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.

A gradually detailed geophysical investigation took place on Ancient Marina territory. In that area was extended Ancient Tritaea, according to responsible Archaeological Services. The first approach had been attempted since 1988 by applied electric mapping based on a twin-probe array. Later, the survey extended to the peripheral zone under the relative request from the 6th Archaeological Antiquity. A new approach was implemented by combining three different geophysical techniques, like electrical mapping, total intensity, and vertical gradient. These were applied on discrete geophysical grids. Electric mapping tried to separate the area into low and high-interest subareas according to soil resistance allocation. That technique detected enough geometrical characteristics, which worked as the main lever for the application of two other geophysical techniques. The other two techniques would be to certify the existence of geometrical characteristics, which divorced them from geological findings. Magnetic methods were characterized as a rapid technique with greater sensitivity in relation to electric mapping. Also, vertical gradient focuses on the horizontal extension of buried remains. Processing of magnetic measurements (total and vertical) certified the results from electric mapping. Also, both of the techniques confirmed the existence of human activity results, which were presented as a cross-section of two perpendicular parts. The new survey results showed that the new findings related to results from the previous approach. Geophysical research in that area is continuing.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

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.

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.

The present study aimed to delineate subsurface features and identify prospective metallic mineral deposits in the Adıyaman-Besni area, situated within the Southeastern Anatolian Thrust Belt of Turkey. This region, characterized by ophiolitic mélanges and volcanic massive sulfide (VMS) deposits in its geological framework, possesses significant mineralization potential, encompassing copper, lead, and various other sulfide minerals. Utilizing the combined methodologies of Induced Polarization (IP) and Electrical Resistivity Tomography (ERT), a comprehensive electrical mapping of the subsurface structures was conducted, revealing that mineralized zones had low resistivity and high chargeability. The findings indicate that the combined use of IP and ERT techniques yields excellent precision in accurately delineating the features of sulfide mineralization and the peripheries of mineral deposits. This study offers fundamental data for the economic assessment of prospective mineral deposits in the Adıyaman-Besni region and underscores the benefits of IP and ERT techniques in subsurface mapping and mineralization delineation investigations. The mineralized zone has low resistivity (< 50 ohm-m) and strong chargeability (> 30 ms), according to geophysical tests. It also offers a methodological framework for subsequent mineral exploration research in analogous geological formations.