Search Results (18)

This systematic literature review examines the rapid growth of research on the use of drones applied to smart agriculture, a key field for the digital and sustainable transformation of the agricultural sector. The study aimed to synthesize the current state of knowledge regarding the application of drones in smart agriculture by applying the Kitchenham protocol (SLR), complemented with Petersen’s systematic mapping (SMS). A search was conducted in high-impact academic databases (Scopus, IEEE Xplore, Taylor & Francis Online, Google Scholar, and ProQuest), covering the period 2019–2025 (July). After applying the inclusion, exclusion, and quality criteria, 73 relevant studies were analyzed. The results reveal that 90% of the publications appear in Q1 journals, with China and the United States leading scientific production. The thematic analysis identified “UAS Phenotyping” as the main driving theme in the literature, while “precision agriculture,” “machine learning,” and “remote sensing” were the most recurrent and highly interconnected keywords. An exponential increase in publications was observed between 2022 and 2024. The review confirms the consolidation of drones as a central tool in digital agriculture, with significant advances in yield estimation, pest detection, and 3D modeling, although challenges remain in standardization, model generalization, and technological equity. It is recommended to promote open access repositories and interdisciplinary studies that integrate socioeconomic and environmental dimensions to strengthen the sustainable adoption of drone technologies in agriculture. Full article

The application of artificial intelligence (AI) in monitoring and managing ocean waste reveals considerable promise for improving sustainable strategies to combat marine pollution. This study performs a bibliometric analysis to examine research trends, knowledge frameworks, and future directions in AI-driven sustainable ocean waste management. This study delineates key research themes, prominent journals, influential authors, and leading nations contributing to the field by analysing scientific publications from major databases. Research from citation networks, keyword analysis, and co-authorship patterns highlights significant topics such as AI algorithms for waste detection, machine learning models for predictive mapping of pollution hotspots, and the application of autonomous drones and underwater robots in real-time waste management. The findings indicate a growing global focus on utilising AI to enhance environmental monitoring, optimise waste reduction methods, and support policy development for sustainable marine ecosystems. This bibliometric study provides a comprehensive analysis of the current knowledge landscape, identifies research gaps, and underscores the importance of AI as a crucial enabler for sustainable ocean waste management, offering vital insights for researchers, industry leaders, and environmental policymakers dedicated to preserving ocean health. Full article

This study presents a structured review of 59 academic articles, identified through an extensive literature survey, focused on the environmental implications of drone-based delivery systems within the broader fields of transportation, logistics, and sustainability. The reviewed journals cover a multidisciplinary range of topics, reflecting the intersection of drone technology with environmental science, logistics management, and operational research. Key journals, such as Transportation Research Part C: Emerging Technologies, Computers and Industrial Engineering, and Applied Mathematical Modelling, offer critical insights into how drone technology can reshape logistics systems, reduce environmental impacts, and contribute to intelligent transportation solutions. In addition, niche publications in areas like artificial intelligence, disaster risk reduction, and sustainable transportation further enhance the breadth of this review. By identifying and categorizing these publications, this review provides a valuable resource for researchers and practitioners aiming to explore the environmental and operational challenges of drone-based delivery systems, while also offering a foundation for future research on their sustainability and integration into existing logistics frameworks. Full article

Magnetic and electromagnetic techniques have a long history of application in mineral exploration to detect deposits and their surroundings. Their implementation over the last fifteen years has been affected by strong variations in the mining market in parallel with important technological developments. During this period, both methods were the subject of numerous documented case studies all over the globe, which is a sign of popularity and longevity of these techniques. Through a review of case histories from the main geophysical journals, we analyze the principal usage of these methods when applied to mineral exploration, while the majority of documented cases originate from North America, Asia, and Australia. There are more case studies describing the use of the magnetic method and we attribute this popularity to direct and indirect use of this method for mineral exploration. In particular, there is an increasing number of magnetic surveys conducted with drones. Combining magnetic and electromagnetic techniques is also common. The number of magnetic and EM technique case histories range by descending order from gold, porphyry copper, polymetallic, massive sulfides, uranium, Ni-Cu-PGE, iron ore, kimberlite, and iron-oxide copper-gold, with a number of single continent-specific applications. Full article

Unmanned aerial systems (UAS), commonly known as drones, have gained widespread use due to their affordability and versatility across various domains, including military, commercial, and recreational sectors. Applications such as remote sensing, aerial imaging, agriculture, firefighting, search and rescue, infrastructure inspection, and public safety have extensively adopted this technology. However, environmental impacts, particularly noise, have raised concerns among the public and local communities. Unlike traditional crewed aircraft, drones typically operate in low-altitude airspace (below 400 feet or 122 m), making their noise impact more significant when they are closer to houses, people, and livestock. Numerous studies have explored methods for monitoring, assessing, and predicting the noise footprint of drones. This study employs a bibliometric analysis of relevant scholarly works in the Web of Science Core Collection, published from 2015 to 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) data collection and screening procedures. The International Journal of Environmental Research and Public Health, Aerospace Science and Technology, and the Journal of the Acoustical Society of America are the top three preferred outlets for publications in this area. This review unveils trends, topics, key authors and institutions, and national contributions in the field through co-authorship analysis, co-citation analysis, and other statistical methods. By addressing the identified challenges, leveraging emerging technologies, and fostering collaborations, the field can move towards more effective noise abatement strategies, ultimately contributing to the broader acceptance and sustainable integration of UASs into various aspects of society. Full article

The impacts of climate change (CC) are intensifying and becoming more widespread. Greenhouse gas emissions (GHGs) significantly contribute to CC and are primarily generated by transportation—a dominant segment of supply chains. City logistics is responsible for a significant portion of GHGs, as conventional vehicles are the primary mode of transportation in logistical operations. Nonetheless, city logistics is vital for urban areas’ economy and quality of life. Therefore, decarbonizing city logistics (DCL) is crucial to promote green cities and sustainable urban living and mitigate the impacts of CC. However, sustainability encompasses the environment, economy, society, and culture, collectively called the quadruple bottom line (QBL) pillars of sustainability. This research uses the QBL approach to review the extant literature on DCL. We searched for articles on SCOPUS, focusing on analytical scholarly studies published in the past two decades. By analyzing publication years, journals, countries, and keyword occurrences, we present an overview of the current state of DCL research. Additionally, we examine the methods and proposals outlined in the reviewed articles, along with the QBL aspects they address. Finally, we discuss the evolution of DCL research and provide directions for future research. The results indicate that optimization is the predominant solution approach among the analytical papers in the DCL literature. Our analysis reveals a lack of consideration for the cultural aspect of QBL, which is essential for the applicability of any proposed solution. We also note the integration of innovative solutions, such as crowdsourcing, electric and hydrogen vehicles, and drones in city logistics, indicating a promising research area that can contribute to developing sustainable cities and mitigating CC. Full article

Since the publication of the seminal work People and Pixels: Linking Remote Sensing and the Social Sciences, the call to “socialize the pixel” and “pixelize the social” has gone largely unheeded from a truly participatory research context. Instead, participatory remote sensing has primarily involved ground truthing to verify remote sensing observations and/or participatory mapping methods to complement remotely sensed data products. However, the recent proliferation of relatively low-cost, ready-to-fly small unoccupied aerial systems (sUAS), colloquially known as drones, may be changing this trajectory. sUAS may provide a means for community participation in all aspects of the photogrammetric/remote sensing process, from mission planning and data acquisition to data processing and analysis. We present an overview of the present state of so-called participatory sUAS through a comprehensive literature review of recent English-language journal articles. This is followed by an overview of our own experiences with the use of sUAS in a multi-year participatory research project in an agroecological system encompassing a tri-county/tri-state region in the Southern Great Plains, USA. We conclude with a discussion of opportunities and challenges associated with our experience. Full article

The environmental physiognomy of an area can significantly diminish its aesthetic appeal, rendering it susceptible to visual pollution, the unbeaten scourge of modern urbanization. In this study, we propose using a deep learning network and a robotic vision system integrated with Google Street View to identify streets and textile-based visual pollution in Dhaka, the megacity of Bangladesh. The issue of visual pollution extends to the global apparel and textile industry, as well as to various common urban elements such as billboards, bricks, construction materials, street litter, communication towers, and entangled electric wires. Our data collection encompasses a wide array of visual pollution elements, including images of towers, cables, construction materials, street litter, cloth dumps, dyeing materials, and bricks. We employ two open-source tools to prepare and label our dataset: LabelImg and Roboflow. We develop multiple neural network models to swiftly and accurately identify and classify visual pollutants in this work, including Faster SegFormer, YOLOv5, YOLOv7, and EfficientDet. The tuna swarm optimization technique has been used to select the applied models’ final layers and corresponding hyperparameters. In terms of hardware, our proposed system comprises a Xiaomi-CMSXJ22A web camera, a 3.5-inch touchscreen display, and a Raspberry Pi 4B microcontroller. Subsequently, we program the microcontroller with the YOLOv5 model. Rigorous testing and trials are conducted on these deep learning models to evaluate their performance against various metrics, including accuracy, recall, regularization and classification losses, mAP, precision, and more. The proposed system for detecting and categorizing visual pollution within the textile industry and urban environments has achieved notable results. Notably, the YOLOv5 and YOLOv7 models achieved 98% and 92% detection accuracies, respectively. Finally, the YOLOv5 technique has been deployed into the Raspberry Pi edge device for instantaneous visual pollution detection. The proposed visual pollutants detection device can be easily mounted on various platforms (like vehicles or drones) and deployed in different urban environments for on-site, real-time monitoring. This mobility is crucial for comprehensive street-level data collection, potentially engaging local communities, schools, and universities in understanding and participating in environmental monitoring efforts. The comprehensive dataset on visual pollution will be published in the journal following the acceptance of our manuscript. Full article

The rapid development of modern logistics and e-commerce highlights the importance of exploring various modes of transportation in the last-mile delivery (LMD) process. However, no comprehensive studies exist in the literature exploring all modes of LMD transportation, the changes in these transportation modes, and the commonalities between them. In this study, we address this gap by conducting a systematic review of 150 academic journal articles utilizing a combination of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) content analysis and text mining analysis. Nine primary transportation methods (parcel lockers, autonomous drones, trucks, bicycles, crowd logistics, electric vehicles, tricycles, autonomous robots, and autonomous vehicles) are identified in this research. Additionally, we provide an analysis of the historical changes in these transportation modes in LMD. Using a bottom-up induction method, we identify the three major clusters of scholarly focus in the LMD literature: emphasis on value co-creation between consumers and logistics providers, practical delivery performance (path optimization or algorithms), and environmental friendliness. Further, we analyze the main themes under each cluster, leading to the identification of opportunities, challenges, and future research agendas. Our findings have implications for scholars, policymakers, and other stakeholders involved in LMD transportation modes. Full article

This paper presents an examination of the primary applications of solar energy as the main power source in the maritime sector, focusing on recent developments. A comprehensive review of the existing literature, including journal articles, proceedings, and patents, is conducted to identify three prominent areas for advancing solar energy-powered boats: maritime drones, sporting boats, and short-range touristic vessels. Maritime drones primarily serve as small autonomous boats for research, conservation, or military operations. On the other hand, sporting boats include nautical and energy design competitions involving students and enthusiasts. In terms of commercial interest, there is a growing demand for environmentally friendly and low-noise boats suitable for tourist activities, particularly in protected areas. Furthermore, specific and illustrative cases are explored in a dedicated section. Lastly, potential future perspectives are discussed and elucidated. Full article

Floods are one of the most often occurring and damaging natural hazards. They impact the society on a massive scale and result in significant damages. To reduce the impact of floods, society needs to keep benefiting from the latest technological innovations. Drones equipped with sensors and latest algorithms (e.g., computer vision and deep learning) have emerged as a potential platform which may be useful for flood monitoring, mapping and detection activities in a more efficient way than current practice. To better understand the scope and recent trends in the domain of drones for flood management, we performed a detailed bibliometric analysis. The intent of performing the bibliometric analysis waws to highlight the important research trends, co-occurrence relationships and patterns to inform the new researchers in this domain. The bibliometric analysis was performed in terms of performance analysis (i.e., publication statistics, citations statistics, top publishing countries, top publishing journals, top publishing institutions, top publishers and top Web of Science (WoS) categories) and science mapping (i.e., citations by country, citations by journals, keyword co-occurrences, co-authorship, co-citations and bibliographic coupling) for a total of 569 records extracted from WoS for the duration 2000–2022. The VOSviewer open source tool has been used for generating the bibliographic network maps. Subjective discussions of the results explain the obtained trends from the bibliometric analysis. In the end, a detailed review of top 28 most recent publications was performed and subjected to process-driven analysis in the context of flood management. The potential active areas of research were also identified for future research in regard to the use of drones for flood monitoring, mapping and detection activities. Full article

In the recent digital age, information and communication technologies are rapidly contributing to remodel the media and journalism. Numerous technologies can be utilized by the media industry to capture news or events, taking footage and pictures of a breaking news. Technology and the media are interwoven, and neither can be detached from contemporary society in most nations. Unsurprisingly, technology has affected how and where information is shared. Nowadays, it is impractical to discuss media and the methods in which societies communicate without addressing the rapidity of technology change. Thus, the aerial journalism term has emerged, which refers to the ability of creating and conveying media content in a timely and efficient fashion. This work aims to integrate a drone with AI to empower aerial journalism via training a neural network to obtain an accurate channel using the NN-RBFN approach. The proposed work can enhance aerial media missions including investigative reporting (e.g., humanitarian crises), footage of news events (e.g., man-made and/or natural disasters), and livestreams for short-term, large-scale events (e.g., Olympic Games). In our digital media era, such a smart journalism approach would help to become far more sustainable and an eco-efficient process. Both MATLAB and 3D Remcom Wireless Insite tools have been used to carry out the simulation work. Simulated results indicate that the proposed NN-RBFN managed to obtain an accurate channel propagation model in a 3D scenario with a high accuracy rate reaching 99%. The proposed framework also could offer various media and journalism services (e.g., high data rate, wider coverage footprint) in timely and cost-effective manners in both normal scenarios or even in hard-to-reach zones and/or short-term, large-scale events. Full article

Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning. Full article