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This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

This paper aimed to develop a participatory methodology to analyze the disaster risk creation in coastal cities, based on an approach that combines social, urban, environmental and disaster risk elements.

The methodology uses some aspects of three theoretical approaches in a complementary way: i) the Pressure and Release (PAR) framework for the identification of dynamic pressures that contribute to disaster risk creation; ii) the application of Drivers, Pressure, State, Impact, Response (DPSIR) framework to analyze environmental dimensions; and iii) urban analysis, applying the Strengths, Weaknesses, Opportunities and Threats (SWOT) tool to classify urban processes. The methodology combined the use of satellite remote sensing data to analyze the urban sprawl and citizen science methods to collect social and environmental data, using the case study of the watershed of the Juqueriquerê River in the coastal city of Caraguatatuba, Brazil. The pilot project was part of a local university extension project of the undergraduate course on Architecture and Urban Planning and also engaged residents and city hall representatives.

The satellite remote sense data analysis indicated a continuous urban sprawl between 1985 and 2020, especially in the south of the Juqueriquerê watershed, reducing urban drainage and increasing the extension and water depth of urban flooding and riverine floods. Using citizen science methods, undergraduates identified settlements with limited economic resources to elevate houses and a lack of infrastructure to promote a resilient coastal city. After identifying the dynamic pressures that contribute to disaster risk creation and the weaknesses and strengths of a resilient city, undergraduate students proposed urban planning interventions and gray and green infrastructure projects to mitigate disaster risks.

The paper identifies urban sprawl in disaster-prone areas as one of the risk factors contributing to disaster. It also comprehensively analyzes differences between different zones in the Juqueriqere River, which will be useful for policy-making.

The method presented an interdisciplinary approach that used satellite remote sensing data and citizen science techniques to analyze disaster risks in coastal cities. The multidimensional approach used to evaluate risks is useful and can be replicated in other similar studies to gain a more comprehensive understanding of disaster risks.

This study looked at the potential applications of geographic information systems (GIS) and remote sensing (RS) for inclusive community development and participation, sustainable tourism, and rural community-based natural resource management (CBNRM) in sub-Saharan Africa and other rural areas worldwide.

To evaluate resource management systems for rural tourism and the environment in Africa and abroad. The study makes use of reviews of relevant literature and documents, and while linking applications for sustainable tourism and local community empowerment with CBNRM and GIS, vital content was manually analyzed.

The study shows a potential affinity between agricultural and tourism businesses that GIS in line with the CBNRM conception can strengthen. In many rural and underdeveloped regions of the continent, this highlights the need for a credible and varied tourism strategy to develop and empower the relevant communities.

Most agricultural communities in Africa are located in low-income regions. Such areas are rich in natural wildlife and have popular tourist destinations. A mix of regional community development initiatives can be built using GIS, sustainable tourism, CBNRM, and community-based tourism (CBT).

This study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.

This study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.

The performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.

The cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.

This study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.

Some similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

This paper aims to assess the current and future dynamics of land cover transitions and analyze the vegetation conditions in Abuja city since its establishment as the capital of Nigeria in 1991.

A random forest classifier embedded in the Google Earth Engine platform was used to classify Landsat imagery for the years 1990, 2001, 2014 and 2020. A post-classification comparison was used to detect the dynamics of land cover transitions. A hybrid simulation model that comprised cellular automata and Markovian was used to model the probable scenario of land cover changes for 2050. The trend of Normalized Difference Vegetation Index was examined using Mann–Kendall and Theil Sen’s from 2014 to 2022. Nighttime band data from the National Oceanic and Atmospheric Administration were obtained to analyze the trend of urbanization from 2014 to 2022.

The findings show that built-up areas increased by 40%, while vegetation, bare land and agricultural land decreased by 27%, 7% and 8%, respectively. Vegetation had the highest declining rate at 3.15% per annum. Built-up areas are expected to increase by 17.1% between 2020 and 2050 in contrast with other land cover. The proportion of areas with moderate vegetation improvement is estimated to be 15.10%, while the proportion of areas with no significant change was 38.10%. The overall proportion of degraded areas stands at 46.8% due to urbanization.

The findings provide a comprehensive insight into the dynamics of land cover transitions and vegetation variability induced by rapid urbanization in Abuja city, Nigeria. In addition, the findings provide valuable insights for policymakers and urban planners to develop a sustainable land use policy that promotes inclusivity, safety and resilience.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

This paper aims to examine the convergence and divergence of business and production practices in the global wine industry, particularly focusing on Pinot Noir producers in Burgundy, New Zealand and South Africa (SA). This study explores the interplay between firm-specific factors and regional contexts to identify competitive advantage drivers among Pinot Noir producers.

This research uses a comparative analysis approach, using data from a comprehensive winery level survey. This study applies methodologies akin to value chain analysis to unravel the configuration of productive and technology/knowledge creation activities within wineries across three regions.

This analysis reveals both convergence and divergence in business and production practices among Pinot Noir producers in Burgundy, New Zealand and South Africa. Although there is a degree of convergence in marketing, distribution and competition strategies, differences exist in production practices and firms’ capabilities. Burgundy emphasizes tradition and terroir expression, contrasting with the modernization and innovation focus observed in New Zealand and South Africa. However, all regions share a commitment to quality as a competitive advantage.

This study acknowledges limitations such as the focus on a specific grape variety and regions, the absence of performance impact analysis and the need for additional variables like environmental, institutional and cultural factors and consumer preferences to provide a comprehensive understanding of industry dynamics.

The insights from this study offer practical implications for winemakers, industry stakeholders and policymakers. Producers can optimize production and marketing strategies based on regional contexts and market segments, whereas stakeholders can identify emerging trends and opportunities in the global wine market. Policymakers can develop targeted policies supporting innovation, sustainability and competitiveness.

This paper provides a unique contribution by conducting a comparative firm-level analysis across distinct wine-producing regions, shedding light on the nuanced interplay of factors shaping competitive advantage among Pinot Noir producers. This study’s comprehensive data set and methodological approach enhance understanding and offer valuable insights for industry stakeholders and policymakers.

This study aims to assess the effects of natural disasters on food security in a sample of 40 sub-Saharan African countries. First, the authors assess the effects of natural disasters on the four dimensions of food security and second, the authors disaggregate natural disaster using the two dimensions that are most representative, namely, hydrological and biological disasters.

The regressions are based on the generalised method of moments on a data set covering the period 2005–2020. Natural disasters are measured by the total number of people affected and food security by its characteristics: access, availability, use and sustainability.

The results show that natural disasters increase the prevalence of undernourishment but reduce dependence on cereal imports. An increase in natural disasters by 1% increases the prevalence of undernourishment by the same proportion. As for import dependency, a 1% increase in natural disasters reduces dependency by 2.2%. The disaggregated effects show that hydrological disasters are more significant than biological disasters in impacting food security. Floods reduce the average energy supply adequacy but also dependence on cereal imports. Policy implications are discussed.

The study complements the extant literature by assessing the effects of natural disasters on food security in a region where food insecurity is one of the worst in the world.