Search Results (83)

In marine seismic surveys, towed streamers record only pressure data with limited offsets and insufficient low-frequency content, whereas Ocean Bottom Nodes (OBNs) acquire multi-component data with wider offset and sufficient low-frequency content, albeit with sparser spatial sampling. Elastic full waveform inversion (EFWI) is used to estimate subsurface elastic properties by matching observed and synthetic data. However, using only towed streamer data makes it impossible to reliably estimate shear-wave velocities due to the absence of direct S-wave recordings and limited illumination. Inversion using OBN data is prone to acquisition footprint artifacts. To overcome these challenges, we propose a hierarchical joint inversion method based on P- and S-wave separation (PS-JFWI). We first derive novel acoustic-elastic coupled equations based on wavefield separation. Then, we design a two-stage inversion framework. In Stage I, we use OBN data to jointly update the P- and S-wave velocity models. In Stage II, we apply a gradient decoupling algorithm: we construct the P-wave velocity gradient by combining the gradient using PP-waves from both towed streamer and OBN data and construct the S-wave velocity gradient using the gradient using PS-waves. Numerical experiments demonstrate that the proposed method enhances the inversion accuracy of both velocity models compared with single-source and conventional joint inversion methods. Full article

This study presents an integrated surveying methodology for efficient and accurate cadastral documentation, combining UAV photogrammetry, SLAM-based terrestrial and aerial scanning, and conventional geodetic measurements. Designed to be scalable across various cadastral and planning contexts, the workflow was tested in Charlottenburg, Romania’s only circular heritage village. The approach addresses challenges in built environments where traditional total station or GNSS techniques face limitations due to obstructed visibility and complex architectural geometries. The SLAM system was initially deployed in mobile scanning mode using a backpack configuration for ground-level data acquisition, and was later mounted on a UAV to capture building sides and areas inaccessible from the main road. The results demonstrate that the integration of aerial and terrestrial data acquisition enables precise building footprint extraction, with a reported RMSE of 0.109 m between the extracted contours and ground-truth total station measurements. The final cadastral outputs are fully compatible with GIS and CAD systems, supporting efficient land registration, urban planning, and historical site documentation. The findings highlight the method’s applicability for modernizing cadastral workflows, particularly in dense or irregularly structured areas, offering a practical, accurate, and time-saving solution adaptable to both national and international land administration needs. Beyond the combination of known technologies, the innovation lies in the practical integration of terrestrial and aerial SLAM (dual SLAM) with RTK UAV workflows under real-world constraints, offering a field-validated solution for complex cadastral scenarios where traditional methods are limited. Full article

The world population affected by seismic risk is increasing due to urban sprawl, especially in vulnerable areas of countries with high population growth. Despite this trend, seismic exposure assessments have predominantly focused on cities in high-income countries, leaving a knowledge gap in data-scarce, seismically active urban areas. This research presents a novel, scalable geospatial methodology for seismic exposure assessment in contexts with limited data availability and its application to San José, Costa Rica, evaluating its time and cost efficiency. The methodology prioritizes the use of free and open-access geospatial data to construct city-scale Geospatial Exposure Databases (city-GEDs) at the individual building level. These databases integrate key attributes from the Global Earthquake Model (GEM) taxonomy, including the building footprint, the plan regularity, the construction date, the roof material, the relative position within the urban block, and urban block compactness. Random Forest classification models were developed to assign buildings to expert-defined building typologies (BTs). In the case of San José, 7226 buildings were classified into eight typologies using the derived attributes, achieving a classification error of 46%. When the building height—visually sampled—was included, the error decreased significantly to 13%, confirming its importance in typology prediction and emphasizing the need for efficient acquisition strategies. This approach is essential for quick pre- or post-disaster seismic risk assessment, allowing time and cost-effective data collection and analysis. This contribution is particularly relevant for Central America and other seismically active regions with limited data, supporting improved risk analysis and urban resilience planning. Full article

Flatfoot, or pes planus, is a condition where the foot’s arch collapses, leading to complications such as pain, gait abnormalities, and an increased risk of injury. Accurate and early diagnosis is critical for effective treatment. Traditional diagnostic methods, including radiographic imaging, footprint analysis, and plantar pressure measurement, often require specialized equipment and are subjective. This study proposes a novel diagnostic device that captures 2D plantar foot images to calculate key morphological indices, including the Staheli Index, Clark’s Angle, and Chippaux–Smirak Index, for flatfoot detection. The device, designed with off-the-shelf components, includes a transparent toughened glass platform and LED illumination to capture images using web cameras. A Python-based application was developed for image acquisition, segmentation, and stitching. The device was tested on 55 participants aged 18–28, and the extracted morphological indices were validated against established thresholds for flatfoot diagnosis. The results showed that the Staheli Index, Chippaux–Smirak Index, and Clark’s Angle reliably detected flatfoot in participants. The study highlights the potential of this device for non-invasive, accurate, and rapid flatfoot diagnosis. Future advancements in deep learning could enhance its capabilities, making it a valuable tool for proactive healthcare in foot deformity detection. Full article

The transition to electric public transportation is crucial for reducing the carbon footprint and promoting environmental sustainability. However, successful implementation requires strong public policies, including tax incentives and educational programs, to encourage widespread adoption. This study identifies the optimal electric bus model for Cuenca, Ecuador, using the multicriteria decision-making methods PROMETHEE and TOPSIS. The evaluation considers four key dimensions: technical (autonomy, passenger capacity, charging time, engine power), economic (acquisition, operation, and maintenance costs), social (community acceptance and accessibility), and environmental (reduction of pollutant emissions). The results highlight passenger capacity as the most influential criterion, followed by autonomy and engine power. The selected electric bus model emerges as the most suitable option due to its energy efficiency, low maintenance costs, and long service life, making it a cost-effective long-term investment. Additionally, its adoption would enhance air quality and improve the overall user experience. Beyond its relevance to Cuenca, this study provides a replicable methodology for evaluating electric bus feasibility in other cities with different geographic and socioeconomic contexts. Full article

This study develops a globally adaptable and scalable methodology for high-resolution, building-level population mapping, integrating Earth observation techniques, geospatial data acquisition, and machine learning to enhance population estimation in rapidly urbanizing cities, particularly in developing countries. Using Bangkok, Thailand, as a case study, this research presents a problem-driven approach that leverages open geospatial data, including Overture Maps and OpenStreetMap (OSM), alongside Digital Elevation Models, to overcome limitations in data availability, granularity, and quality. This study integrates morphological terrain analysis and machine learning-based classification models to estimate building ancillary attributes such as footprint, height, and usage, applying micro-dasymetric mapping techniques to refine population distribution estimates. The findings reveal a notable degree of accuracy within residential zones, whereas performance in commercial and cultural areas indicates room for improvement. Challenges identified in mixed-use and townhouse building types are attributed to issues of misclassification and constraints in input data. The research underscores the importance of geospatial AI and remote sensing in resolving urban data scarcity challenges. By addressing critical gaps in geospatial data acquisition and processing, this study provides scalable, cost-effective solutions in the integration of multi-source remote sensing data and machine learning that contribute to sustainable urban development, disaster resilience, and resource planning. The findings reinforce the transformative role of open-access geospatial data in Earth observation applications, supporting real-time decision-making and enhanced urban resilience strategies in rapidly evolving environments. Full article

With advancement in Unmanned Aerial Vehicle (UAV) photogrammetry, productivity in construction management can now be achieved with accuracy and is less labour-intensive. In the basement construction of buildings, prudent earthwork activities are often necessary, setting the basis of the building footprint. As such, monitoring earthwork volume estimation becomes important to avoid over- or under-cutting the earth. Conventional methods by means of land surveying are time-consuming, labour-intensive, and susceptible to varying degrees of accuracy. Moreover, earthwork sites often have multiple activities ongoing that increase the complexity of volume estimation through land surveying. This study explores the use of UAV photogrammetry to estimate earthwork excavation volume in a complex urban earthwork site in Singapore over time and discusses the feasibility, challenges and productivity enhancements of integrating the technology into the construction process. In this study, the earthwork site and controlled trials show that the models reconstructed with UAV photogrammetry data can produce volume measurements that fulfil the stakeholder’s accuracy tolerance of 5% between the estimated and actual volume. The filtering of unwanted objects in the model, such as columns, cranes and trucks, was successful but was insufficient for objects that occluded large areas of the soil surface. The integration of UAV photogrammetry with a highly automated acquisition and processing workflow for earthwork monitoring brings about productivity enhancements in time and labour efforts and improves the efficiency and consistency of models. Furthermore, the digitalisation of earthwork sites into point clouds and three-dimensional (3D) models increases data visualisation and accessibility, facilitates project team collaboration, and enables cross-platform compatibility into Building Information Modelling (BIM), which can significantly aid in reporting and decision-making processes. Full article

Sea fennel, a halophyte with growing economic importance in the Mediterranean region, offers a rich source of bioactive compounds for diverse applications in various industries, including food, pharmaceuticals, and cosmetics. Recognizing the crucial role of eco-design in promoting sustainable food production, this study aimed to assess the environmental impacts of two novel sea fennel products: dried spice and fermented pickles. The Life Cycle Assessment (LCA) method was used to evaluate the environmental burdens of these new products, from raw material acquisition to packaging end-of-life, to fine-tune the innovation process. Primary data were collected from a company in the Marche region, Italy. The Environmental Footprint 3.1 method was applied to analyze the impacts. From the results obtained, the climate change score for the spice was 6.24 kg CO₂ eq./kg spice, while the fermented pickle was 0.89 kg CO₂ eq./kg product—net weight. The results also revealed that primary packaging emerged as the primary environmental hotspot for both products, accounting for more than 40% of the total impacts in most of the impact categories. Glass packaging significantly contributed to the environmental impact of the spice, while both glass jars and tin-plated steel lids contributed substantially to the impact of the pickled products. Despite the generally low impact of sea fennel cultivation, the processing and packaging stages significantly increased the overall environmental impacts of both products. This study provides valuable insights for manufacturers seeking to develop and commercialize highly sustainable sea fennel-based products. By identifying key environmental hotspots and implementing eco-design principles during the product development phase, manufacturers can significantly reduce the environmental impact of these novel food products. Full article

Diabetes is a chronic condition, and traditional monitoring methods are invasive, significantly reducing the quality of life of the patients. This study proposes the design of an innovative system based on a microcontroller that performs real-time ECG acquisition and evaluates the presence of diabetes using an Edge-AI solution. A spectrogram-based preprocessing method is combined with a 1-Dimensional Convolutional Neural Network (1D-CNN) to analyze the ECG signals directly on the device. By applying quantization as an optimization technique, the model effectively balances memory usage and accuracy, achieving an accuracy of 89.52% with an average precision and recall of 0.91 and 0.90, respectively. These results were obtained with a minimal memory footprint of 347 kB flash and 23 kB RAM, showcasing the system’s suitability for wearable embedded devices. Furthermore, a custom PCB was developed to validate the system in a real-world scenario. The hardware integrates high-performance electronics with low power consumption, demonstrating the feasibility of deploying Edge-AI for non-invasive, real-time diabetes detection in resource-constrained environments. This design represents a significant step forward in improving the accessibility and practicality of diabetes monitoring. Full article

This article discusses the challenges of adapting to and mitigating climate change through sustainable resource management in the agri-food sector. These aspects are mandatory obligations for businesses under new EU directives and regulations. Greenhouse gas (GHG) emissions must be controlled at every stage of the value chain, from the acquisition of raw materials to transportation and cooperation with suppliers. The purpose of this paper is to analyze the areas generating GHG emissions in the agri-food enterprise toward the development of guidelines for the sustainable development of domestic food production. This paper presents a GHG study in three scopes at one of the mills in Poland based on the GHG protocol methodology. The analysis of consumption of energy carriers was used to determine GHG emissions (Scopes 1 and 2), and the total amounted to about 2.1 million kg CO_2eq (the share of Scope 1 was about 16% and Scope 2 as high as 83%), and the average carbon footprint of flour production in terms of unit weight was 0.040 kg CO_2eq/kg. Extending the analysis to Scope 3, the emissions associated with this scope accounted for the largest share (92%), while Scopes 1 and 2 accounted for only 8%. The determined carbon footprint (considering the three GHG emission scopes) was 0.52 kg CO_2eq/kg. In Scope 3, the largest contribution was from category 1 emissions (92%) related to grain cultivation, and category 5 (6%) were emissions related to the transportation of sold products. The smallest impact is from category 3 emissions related to the management of generated waste. Regular calculation and reporting of emissions in each area enables the company to more fully understand its environmental impact, identify risks and implement changes that bring financial and environmental benefits. Full article

In recent years, the growth of digital data has been unimaginable. This also applies to geospatial data. One of the largest data types is LiDAR point clouds. Their large volumes on disk, both at the acquisition and processing stages, and in the final versions translate into a high demand for disk space and therefore electricity. It is therefore obvious that in order to reduce energy consumption, lower the carbon footprint of the activity and sensitize sustainability in the digitization of the industry, lossless compression of the aforementioned datasets is a good solution. In this article, a new format for point clouds—3DL—is presented, the effectiveness of which is compared with 21 available formats that can contain LiDAR data. A total of 404 processes were carried out to validate the 3DL file format. The validation was based on four LiDAR point clouds stored in LAS files: two files derived from ALS (airborne laser scanning), one in the local coordinate system and the other in PL-2000; and two obtained by TLS (terrestrial laser scanning), also with the same georeferencing (local and national PL-2000). During research, each LAS file was saved 101 different ways in 22 different formats, and the results were then compared in several ways (according to the coordinate system, ALS and TLS data, both types of data within a single coordinate system and the time of processing). The validated solution (3DL) achieved CR (compression rate) results of around 32% for ALS data and around 42% for TLS data, while the best solutions reached 15% for ALS and 34% for TLS. On the other hand, the worst method compressed the file up to 424.92% (ALS_PL2000). This significant reduction in file size contributes to a significant reduction in energy consumption during the storage of LiDAR point clouds, their transmission over the internet and/or during copy/transfer. For all solutions, rankings were developed according to CR and CT (compression time) parameters. Full article

Although UAV height is precisely determined using GNSS, the vertical distance between the UAV and the sea surface should be subtracted to obtain the sea surface height (SSH). This distance can be measured using nadir-looking LiDAR or GNSS reflectometry (GNSS-R); thus, these two methods are examined in this study through three two-minute UAV experimental flights. The measurements of the flight-averaged SSHs made with both approaches were in good agreement with the reference SSH determined from a GNSS buoy, with differences of 0.03 m (LiDAR) and 0.05 m (GNSS-R), although the standard deviation (SD) for GNSS-R (1.72 m) was significantly larger than that for LiDAR (0.20 m). Each 4 Hz GNSS-R measurement was subject to errors caused by surface waves, though over 16 GNSS reflection points within a 70 m diameter footprint were used; these errors were, however, removed in the temporal mean. Extending the footprint diameter to 230 m with stronger data quality controls resulted in a smaller error (0.02 m) and SD (0.79 m). Meanwhile, LiDAR measured the flat-surface SSH at the nadir only, which inherently filtered out slant reflections, resulting in a lower SD. However, this filter reduces data acquisition rates, especially when the UAV attitude tilts. Full article

Construction is one of the economic sectors with the greatest influence on climate change. In addition to working procedures, the primary carbon footprint is attributed to the choice of materials and the energy required for their manufacturing. The underlying idea of this study is to minimize the effects and offer new solutions to emerging problems in the quest for materials that can be deemed as natural, such as gypsum (calcium sulphate dihydrate) and rice straw (Oryza sativa). The acquisition of these materials involves a lower carbon footprint compared to the conventional materials. It is well known since ancient times that gypsum and cereal straw can be used in construction, with numerous examples still available. Cereal straw is one of the oldest construction materials, traditionally combined with earth and occasionally with certain binders, with it continuing to be employed in construction in many countries to this day. This work showcases the feasibility of producing stable prefabricated elements from straw waste with construction gypsum, addressing a significant environmental concern posed by the alternative of having to burn such materials. In this study, for the proposed bio-based material, specific tests, such as thermal conductivity, flexural and compressive strength, and fire resistance, were carried out to evaluate the principal physical and mechanical characteristics for different compositions of water, gypsum, and straw fiber samples. The results highlighted the good performance of the proposed materials in order to spread their use in the green building industry. The addition of straw fibers improved, in different ways, some important physical characteristics of these components so as to diminish environmental pollution and to obtain better material performance. The tests highlighted the different behaviors of the proposed material with respect to the different cuts of the straw and as well as the water/gypsum ratio; this is not very well understood and probably depends on the micro structure of the straw fibers. The blocks with raw straw showed a significant improvement in the breaking mechanism (1775.42 N) compared to the blocks with cut straw (712.26 N) when subjected to bending tests, and their performance in compression tests was also acceptable. Additionally, a very interesting reduction in thermal conductivity was achieved by incorporating rice straw (0.233 W/mK), and high fire exposure times were obtained, with gypsum preventing the spread of ignition in any type of fiber. Full article

This study proposes an improved methodology based on life cycle assessment (LCA), which is used to calculate the carbon footprint of agriculture, provides a simple and feasible calculation path, and constructs a streamlined framework for calculating the carbon footprint based on the agricultural traceability system records. Using important economic crop (Onion) as research subject, and choose the largest planting area in R.O.C. (southern Taiwan) as a case study. A total of 64 farm production history records have been collected, includes all of farms certified with a traceable agricultural products (TAP) label. Through a detailed analysis of the traditional carbon footprint calculation method, found that agricultural traceability system records could replace the data source in carbon footprint verification (CFV) process, and system records could be used as activity data after being organized. With our method, no need to go through a complicated CFV process, just download the existing data on agricultural traceability system, can start calculating carbon footprint as soon as possible. To compared to traditional assessment method, results show a margin of error is less than 6% compared to traditional assessment methods. Advantages of improved method were be found, such as easy data acquisition, simplified calculation steps, and improved data transparency and accuracy. From statistical data, show that at least seven categories of carbon emission sources for carbon footprints, the most significant of carbon emission impact are fertilizers. The result of improved methodology based on life cycle assessment (LCA), show that using the improved methods can help promote the carbon footprint management efficiency of agricultural organizations such as Farmers’ Association or Agricultural Production Marketing Group, promptly monitor the carbon footprint status of their fields and adjust strategies to reduce carbon footprints in real-time, advancing towards the goal of net-zero carbon emissions. Full article

Angle domain common image gathers (ADCIGs) play a crucial role in seismic exploration, offering prestack underground illumination information that aids in validating migration velocity and conducting prestack amplitude versus angle (AVA) analysis for reservoir characterization. This paper introduces an innovative approach for compensating amplitude errors caused by irregular seismic acquisition geometries in ADCIGs. By incorporating an angle domain illumination compensation factor, the proposed method effectively modifies these errors, preserving the amplitude of seismic reflectivity in the prestack angle domain. The effectiveness of the proposed approach is validated through comprehensive tests conducted on synthetic and field data examples. The results demonstrate the capability of the method to enhance the quality of ADCIGs derived from 3D reverse time migration (RTM), yielding accurate and reliable amplitude preservation. While the illumination compensation factor assumes a vertically linear velocity model, the method holds promise for extension to more complex media and diverse migration techniques. This suggests its applicability and adaptability beyond the specific assumptions considered in this study. In conclusion, this paper presents an innovative angle domain illumination compensation factor that significantly improves the quality of ADCIGs by addressing amplitude errors arising from irregular seismic acquisition geometries. The experimental validation using synthetic and field data confirms the effectiveness of the proposed method within the context of 3D RTM. Furthermore, the technique holds potential for broader application in more complex subsurface scenarios and various migration methodologies. Full article