conservation.

The research makes an original contribution on the visual implications of anthropogenic landscape transformations in ten municipalities constituting this serial property, six years after its UNESCO nomination (2014).

Effective interpretation of Australia’s unique landscapes has the potential to contribute to international tourists’ understanding of natural processes, as well as enhance their perceptions and appreciation of this environment. Research indicates that mystery, familiarity, and comprehension all influence how humans view landscapes and that preferences and perceptions are likely to be culturally determined. This chapter explores human perceptions of landscapes and highlights key differences in Western and Chinese approaches to experiencing and interpreting natural environments. It presents results of a study that used photo-elicitation to explore Chinese students’ perceptions of, and preferences for, six Australian landscapes. The chapter concludes with a discussion of the implications and recommendations for interpreting Australian landscapes for Chinese audiences.

Cultural landscapes are no more considered only as territories of cultural interest but also as integrated systems of cultural, social, and economic values. The adjustment of this consideration with the modern paradigms of collective governance and management necessitates investigations on challenges of management of cultural landscapes for valorizing their resources toward sustainable development. In this framework, the purpose of this paper is to analyze the state of participatory governance and management in cultural landscapes, focusing on the case of Takht-e Soleyman World Heritage Site (WHS) in Iran.

The paper presents the results of a study based on a theoretical analysis, accompanied by in-depth interviews with the key actors in the cultural heritage governance and management sectors, and large-scale surveys of the local population through the circulation of questionnaires.

This paper debates the reasons behind and the way forward to make governance and management approaches consistent with international theories and national policies. The analysis focuses on rural cultural landscapes and accordingly the Takht-e Soleyman WHS is deeply investigated.

The policies for participatory governance and management of rural cultural landscapes have been raised in the literature. However, more attention has to be paid to the strategies and mechanisms based on local features for their implementation. The study detailed in this paper makes a contribution to the debate on the design and implementation of participatory governance and management systems in this field by examining the actual extent of successful implementation of theoretical values and national policies in the case of Takht-e Soleyman WHS.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This paper investigates the adaptive capacity expressed by an agricultural region in response to changing conditions as a mean to address the future of cultural landscapes in a time of extreme uncertainty. Through the conceptual framework of biocultural diversity, the work assesses regimes shifts and persistence as a dialectic between natural and anthropogenic components of the landscape.

Specifically, the mechanisms of progressive landscape adaptation are explored by using an integrated method to align manifestations of biological and cultural diversity. Through a multidimensional approach applied to spatial analysis, the study determines a relationship between the footprint of past and modern landscape regimes and contemporary rural management practices in McLaren Vale, South Australia.

The biological and cultural diversity patterns along riparian corridors, or “biocultural corridors” are indicators of past and current adaptive capacity, which are able to convey novel trajectories of sustainable management in the landscape system.

Understanding the positive feedback loops between nature and human interactions as represented by their interrelationships in the landscape can inform planning decisions for sustainable agricultural development and enable effective regional long-term trajectories of resilience.

Historic districts are faced with inadequate features protection and landscape destruction in the process of accelerating urban construction, urban design focuses on how places function in addition to how they look, this theory is suitable for the protection of historical districts. At the present stage, the traditional urban design of historical districts has a series of problems, such as uncomprehensive research about characteristics, incomplete workflow and the lack of transformation route from design scheme to implementation, the main reason is that there is a mismatch between the setting goal and planning implementation. Urban design based on “Action planning” has the characteristics of comprehensive research contents, specific implementation plans and complete workflow, it arranges the workflow with four stages, including design preparation, design planning, design implementation and design evaluation, which is a new exploration and attempt to turn traditional blueprint-oriented into action-oriented. Urban design of North Shuncheng Lane Historic Districts in Xi'an city used action planning as a concept and established an framework including “identify development status and problems—construct action target and strategy—determine action projects and specific plans—improve and evaluate the results”, to implement design ideas into practice and to guide the protection of historic districts. Above all, action-oriented planning is considered the key to the development of historic districts.

Tourists’ destination image is crucial for visiting intentions. An ancient capital with diverse characteristics is an important component of China’s urban tourism. The purpose of this paper is to address the following questions: what are the differences and commonalities of the perceived destination image of ancient capitals? What makes the difference of the perceived destination image in these cities? Aside from the exterior factors, are there internal factors of cities that influence tourists’ cognition and perception of destination image?

The comment text data of Baidu tourism website were used to determine the differences in the destination images of China’s four great ancient capitals: Beijing, Xi’an, Nanjing and Luoyang. ROST content mining and semantic network analysis were for differences and commonalities of the perceived destination image, and correlation analysis was used to explore the internal factors of cities that influence tourists’ cognition and perception of destination image.

Though the same as ancient capital, the four ancient capitals’ images are far apart; historical interests are the core of tourism experience in ancient capital city; image perception is from physical carrier, history and culture, and human cognition; tourist’ destination affect of ancient capital is most from its history and culture; protecting identity and maintaining daily life are crucial for ancient city tourism.

Previous studies on ancient capitals have focused on the invariable identity of ancient capitals’ destination images, and left a gap on determining from where the invariable identity comes in general and how much it influences destination image. This gap was addressed in this study, by analyzing the destination images of four ancient capitals in China as cases. In this way, this study provided reference to the other ancient cities worldwide.