Search Results (1,711)

Previous reviews have examined specific facets of Geographic Information Systems (GIS) in transportation planning, such as transit-focused applications and open source geospatial tools. However, this study offers the first systematic, PRISMA-guided longitudinal evaluation of GIS integration in transportation planning, spanning thematic domains, data models, methodologies, and outcomes from 2004 to 2024. This study addresses this gap through a longitudinal analysis of GIS-based transportation research from 2004 to 2024, adhering to PRISMA guidelines. By conducting a mixed-methods analysis of 241 peer-reviewed articles, this study delineates major trends, such as increased emphasis on sustainability, equity, stakeholder involvement, and the incorporation of advanced technologies. Prominent domains include land use–transportation coordination, accessibility, artificial intelligence, real-time monitoring, and policy evaluation. Expanded data sources, such as real-time sensor feeds and 3D models, alongside sophisticated modeling techniques, enable evidence-based, multifaceted decision-making. However, challenges like data limitations, ethical concerns, and the need for specialized expertise persist, particularly in developing regions. Future geospatial innovations should prioritize the responsible adoption of emerging technologies, inclusive capacity building, and environmental justice to foster equitable and efficient transportation systems. This review highlights GIS’s evolution from a supplementary tool to a cornerstone of data-driven, sustainable urban mobility planning, offering insights for researchers, practitioners, and policymakers to advance transportation strategies that align with equity and sustainability goals. Full article

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article

This study examines the impact of free trade zone (FTZ) establishment on regional economic resilience (RER) in China, using provincial-level panel data spanning from 2010 to 2022 and a multi-period difference-in-differences (DID) approach. The empirical results indicate that FTZ implementation significantly enhances regional economic resilience by 3.46%, with the development of green finance acting as a key moderating mechanism that amplifies this positive effect. Heterogeneity analysis uncovers notable disparities across policy cohorts and geographical regions: the first wave of FTZs demonstrates the most pronounced resilience-enhancing impact, whereas later cohorts exhibit weaker or even adverse effects. Coastal regions experience substantial benefits from FTZ policies, in contrast to statistically insignificant outcomes observed in inland areas. These findings suggest that strategically expanding the FTZ network, when paired with tailored implementation mechanisms and the integration of green finance, could serve as a powerful policy tool for post-COVID economic recovery. Importantly, by strengthening economic resilience through institutional openness and green investment, this study offers valuable insights into balancing economic growth with environmental sustainability. It provides empirical evidence to support the optimization of FTZ spatial governance and institutional innovation pathways, thereby contributing to the pursuit of sustainable regional development. Full article

Enhancing market integration levels is crucial for advancing sustainable regional collaborative development and achieving ecological protection and high-quality development goals within the Yellow River Basin, fostering a balance between economic efficiency, social equity, and environmental resilience. This study analyzed the retail price data of goods from prefecture-level cities in the Yellow River Basin from 2010 to 2022, employing the relative price method to measure the market integration index. Additionally, it examined the temporal and spatial evolution patterns and driving factors using the Dagum Gini coefficient and panel regression models. The results indicate the following. (1) The market integration index of the Yellow River Basin shows a fluctuating upward trend, with an average annual growth rate of 9.8%. The spatial pattern generally reflects a situation where the east is relatively high and the west is relatively low, as well as the south being higher than the north. (2) Regional disparities are gradually diminishing, with the overall Gini coefficient decreasing from 0.153 to 0.104. However, internal differences within the downstream and midstream areas have become prominent, and contribution rate analysis reveals that super-variable density has replaced between-group disparities as the primary source. (3) Upgrading the industrial structure and enhancing the level of economic development are the core driving forces, while financial support and digital infrastructure significantly accelerate the integration process. Conversely, the level of openness exhibits a phase-specific negative impact. We propose policy emphasizing the need to strengthen development in the upper reach of the Yellow River Basin, further improve interregional collaborative innovation mechanisms, and enhance cross-regional coordination among multicenter network nodes. Full article

The impacts of the climate crisis compel congregations to reimagine their mission and identity in various ways. Working with data taken from U.S. clergy participating in an online program for education and support on climate and environmental issues, as well as selected congregational leaders from their congregations, this article examines the ways that ministers and their congregations in primarily North American mainline Protestant contexts frame the climate crisis and how those understandings both create tension and open space for new conversations about their Christian and congregational vocation. It also describes how these ministers and congregations engage with environmental issues through means beyond technological solutions and consumption choices, such as collaborating with other community organizations, hosting rituals for grieving or meaning-making, and inviting transformative encounters with the more-than-human world. Finally, it will suggest possible strategies for leaders and their congregations to frame and creatively engage with the environment through various methods. Full article

The maritime sector is undergoing rapid transformation, driven by increasingly stringent international regulations targeting air pollution. While newly built vessels integrate advanced technologies for compliance, the global fleet averages 21.8 years of age and must meet emission requirements through retrofitting or operational changes. This study evaluates, at environmental and economic levels, two key sulphur abatement strategies for a 1998-built cruise vessel nearing the end of its service life: (i) the installation of open-loop scrubbers with fuel enhancement devices, and (ii) a switch to marine diesel oil as main fuel. The analysis was based on real operational data from a cruise vessel. For the environmental assessment, a Tier III hybrid emissions model was used. The results show that scrubbers reduce SO_x emissions by approximately 97% but increase fuel consumption by 3.6%, raising both CO₂ and NO_x emissions, while particulate matter decreases by only 6.7%. In contrast, switching to MDO achieves over 99% SO_x reduction, an 89% drop in particulate matter, and a nearly 5% reduction in CO₂ emissions. At an economic level, it was found that, despite a CAPEX of nearly USD 1.9 million, scrubber installation provides an average annual net saving exceeding USD 8.2 million. From the deterministic and probabilistic analyses performed, including Monte Carlo simulations under various fuel price correlation scenarios, scrubber installation consistently shows high profitability, with NPVs surpassing USD 70 million and payback periods under four months. Full article

Geological storage is an integral element of the green energy transition. Geological formations, such as aquifers, depleted reservoirs, and hard rock caverns, are used mainly for the storage of hydrocarbons, carbon dioxide and increasingly hydrogen. However, potential adverse effects such as ground movements, leakage, seismic activity, and environmental pollution are observed. Existing research focuses on monitoring subsurface elements of the storage, while on the surface it is limited to ground movement observations. The review was carried out based on 191 research contributions related to geological storage. It emphasizes the importance of monitoring underground gas storage (UGS) sites and their surroundings to ensure sustainable and safe operation. It details surface monitoring methods, distinguishing geodetic surveys and remote sensing techniques. Remote sensing, including active methods such as InSAR and LiDAR, and passive methods of multispectral and hyperspectral imaging, provide valuable spatiotemporal information on UGS sites on a large scale. The review covers modelling and prediction methods used to analyze the environmental impacts of UGS, with data-driven models employing geostatistical tools and machine learning algorithms. The limited number of contributions treating geological storage sites holistically opens perspectives for the development of complex approaches capable of monitoring and modelling its environmental impacts. Full article

This paper presents a conceptual and methodological framework for using Geographical Information Systems (GIS) to “deep map” cultural heritage sites along Ireland’s Wild Atlantic Way, with a focus on the 1588 Spanish Armada wrecks in County Kerry and archaeological landscapes in County Sligo’s “Yeats Country.” Drawing on interdisciplinary dialogues from the humanities, social sciences, and geospatial sciences, it illustrates how digital spatial technologies can excavate, preserve, and sustain intangible cultural knowledge embedded within such palimpsestic landscapes. Using MAXQDA 24 software to mine and code historical, literary, folkloric, and environmental texts, the study constructed bespoke GIS attribute tables and visualizations integrated with elevation models and open-source archaeological data. The result is a richly layered cartographic method that reveals the spectral and affective dimensions of heritage landscapes through climate, memory, literature, and spatial storytelling. By engaging with “deep mapping” and theories such as “Spectral Geography,” the research offers new avenues for sustainable heritage conservation, cultural tourism, and public education that are sensitive to both ecological and cultural resilience in the West of Ireland. Full article

Blasting is an effective technique for fragmenting rock in open-pit mining operations. Blasting operations produce either boulders or fine fragments, both of which increase costs and pose environmental risks. As a result, predicting the mean fragmentation size (MFS) distribution of rock is critical for assessing blasting operations’ quality and mitigating risks. Due to the limitations of empirical and statistical models, several researchers are turning to artificial intelligence (AI)-based techniques to predict the MFS distribution of rock. Thus, this study uses three AI tree-based algorithms—extra trees (ET), gradient boosting (GB), and random forest (RF)—to predict the MFS distribution of rock. The prediction accuracy of the models is optimized utilizing the tree-structured Parzen estimators (TPEs) algorithm, which results in three models: TPE-ET, TPE-GB, and TPE-RF. The dataset used in this study was collected from the published literature and through the data augmentation of a large-scale dataset of 3740 blast samples. Among the evaluated models, the TPE-ET model exhibits the best performance with a coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), and max error of 0.93, 0.04, 0.03, and 0.25 during the testing phase. Moreover, the block size (XB, m) and modulus of elasticity (E, GPa) parameters are identified as the most influential parameters for predicting the MFS distribution of rock. Lastly, an interactive web application has been developed to assist engineers with the timely prediction of MFS. The predictive model developed in this study is a reliable intelligent model because it combines high accuracy with a strong, explainable AI tool for predicting MFS. Full article

Accurate prediction of drifting trajectories is essential for improving the operational efficiency of maritime search and rescue (SAR), particularly within the complex geomorphological settings of island and reef regions, such as those in the South China Sea. This study investigates the drift characteristics of life rafts under varying loading conditions across both open-sea and island–reef regions. Comprehensive field experiments were conducted over 15 days in the waters around the Wanshan Archipelago, using advanced instruments to collect wind, current, and drift trajectory data. Based on these observations, two models—the AP98 leeway model and a BP neural network model—were developed and validated. The results show that the AP98 model performs better in open-sea conditions, whereas the BP neural network provides more accurate predictions in island and reef areas with complex environmental factors. A Monte Carlo simulation was also integrated to enhance the robustness of drift area predictions. These findings offer valuable insights into life raft drift behavior in complex marine environments and provide technical support for improving SAR operations in island–reef regions. Full article

This study focuses on the impact of China’s digital economy on sustainable modernization from 2011 to 2021, using provincial panel data for empirical analysis. By applying threshold and mediation models, we find that the digital economy promotes modernization through industrial upgrading (with a mediating effect of 38%) and trade openness (coefficient = 0.234). The research reveals “U-shaped” nonlinear threshold effects at specific levels of digital development (2.218), market efficiency (9.212), and technological progress (12.224). Eastern provinces benefit significantly (coefficient ranging from 0.12 to 0.15 ***), while western regions initially experience some inhibition (coefficient = −0.08 *). Industrial digitalization (coefficient = 0.13 ***) and innovation ecosystems (coefficient = 0.09 ***) play crucial roles in driving eco-efficiency and equity, in line with Sustainable Development Goals 9 and 13. Meanwhile, the impacts of infrastructure (coefficient = 0.07) and industrialization (coefficient = 0.085) are delayed. Economic modernization improves (coefficient = 0.37 ***), yet social modernization declines (coefficient = −0.12 *). This study not only enriches economic theory but also extends the environmental Kuznets curve to the digital economy domain. We propose tiered policy recommendations, including the construction of green digital infrastructure, carbon pricing, and rural digital transformation, which are applicable to China and offer valuable references for emerging economies aiming to achieve inclusive low-carbon growth in the digital era. Future research could further explore the differentiated mechanisms of various digital technologies in the modernization process across different regions and how to optimize policy combinations to better balance digital innovation with sustainable development goals. Full article

In the context of industrial digitalization, the Industry 5.0 paradigm introduces digital twins as a cutting-edge solution. This study explores the concept of digital twins and their integration with the Industrial Internet of Things (IIoT), offering insights into how these technologies bring intelligence to industrial settings to drive both process optimization and sustainability. Industrial digitalization connects products and processes, boosting the productivity and efficiency of people, facilities, and equipment. These advancements are expected to yield broad economic and environmental benefits. As connected systems continuously generate data, this information becomes a vital asset, but also introduces new challenges for industrial operations. The work presented in this article aims to demonstrate the possibility of generating advanced tools for process optimization. This, which ultimately impacts the environment and empowers people in the processes, is achieved through data integration and the development of a digital twin using open tools such as NodeRed v4.0.9 and Python 3.13.5 frameworks, among others. The article begins with a conceptual analysis of IIoT and digital twin integration and then presents a case study to demonstrate how these technologies support the principles of the Industry 5.0 framework. Specifically, it examines the requirements for applying the golden batch concept within a biological production environment. The goal is to illustrate how digital twins can facilitate the achievement of quality standards while fostering a more sustainable production process. The results from the case study show that biomaterial concentration was optimized by approximately 10%, reducing excess in an initially overdesigned process. In doing so, this paper highlights the potential of digital twins as key enablers of Industry 5.0—enhancing sustainability, empowering operators, and building resilience throughout the value chain. Full article

Agriculture and its related industries produce annually a vast amount of byproducts and waste which comprise a large proportion of global waste. Only a small percentage is managed with environmentally acceptable procedures, while a large proportion is either incinerated or discarded into nearby open fields, causing serious environmental burdens. Since these byproducts exhibit a rich nutritional and phytochemical content, they may be considered as raw materials for various industrial applications, initiating the need for the development of sustainable and eco-friendly methods for their valorization. Among the various methods considered, Solid-State Fermentation (SSF) constitutes an intriguing eco-friendly bioprocess, being suitable for water-insoluble mixtures and providing products with improved stability and depleted catabolic suppression. Thus, there are several literature studies highlighting the aspects and efficacy of SSF for improving the nutritional and phytochemical contents of diverse agro-industrial waste. The review herein aspires to summarize these literature results with a special focus on the enhancement of their antioxidant potency. For this purpose, specific keywords were used for searching multiple scientific databases with an emphasis on the most recent studies and higher impact journals. The presented data establish the usefulness and efficacy of the SSF bioprocess to obtain fermentation products with enhanced antioxidant profiles. Full article