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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.

The primary objective of this paper is to explore how space organizations can incorporate children and imagination in their accountability-based accounting and decision-making processes.

This study centers on stakeholder engagement with children, specifically examining the drawing competition associated with the CHaracterising ExOPlanet Satellite (CHEOPS) space exploration mission. We employ a multidimensional research design consisting of both an interpretive approach to the 2,748 space-related drawings submitted by children across Europe to the CHEOPS drawing competition in 2015 and a content analysis of 46 media releases published by ESA and the University of Bern, the key partners of the CHEOPS mission.

Our analysis of the children’s drawings and the CHEOPS media releases indicates that the related organizations account for some of the children’s visions and imaginations, but shortcomings exist in addressing the ethical and space environmental concerns related to space exploration. We explore implications for the space accounting agenda by applying the critical dialogic accountability framework proposed by Dillard and Vinnari (2019), which allows for a discussion on an outline for action by incorporating intergenerational equity (Thomson et al., 2018) and moral imagination (Werhane, 1999).

This study offers a novel exploration of a largely overlooked yet crucial stakeholder group: children. By focusing on their unique perspectives and imaginative capabilities, the paper brings forth the voices of those who will inherit the future of space exploration. Employing children’s drawings as a medium of symbolic communication, this research study offers fresh insights into their perceptions, particularly relevant to space accounting. This innovative approach not only enriches the literature on stakeholder engagement and accountability but also provides space organizations with valuable guidance on fostering inclusivity and ensuring that the interests of future generations are considered in decision-making processes.

This study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.

The study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.

The study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.

Based on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.

The local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.

This research aims to contribute to History of Education Studies as well as to New Cold War Studies, by examining a Reactor Technology Specialist Engineer program, launched in Hungary three times in the 1980s for Cuban nuclear engineers, graduates of the University of Havana.

The institutional setting, the content of the program, the teaching staff, the students, and the outcomes are studied. The factors that motivated the birth of this special program are examined, including the following areas; in what ways it was different from the courses in which foreign students participated in Hungary; what its strengths and weaknesses were; how we can learn from this past experience and what relevance it has for the present.

The analysis – carried out within the context of Cuban–Hungarian relations in the Cold War – demonstrates that these two satellite countries used the fields of science and education to widen their international possibilities and at the same time to reinforce their national interests by cooperating with each other.

The investigation is based on archival sources, university yearbooks and journals as well as contemporary Hungarian press. Written sources were complemented by interviews with Cuban students and Hungarian teaching staff, thus providing a personal perspective, balancing official views.

A major obstacle regarding the measurement of an organization's sustainability and accountability in the space economy is defining the context and boundaries of commercial activity in outer space. Here, we introduce an ecosystem framework to address this obstacle. We utilize this framework to analyze the space mining sector. Our ecosystem framework sets the space mining sector's boundaries and helps a firm identify key stakeholders, activities, policies, norms and common pool resources in that sector and the interactions between them; a significant step in structuring how to measure space sustainability and accountability.

Borrowing theories and perspectives from a wide range of academic fields, this paper conducts a comprehensive context analysis of the space mining ecosystem.

Using our ecosystem framework to define the context and set boundaries for the space mining sector allowed us to identify sustainability-related issues in the sector and offer roadmaps to develop sustainability measures and standards.

To the best of the authors’ knowledge, this is one of the first papers to introduce a framework to define boundaries in the global space economy and provides a tool to understand, measure and evaluate the space mining sector's environmental, social and economic issues.

New Space activities offer benefits for human progress and life beyond the Earth. However, there is a risk that the New Space Economy may develop according to an anthropocentric mindset favouring human progress and survival at the expense of all other species and the environment. This mindset raises concerns over the social and environmental impacts of space activities and the accountability of space actors. This research article explores the accountability of space actors by presenting a pluralistic accountability framework to understand, inspire and change accountability in the New Space Economy. This study also identifies future research opportunities.

This paper is a reflective and normative essay. The arguments are developed using contemporary multidisciplinary academic literature, publicly available evidence and examples. Further, the authors use Dillard and Vinnari's accountability framework to examine a pluralistic accountability system for space businesses.

The New Space Economy requires public and private entities to embrace hybrid and pluralistic accountability for their social and environmental impacts. A new way of seeing the relationship between human life, the Earth and celestial space is needed. Accounting language is used to mirror and mobilise broader forms of responsibility in those involved in space.

This paper responds to the AAAJ's special issue call for examining how accountability can be ensured in the New Space Age. The space activities businesses conduct, and the anthropocentric view inspiring their race toward space is concerning. Hence, the authors advocate the need for rethinking accountability between humans and nature. The paper contributes to fostering the debate on social and environmental accounting and the accountability of space actors in the New Space Economy. To this end, the authors use a pluralistic accountability framework to help understand how the New Space Economy can face the risks emanating from its anthropocentric mindset.

The purpose of the study is to analyze changes in land use, specifically residential area expansion, in South Tangerang City and identify the factors that influence land use change.

The study used remote sensing methods in ArcGIS 10.8 for data analysis and processing, including spatial analysis and identification of land use changes. The study analyzed satellite images from 2010 and 2020 to identify changes in land use in South Tangerang City over the ten-year period.

The study found that the most significant land use changes in South Tangerang City between 2010 and 2020 were the reduction of mixed plantation area and the expansion of residential areas. The study identified the development of small townships by private developers as the main factor that influenced land use change in South Tangerang City.

The study has several limitations, including a focus on only one aspect of land use change (i.e. residential area expansion), limited scope of the study area (South Tangerang City) and a reliance on remote sensing methods for data analysis.

The findings of the study can be used by policymakers and city planners to develop sustainable land use planning strategies that balance the need for urban development with environmental and social concerns. By understanding the factors that drive land use changes in South Tangerang City, policymakers can develop policies that encourage sustainable urban growth and development while preserving natural resources and protecting the environment.

The study has social implications as the expansion of residential areas in South Tangerang City indicates a growing demand for housing in the area. The study highlights the importance of developing affordable and sustainable housing solutions to meet the needs of the growing population in South Tangerang City. Additionally, the study emphasizes the importance of understanding the social and economic factors that drive land use change and their implications for the well-being of local communities.

The residential area development in South Tangerang City is driven by private developers who make small independent cities that have all facilities in one area. These small cities attract people to reside and also drive high population growth in South Tangerang City, considering it is a buffer city of Jakarta that has good infrastructure development.

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

The purpose of the present study is to explore the factors that influence the residential location choice of households in the Indian context.

Two sub-urban towns of Kolkata, i.e. Biddhanagar Municipal Corporation and Rajarhat Newtown, are taken as case studies. In total, 34 variables are considered related to the household's socioeconomic background (10), dwelling (3), travel (2) and level of importance of neighborhood characteristics (19). A revealed preference household survey is conducted, which resulted in the collection of 390 samples. Firstly, 19 level of importance variables are deduced to 3 latent factors using exploratory and confirmatory factor analysis. After that, a utility function is estimated using a multinomial logit regression to identify significant factors.

The results indicate the significance of car ownership, work travel time, work mode, monthly income, proximity to work, proximity to family/relative/friends, proximity to quality schools, quality of physical infrastructure such as water availability, power availability and housing typology among others in the residential location choice of households.

Kolkata, a major metropolitan area in eastern India, has been experiencing space constraints to cater to the housing demand of the ever-increasing population. Although satellite towns are being developed, the infrastructure landscape in such towns is uneven, resulting in more housing demand in one area than another. The study furnishes significant findings to develop more informed policies to attract households in low-demand areas and manage high-demand areas to achieve sustainable planning.

Considering the importance of geographical context, it is the first study on residential location choice, providing a perspective of household behavior from eastern India. It also shows the importance of physical infrastructure attributes in residential location choice for developing countries, which is seldom employed in the current practice. Moreover, previous studies (although few) done in India consider the objective value of location characteristics. The present study focuses on subjective value or the level of importance perceived by respondents.

The purpose of this paper is to explore the application of smart systems and emerging technologies for disaster risk management (DRM) in Nepal. This delves into specific technologies, including advanced connection and communication technologies, AI, big data analytics, autonomous vehicles and advanced robotics, examining their capabilities and potential contributions to DRM. Further, it discusses the possibility of implementing these technologies in Nepal, considering the existing policies and regulations, as well as the challenges that need to be addressed for successful integration.

For this review journal series of search strategy for identifying relevant journals, the initial examination of results, a manual assessment, geographical refinement, establishment of criteria for the final selection, quality assessment and data management, along with a discussion of limitations. Before delving into the relevant literature within the field of research interest, the authors identified guiding keywords. Further, the authors refined the list by filtering for articles specifically related to Nepal, resulting in a final selection. The final selection of these 95 articles was based on their direct relevance to the research topics and their specific connection in the context of Nepal.

The way technology is used to reduce disaster risk has changed significantly in Nepal over the past few years. Every catastrophe has given us a chance to shift to something innovative. The use of new emerging technologies such as artificial intelligence (AI), big data analytics, autonomous vehicles, advanced robotics and advanced connection and communication technologies are increasing for the purpose of generating risk knowledge, reducing disaster risk and saving the loss of lives and properties. The authors conclude that the successful implementation of smart systems and emerging technologies for disaster risk management in Nepal has the potential to significantly improve the country's resilience and minimize the impact of future disasters. By leveraging data-driven decision-making, enhanced connectivity and automation, Nepal can build a more proactive, adaptive and efficient disaster management ecosystem.

Studies on the application of smart systems in Nepal are limited and scattered across different database. This work collects together such literatures to understand the current status of the application of the smart system and technologies and highlights the challenges and way forward for effective disaster risk management in Nepal. Therefore, this work is an original one and adds value to the existing literatures.