In Côte d'Ivoire, the government and its development partners have implemented a national strategy to promote agroforestry and reforestation systems as a means to combat deforestation, primarily driven by agricultural expansion, and to increase national forest cover to 20% by 2045. However, the assessment of these systems through traditional field-based methods remains labor-intensive and time-consuming, particularly for the measurement of dendrometric parameters such as tree height. This study introduces a remote sensing approach combining drone-based Airborne Laser Scanning (ALS) with ground-based measurements to enhance the efficiency and accuracy of tree height estimation in agroforestry and reforestation contexts. The methodology involved two main stages: first, the collection of floristic and dendrometric data, including tree height measured with a laser rangefinder, across eight (8) agroforestry and reforestation plots; second, the acquisition of ALS data using Mavic 3E and Matrice 300 drones equipped with LiDAR sensors to generate digital canopy models for tree height estimation and associated error analysis. Floristic analysis identified 506 individual trees belonging to 27 genera and 18 families. Tree height measurements indicated that reforestation plots hosted the tallest trees (ranging from 8 to 16 m on average), while cocoa-based agroforestry plots featured shorter trees, with average heights between 4 and 7 m. A comparative analysis between ground-based and LiDAR-derived tree heights showed a strong correlation (R2 = 0.71; r = 0.84; RMSE = 2.24 m; MAE = 1.67 m; RMSE = 2.2430 m and MAE = 1.6722 m). However, a stratified analysis revealed substantial variation in estimation accuracy, with higher performance observed in agroforestry plots (R2 = 0.82; RMSE = 2.21 m and MAE = 1.43 m). These findings underscore the potential of Airborne Laser Scanning as an effective tool for the rapid and reliable estimation of tree height in heterogeneous agroforestry and reforestation systems.

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.

In the history of public health, space has evolved through several stages driven by shifts in concepts of disease control. The history of public health is summarized by George Rosen in six phases: Origins (before 500 CE), Middle Ages (500–1500), Mercantilism and Absolutism (1500–1750), Enlightenment and Revolution (1750–1830), Industrialism and the Sanitary Movement (1830–1875), and the Bacteriological Era (1875–present). By integrating architectural sociology—a temporal lens examining the interplay between architecture, individuals, and society—this study investigates how architects historically responded to public health challenges, offering critical insights for contemporary healthy habitat design. Architecture not only addresses survival needs but also materializes societal consciousness. The progression of health-related cognition (e.g., germ theory), behavioural norms (e.g., hygiene practices), infrastructure systems (e.g., sanitation networks), and scientific advancements collectively redefined spatial paradigms. Architects constructed temples, thermae, lazarettos, Beitian Yangbingfang (charitable infirmaries), anatomical theaters, quarantine hospitals, tenements, mass housing, and biosafety laboratories. These cases exemplify the co-evolution of “Concept” (disease control ideologies), “Technology” (construction methods), and “Space” (built environments). By synthesizing centuries of public health spatial practices, this research deciphers the dynamic interplay among “Concept, Technology, and Space”. Leveraging historical patterns, we propose a predictive framework to refine future spatial strategies in anticipation of emerging health crises.

In response to the increasing youth unemployment rate and the demand for future-oriented career development, university student entrepreneurship has emerged as a critical domain in both economic policy and education. This study conducts a comprehensive literature review to examine the interrelationships between entrepreneurship, entrepreneurship education, entrepreneurial competency, and entrepreneurial intention among university students, with an emphasis on the Human Resource Development (HRD) perspective. The review reveals that entrepreneurial mindset significantly influences students’ intention to start a business, while entrepreneurship education contributes both directly and indirectly through the development of entrepreneurial competencies. Entrepreneurial competencies serve as a practical foundation for translating intention into action and are integral to HRD’s goal of competency-based talent development. The study further highlights that entrepreneurship education aligned with HRD principles—such as experiential learning, self-directed development, and learning organization frameworks—can foster employability and self-employment capacity. This integrative analysis suggests that university entrepreneurship programs should not be seen merely as policy instruments, but rather as strategic HRD initiatives for developing future-ready, opportunity-creating human capital. Implications for educational design, policy development, and future empirical research are discussed.

As social growth and educational concepts continue to evolve, college libraries, as hubs of cultural innovation and inheritance, are crucial in advancing the practice of great traditional culture aesthetic teaching. Based on the special status and resource advantages of college libraries, this paper explores the paths and approaches colleges libraries take in advancing the practice of aesthetic education of excellent traditional culture by combining the connotation and characteristics of excellent traditional culture. With a study of the research and case studies that concentrate on the planning of cultural events, the development of collection resources, and the use of digital innovation, it suggests a workable path. The goal is to give university libraries theoretical direction and useful references so they can carry out the aesthetic education of superior traditional culture.