The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

This study systematically examines the literature of electric vehicle (EV) purchase intention and consumer behavior using a bibliometric method to unveil three main research questions: 1) identifying influential publications, authors, and journals; 2) analyzing the thematic evolution of research over time; and 3) identifying emerging research directions. The main objective is to provide a comprehensive understanding of the current state of knowledge and to guide future research in this evolving field. A comprehensive bibliometric analysis was conducted, using Scopus statistics analysis, R-Studio Biblioshiny and VOSviewer, comprising 687 publications authored by 1743 researchers representing 34 different countries with the dataset sourced from the Scopus database from 2010 to 2023. To achieve a nuanced understanding of the research landscape, a multifaceted approach was adopted, including detailed citation analysis, author co-citation analysis, keyword analysis, and thematic mapping. Through meticulous analysis, this study identifies the most influential publications, authors, and journals in the domain of EV purchase intentions and consumer behaviors. It also traces the evolution of themes over time and identifies emerging research directions, providing valuable insights into the trajectory and future avenues of inquiry within this field. The findings contribute to a deeper understanding of the dynamics shaping research in the realm of EVs. The insights gained contribute significantly to advancing knowledge in this crucial domain, offering theoretical insights and practical implications for policymakers, businesses, manufacturers, and academics.

Digitalization has recently gained significant relevance in the education field. The focus has been on its use and application, as well as on training teachers and students to become responsible, competent, and ethical users of technology. This is connected to the creation of policies and programs that promote online learning and interaction from basic to higher education. In this context, this study aims to analyze the scientific production related to digital citizenship through a bibliometric mapping of publications indexed in the Web of Science database. The goal is to identify the main research trends in this field. The results show a growth in the number of publications since 2016, mainly focusing on topics such as digital citizenship media, digital competences, higher education, teachers, students, adolescents, adults, competence, digital literacy, and citizenship education. The presence of a significant number of journals related to the field of education denotes a close relationship between this field and the topic of study. Also, it is revealing a higher concentration of research production in the United States and Europe, with Latin America being absent from this scenario. The study identifies an intellectual structure of the discipline, particularly regarding the most relevant authors, journals, and descriptors. These results are important for understanding the research practices inherent to the field, which projects digital citizenship as an emerging topic. The study concludes by proposing lines of interest for further research on the topic in education and other fields, as well as acknowledging the limitations found in the present article.

The Guacimal River catchment has an area of 181 km² and is located in the NW of Costa Rica, between the coordinates 84.745° W-10.016° N and 84.909° W-10.325° N. In this territory, as in most of the country, detailed geomorphological studies are scarce; therefore, the objective of this paper is to present the geomorphological mapping at a scale of 1:25,000 of the Guacimal River, which allows us to explain the dynamics of the agents involved in the modeling of the catchment. The work methodology consisted of three stages: pre-mapping, field activity and post-mapping, which resulted in a map in which ten relief forms are represented, ordered according to their morphogenesis in endogenous modeled and exogenous (fluvial, gravitational and littoral). This document will be the base line for land use planning, both continental and coastal, and for local risk management.

Maps of forest stand condition—the current phase of the forest-forming process—will be useful for foresters in their forest management in addition to the forest planning and cartographic materials. The mapping methodology was applied in the test area of the Bolshemurtinsky forest district of the Krasnoyarsk region, which is typical for the southern taiga forests of East Siberia. Source data for mapping was obtained on the basis of descriptions of the forest subcompartments on the GIS attribute table of the forest district. Forest stand confinement to the terrain relief indicators was identified on the basis of the SRTM 55-01 digital terrain model data. Spatial analysis has been performed using the ArcGIS Spatial Analyst module. Mapping capability has been shown not only for the year of forest inventory but also for the earlier period of time. To determine the predominant species and the age of the 100-year-old forest stand, a scheme was proposed in which the conceivable options are typified depending on the succession trend, the forest stand age prior to disturbance, and the period of reforestation. Map fragments of the test area as of 2006—the year of forest inventory—and as of 1906—the year of the intensive colonization beginning in southern Siberia—are demonstrated. Maps of forest condition in the test area represent successions that are typical in the southern taiga forests of Siberia: post-harvest, pyrogenic, and biogenic. The methodology of forest condition mapping is universal.

This paper uses existing studies to explore how Artificial Intelligence (AI) advancements enhance recruitment, retention, and the effective management of a diverse workforce in South Africa. The extensive literature review revealed key themes used to contextualize the study. This study uses a meta-narrative approach to literature to review, critique and express what the literature says about the role of AI in talent recruitment, retention and diversity mapping within South Africa. An unobtrusive research technique, documentary analysis, is used to analyze literature. The findings reveal that South Africa’s Human Resource Management (HRM) landscape, marked by a combination of approaches, provides an opportunity to cultivate alternative methods attuned to contextual conditions in the global South. Consequently, adopting AI in recruiting, retaining, and managing a diverse workforce demands a critical examination of the colonial/apartheid past, integrating contemporary realities to explore the potential infusion of contextually relevant AI innovations in managing South Africa’s workforce.