This study presents GeoSAM, a QGIS plugin that integrates five SAM prompting strategies (Text, Box, Point, Detection-guided Box and Detection-guided Point) for agricultural land extraction. Using 25 cm-resolution aerial imagery, Point Prompt achieved the highest F1-Score (0.9785) but is limited for large-scale use. Text Prompt scored 0.9406, with generic terms (e.g. ‘farm plot’) outperforming crop-specific terms. Adding text to Box Prompt improved its F1-Score from 0.5417 to 0.9220 (~70% gain), highlighting the value of semantic information. Detection-guided Point (0.8861) slightly outperformed Detection-guided Box+Text (0.8785). Compared to 97.05% F1 with 8 cm drone imagery, the performance dropped to 88.62% at 25 cm resolution, underscoring resolution's critical impact. GeoSAM helps practitioners navigate the accuracy–automation trade-off by offering context-appropriate segmentation strategies within a unified tool.

Given the increasing need for accurate agricultural land monitoring due to the discrepancy between official records and actual land use in South Korea, an urgent demand is for automated monitoring systems. This study aims to develop a system that combines object detection and segmentation capabilities in high-resolution images using the YOLO-Seg model for paddy parcel monitoring based on drone imagery. We propose a centroid-based tiling-movement technique together with a systematic analysis of optimal overlap rates (0–90

The consolidation and closure of small schools in rural areas has not only worsened the educational environment but also risked accelerating the socioeconomic decline of rural communities. This study examines how elementary school closures affect educational accessibility and seeks to optimize closure prioritization through a fairness-oriented approach. An optimal prioritization model, developed using the p-median algorithm, was applied to simulate and assess changes in commuting conditions and spatial equity. Using a case study of a South Korean county, we demonstrate the model’s ability to minimize disparities in urban and rural commuting environments while ensuring a balanced and fair decision-making process for school closures. This approach highlights a viable pathway to equitable educational infrastructure planning in regions facing demographic decline.

This study investigates the impact of foundation connection uncertainties on the structural reliability of single-span greenhouses by means of a Monte Carlo simulation. Comparative analyses between fixed-support conditions and those allowing displacement and rotation revealed significant differences in the structural behavior. Under fixed conditions, the maximum von Mises stress remained within the allowable limits (182.6 MPa), with a failure probability of 27.0%. However, with displacement and rotation, the stress exceeded the allowable limits by 50.8% (445.4 MPa), with the failure probability increasing to 50.7%. The results here indicate that the snow unit weight significantly influences structural safety and that the ground-structure interaction is a decisive factor with regard to the structural behavior. Current design standards may be inadequate, particularly given the increasingly extreme environmental conditions caused by climate change. This study suggests incorporating snow unit weight criteria and ground-structure interaction guidelines into greenhouse design standards.

Agricultural land management increasingly depends on accurate object detection and segmentation from aerial imagery. Traditional approaches relying on bounding boxes often lack the precision required for detailed agricultural monitoring, whereas manual segmentation remains time-consuming and costly. This study develops a YOLO-SAM fusion framework integrating the You Only Look Once (YOLO) detector with the Segmentation Anything Model (SAM) to enhance the accuracy and efficiency of agricultural field detection and delineation from high-resolution drone imagery. The proposed model uses YOLO’s bounding-box centroids as input prompts for SAM, linking semantic recognition with geometric segmentation to generate precise polygon boundaries without manual annotation. The framework also enables direct integration into QGIS for visualization and spatial analysis, thereby supporting practical GIS-based applications. The YOLO-SAM fusion model achieved optimal performance with a confidence score of 0.5 and 80% tile overlap during image tilling, resulting in a detection area ratio of 96.5% and an error area ratio of 4.0%. These results demonstrate the proposed approach effectively manages complex agricultural environments while reducing data preparation costs and providing a scalable, GISoperational solution for automated agricultural field mapping and resource management

Wild-simulated ginseng (WSG; Panax ginseng C.A. Meyer) is prized for its unique ginsenoside profile and medicinal properties; however, traditional cultivation faces challenges, such as declining survival rates and prolonged growth periods. We aimed to develop an enhanced post-harvest treatment using climate-smart conditions to improve survival rates, boost root mass, and preserve the characteristic ginsenoside profile of WSG. Three-year-old WSG roots were transplanted and cultivated in a controlled smart facility in regulated light and irrigation conditions for five months using premium ginseng soil, a certified organic ginseng soil medium. Growth performance was monitored and ginsenoside profiles were analysed via ultra-performance liquid chromatography. Post-treatment, WSG exhibited a 2.5-fold increase in root weight and an overall survival rate of 97%. Total ginsenoside content reached 10.458 mg/g dried ginseng, with notably high levels of Re (7.716 mg/g) and the presence of rare compounds, such as Compound K and Rg3. The root-to-shoot ratio exceeded 1.23, indicating efficient resource allocation. These results demonstrate that climate-smart post-harvest treatment effectively enhances root development and maintains the medicinal quality of WSG, offering a promising strategy to overcome the limitations of conventional cultivation and improve its commercial viability.

The increasing frequency of climate change and extreme weather events has highlighted the urgency of structural safety in agricultural facilities. Accurate predictions of internal forces and deformations in frame members are essential for ensuring structural integrity. However, the conventional finite element method (FEM) analysis is computationally expensive and requires a complete reanalysis when structural configuration or loading conditions are modified. This study proposes a novel approach using physics-informed neural networks (PINNs) based on FEM principles to predict internal forces and deformations in single-span greenhouse frames. The proposed model integrates equilibrium equations, strain-displacement relationships, and material constitutive laws into the loss function. The accuracy of this approach was evaluated using a single-span agricultural greenhouse subjected to snow loads. The results show that PINN achieves remarkable accuracy, with maximum errors of 1.24% and deformation errors of 0.1 mm, showing excellent agreement with FEM analysis results. These findings validate the effectiveness of the proposed FEM-based PINN for analyzing complex structural systems, presenting a significant methodological advancement applicable to structural analyses of agricultural facilities.

This study introduces an expanded methodology for smart regional planning tailored to improve public service accessibility. We develop a city-level distance-time conversion factor (DCF) that utilizes regional characteristics to offer more intuitive estimates of travel times and distances in public service planning. This approach integrates three key variables: road network distances, Euclidean straight-line distances, and minimum travel times derived from both speed limits and actual traffic speeds. The DCF, formulated from the circuity factor (CF) and the delay factor (DF), identifies areas with elevated DCF values, particularly in major metropolitan areas. These metrics serve as critical indicators for densely populated areas, marking a substantial improvement over traditional methods of uniform location planning. Our analysis addresses underdevelopment and population density challenges, underscoring the need for adaptable planning strategies. By incorporating real-time traffic data, the DCF provides insights crucial for strategically developing public infrastructure in high-demand regions. This research enhances the existing smart public service planning frameworks, emphasizing the significance of regional-specific strategies. Ultimately, our findings advocate for a tailored approach to infrastructure development, aiming to create more efficient and responsive public services.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Odorous contamination would occur in the polluted soils' remediation of the abandoned industry factory. To suppress odorous substances' emissions, a novel foam was prepared with plant protein, xanthan gum, and alkyl glycosides. Since xanthan gum and alkyl glycosides have the characteristics of prolonged foam half-life and enhanced foaming ability, the optimal ratio of xanthan gum to alkyl glycosides is crucial during foam formulation determination. When xanthan gum concentration increased from 0.00% to 0.30%, the viscosity of the foaming solution increased from 12 to 223 mPa∙s, while the surface tension declined by 12.3%. As a result, the foam half-life increased from 0.3 to 56 h, and the foaming volume decreased by 25.5%. On contrary, with the addition of alkyl glycosides, the surface tension decreased significantly, and the foaming volume increased by 36.5% under the xanthan gum concentration of 0.30%. Therefore, the optimal content of xanthan was 0.30% in the presence of 100 mM alkyl glycoside. Then, this novel foam could prevent 67% of p-xylene from escaping within 24 h. In actual sites, novel foams showed a barrier rate of over 75% on different odorous substances most of the time.

Urban air mobility (UAM) using passenger drones has drawn considerable attention as a next-generation mobility solution. UAM may be an alternative for realizing carbon neutrality and for solving traffic congestion in large cities. We designed a three-dimensional movement route model of a passenger drone and examined the environmental impact to inform Seoul's urban air traffic (UAM) policy. This study explored the safety route of large drones using 3D spatial information elements based on a weighted entropy function. Life cycle assessment was used to evaluate the environmental impact of replacing internal combustion engine vehicles (ICEV) with passenger drones. We found that safe routes include high-frequency routes traveling along a stream or river to minimize human and material damage even if the drone would fall when moving along a stream or river. The analysis of the amount of pollutant emissions based on the traffic volume of Seoul shows that current emissions are 4.7 times the estimated emissions from passenger drones. Since environmental damage can be controlled based on the national energy supply and demand policy, further in-depth research should be conducted.

Public health risks such as obesity are influenced by numerous personal characteristics, but the local spatial structure such as an area's built environment can also affect the obesity rate. This study analyzes and discusses how a greenbelt plan as a tool of urban containment policy has an effect on obesity. This study conducted spatial econometric regression models with five factors (13 variables) including transportation, socio-economic, public health, region, and policy factors. The relationship was analyzed between two policy effects of a greenbelt (i.e., a green buffer zone) and obesity. The variables for two policy effects of greenbelt zones are the size of the greenbelt and the inside and outside areas of the greenbelt. The results indicate that the two variables have negative effects on obesity. The results of the analyses in this study have several policy implications. Greenbelts play a role as an urban growth management policy, leading to a reduced obesity rate due to the influence of the transportation mode. In addition, greenbelts can also reduce the obesity rate because they provide recreation spaces for people.

Providing rapid access to emergency medical services (EMS) within the “golden time” for survival is important to improve the survival rate of emergency patients. This study analyzes the accessibility of EMS based on driving speed changes following real-time road traffic conditions by time to estimate vulnerable areas for EMS and survival rates of emergency patients. The key results of the network analysis based on real-time road speed and this evaluation of vulnerable areas by village level across South Korea reveal the different characteristics of urban and rural areas to access emergency medical facilities. In urban areas, road traffic congestion during rush hour delays the patients’ access time to EMS. In contrast, in rural areas, the long geographical distance to an emergency medical facility is a hurdle for receiving care from an EMS during the “golden time” because emergency medical facilities are mostly located in urban areas. The existing standard to assess vulnerable areas of EMS accessibility is based on the speed limit of roads, but the time may be underestimated because the speed limit alone does not reflect the real road conditions. The study results show that an effective way to increase the survival rate is receiving immediate first aid treatment, which means that the government should continuously train the public to perform cardiopulmonary resuscitation (CPR) as well as install automated external defibrillators (AEDs) in populated places, and train the public to use them. Reducing assess time to emergency medical centers in urban areas and providing additional manpower to help with first aid in rural areas are reasonable ways to improve the survival rate of emergency patients.